Putting Fear of Crime on the Map: Investigating Perceptions of Crime Using Geographic Information Systems

Springer Series on Evidence-Based Crime Policy

Series Editors: Lawrence W. Sherman, University of Pennsylvania Heather Strang, Australian National University Crime prevention and criminal justice policies are domains of great and growing importance around the world. Despite the rigorous research done in this field, policy decisions are often based more on ideology or speculation than on science. One reason for this may be a lack of comprehensive presentations of the key research affecting policy deliberations. While scientific studies of crime prevention and criminal policy have become more numerous in recent years, they remain widely scattered across a wide range of journals and countries. The Springer Series on Evidence-Based Crime Policy aims to pull this evidence together while presenting new research results. This combination in each book should provide, between two covers (or in electronic searches), the best evidence on each topic of crime policy. The series will publish primary research on crime policies and criminal justice practices, raising critical questions or providing guidance to policy change. The series will try to make it easier for research findings to become key components in decisions about crime and justice policy. The editors welcome proposals for both monographs and edited volumes. There will be a special emphasis on studies using rigorous methods (especially field experiments) to assess crime prevention interventions in areas such as policing, corrections, juvenile justice and crime prevention. Published in Cooperation with the Campbell Crime and Justice Group

For further volumes: http://www.springer.com/series/8396

Bruce J. Doran · Melissa B. Burgess

Putting Fear of Crime on the Map Investigating Perceptions of Crime Using Geographic Information Systems

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Bruce J. Doran Fenner School of Environment & Society The Australian National University Canberra, ACT 2601, Australia [email protected]

Melissa B. Burgess Fenner School of Environment & Society The Australian National University Canberra, ACT 2601, Australia

ISBN 978-1-4419-5646-0 e-ISBN 978-1-4419-5647-7 DOI 10.1007/978-1-4419-5647-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011934029 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Dedication

We would like to dedicate this book to the many survey participants who gave willingly their time and experience – without their contribution, the research in Wollongong and Kings Cross would not have been possible. The two-stage interview process used in the Wollongong study provided a means for informal discussions in addition to the survey itself. Very often people chose to share their thoughts on policing or crime in the area and to describe personal experiences, or those of work colleagues, friends or family. The following accounts are the stories of some of the participants and in many ways these personal reflections provide powerful insights into the impact of fear of crime at an individual level.

A Real Estate Agent Leaves At the time of the survey, Amelia1 worked in the Piccadilly area of Wollongong. She was a community-minded person who took great pride in the fact that she had raised a number of adopted children and was a key person in the local business community. She worked for a real estate agency and was based in the Piccadilly shopping mall, the key feature of the precinct and a focus of crime, disorder and fear in the CBD area. The mall, despite being next to the main railway station that commuters used to access the CBD, was poorly utilized. The area had long proven to be a serious challenge for the police, the Wollongong City Council (WCC) and business residents of the local community. Amelia firmly believed that her job provided her with the potential to make positive changes in the area. As a senior real estate agent who primarily dealt in commercial property, she was able to encourage buyers who she felt were likely to have a beneficial presence in the area. An example of this was the ongoing negotiations she was handling with a university who were considering the purchase of a motel above the mall. It was well known that the motel functioned as an informal brothel and centre for drug dealing. Amelia felt that a university-run research 1 The names of respondents have been changed to protect privacy, but the content of their stories have not been altered.

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facility would dramatically change the dynamics. One afternoon while locking up the shopfront for the agency, Amelia was approached from behind and doused with petrol. She was then confronted by a drug addict she knew well – someone she was not normally bothered by but who, on this occasion, was high and did not seem to recognize her. Amelia frantically pleaded with the addict as he waved a lighter and threatened to ignite her. After several terrifying moments, her attacker seemed to lose interest and walked away. Amelia was someone conditioned to minor disorder. She knew by name many of the addicts, including her attacker, who used the methadone clinics. She was also understanding of the weekend alcohol-related problems, as well as the youths involved in tagging graffiti. However, the very direct and personal attack she experienced outside her shopfront that afternoon was too much for her. The attack took place between the first and second stages of the interview. Several weeks later, she had moved to another area and a community-minded person, who genuinely believed Piccadilly could change for the better, had left the area for good.

A Cobbler Who Wouldn’t Eat Outside Tony was a huge man standing well over 6.5 feet tall. He ran a small shoe repair shop that opened directly out onto the Crown Street Mall. The area was not in the core of crime hotspot for the CBD but was a focus of social disorder on the weekends. Tony told me that he had recently retired at the age of 35 from a specialist military unit in the Australia Defence Force and had located in Wollongong for family reasons. He had described how he had been a victim of several serious crimes in the 12 months preceding the survey. One crime was particularly fear inspiring – a group of youths had attacked him with an iron bar while he was getting from his shop to his car at the back of the building after work. However, when asked about the incident, Tony explained that this did not bother him because of his self-defence training and that he was easily able to disarm the attackers. It was all the more striking then, to hear him talk about how he would never eat his lunch or take breaks in the mall area directly outside his shop. His fear in this case related to the fact that, in his judgement, there was a chance of being attacked with a syringe and this was not a risk he was prepared to take. He explained that his first priority was his family and that if he was a victim of a syringe attack, he may no longer be able to act as a provider. It is hard to imagine a more capable guardian than Tony, yet his avoidance behaviour meant that he was effectively removed from the mall area only metres away from his shopfront.

A Night on the Town Goes Wrong John ran a small shop below the Crown Street Mall that sold specialist figurines for dungeons and dragons-type board games. He was a soft spoken small man who was 20 years old. He maintained a calm demeanour during the interview but passionately

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related a story at the conclusion of the exercise. He undid the top three buttons of his shirt and revealed some massive scarring around the base of his neck. I could see that he had grown a beard to hide some of them. John went on to explain that two years back, he had been walking through Crown Street Mall late at night one weekend. The mall often serves as a conduit between two night club strips at either end of the vehicle-free area. He had left friends and was going to ‘kick on’ at some of the clubs alone on Keira Street. He found himself suddenly surrounded by three men with skateboards – without warning or provocation; they picked up the boards and attacked him. He was seriously injured but able to walk after the incident and attempted to get help from passers-by. When this proved to be unsuccessful, he attempted to catch a taxi from Crown Street to the hospital, only a kilometre away but up a steep incline. When no taxis would stop he was forced to walk to the hospital. After buttoning up his shirt, he stated strongly that he was determined not to let the experience ‘beat him’. Many months later, I was conducting a social disorder assessment in the mall at 4 am and was aghast to see John walking determinedly, and alone, through the paved walkway area. Here appeared a classic manifestation of the risk-victimization paradox – a young man who was relatively more likely to become a victim of crime displaying an apparently irrational lack of fear. I had the strong impression though that John was carrying something to protect himself.

A Husband Threatens to Take the Law into His Own Hands Probably one of the more horrific accounts related to me while conducting interviews was the experience of Michelle, a petite mid-30s dress-shop owner, who worked at the bottom end of Crown Street Mall. I could see that she was nervous as the survey moved through a section on victimization over the past 12 months. At the end of the interview, her husband came from the back of the shop to join the conversation. They were both very keen to know what the survey data would be used for – would it be used to police antisocial behaviour in the mall? Who would have access to the results? Was the study simply an academic exercise? It emerged that their concern stemmed from a serious attack that had taken place a number of months prior to the interview. Michelle had been accosted in her shop by a much larger woman who demanded cash from the register. When Michelle refused, the woman became violent and threw her against a display. Her attacker then went into a frenzy, kicking and punching her repeatedly, as well as bodily picking her up and throwing her around the shop, as the smaller woman desperately tried to fend off the blows. The assault continued for some minutes before the offender left the shop. Michelle was badly shaken and had sustained serious facial injuries that required surgery. She pressed charges, as the offender was generally known in the CBD. However, a week later, Michelle’s attacker was back in her shop to threaten her again. Michelle and her husband spoke of their frustration with the authorities – in their opinion, the system had completely failed them and had left them both feeling vulnerable. Michelle no longer felt secure in the shop by herself, so her husband, who was self-employed, had moved to the rear of the shop and established an office. He emphasized strongly

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that he would not tolerate any future intimidation and that if it were to happen again, he would take the law into his own hands. The responses to the survey varied considerably. Some respondents told of how they carried their car keys when leaving work so that sharp ends protruded from between their fingers – if they needed to defend themselves they were ready. Other people spoke on their mobile phones when walking in public to avoid being addressed by strangers. Many people drew very detailed cognitive maps which outlined the areas they avoided because they were afraid of being robbed, beaten or attacked. In some cases, people were prisoners not in their homes but in their workplace, as is the case in the example below.

Some of the cognitive maps drawn by a survey respondent in Wollongong outlining areas they avoided around their workplace (the hollow arrow indicates the location of their workplace)

The point of these stories is not to overemphasize the shocking nature of some of the experiences but rather to acknowledge the individual stories and behavioural responses that are somewhat masked by, and lay behind, the collective spatial analyses presented in the Kings Cross and Wollongong studies. These accounts also serve to reinforce the fundamental assumption behind this book, namely that fear of crime is a significant problem for society because it prompts people to adopt protective and

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avoidance behaviours. These behaviours have many consequences at the level of the individual and community. They are complex and can be hard to understand but they also provide a lens through which to examine interactions between members of a community, public space and relationships with crime and disorder. We use techniques from behavioural geography in conjunction with Geographic Information Systems, to develop an approach which we hope will contribute to the literature on fear of crime as well as the management of the problem. The approach is relatively simple, but strongly grounded in well-established principles of cognitive mapping. It is transferable to other contexts and situations – in the final chapter we outline many possible future applications and avenues for research. We would again like to thank the people who participated in the Wollongong and Kings Cross studies as it is their contribution that allows us to ‘put fear on the map’.

Series Foreword

All over the world, politicians and policy makers are increasingly inclined to claim that their proposals are ‘evidence-based’. Social scientists have even caught this spirit of evidence, which may show in their occasional use of the malapropism of ‘evidence-based research’ (thus implying the existence of some other legitimate category of research that is not based on evidence, perhaps including what Peter Reuter and others describe as ‘mythical numbers’i ). Even when policies can clearly cite a relevant body of research, however, scientists cannot agree on what makes a policy ‘evidence-based’.ii The present book series must therefore grapple with a series of challenges to its very name, let alone the ordinary hurdles of good research. One challenge is about the scope of evidence that is embraced by the concept of ‘evidence-based’ anything. In forensic evidence, courts usually offer a very broad invitation to facts and measures in support of a hypothesis that bears on the case. In the United States they even allow theories of causation to be presented to juries, a practice widely attacked as ‘junk science’ until the US Supreme Court barred the use of theories that had not been tested, at least in the federal courts (Daubert v. Merell Dow, 1993). While many court decisions may still turn on theories that most scientists would dismiss as not adequately evidence-based, the standard at least requires some evidence. A far narrower scope for what is ‘evidence-based’ has been implied by those who focus on ‘what works’, or the impact of programmes on outcomes.iii Readers might expect a series on evidence-based crime prevention to use that boundary. They will, perhaps, be pleasantly surprised that we do not. As any definition of good medical practice holds, an accurate diagnosis is a prerequisite to choosing an appropriate treatment. Similarly, it is just as important to know ‘what is’ as to know ‘what works’. Tools and evidence for classifying crimes and criminals, for analysing trends and patterns in criminal events, understanding how crimes are committed and may therefore be prevented – all these are essential forms of evidence for the broader enterprise of crime prevention. Even research that focuses on interventions is usually accompanied by descriptive and diagnostic data on the nature of the crime issue in question. An entire series of books can certainly afford to do the same. A further challenge is how rigorous a series should be in defining adequate evidence of cause and effect, or even descriptive estimates of crime patterns. Our aim

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is to publish the most rigorous evidence available on important crime problems. If, for example there are no randomized controlled trials on gun crime prevention, then the best possible quasi-experiments are a welcome addition to the policy debate. Despite the editors’ strong associations with experimental criminology, we do not insist that randomized trials are the only worthwhile source of evidence for policy. As Sherman has defined evidence-based policing,iv the best definition of rigor is that the evidence simply be ‘scientific’, with all the systematic care and precision required by the scientific method. The aim of this series is to help foster evidence-based crime prevention with a broader range of materials, and a more flexible medium, than is presently available. We invite readers to examine the series as a more rigorous, complete and independent source of evidence than may be available from government reports or programme delivery organizations. We invite submissions from authors who want their readers to have all the evidence produced by a particular project, and who have much more evidence to report than can fit in any one journal article. We invite subscriptions from libraries that require the most complete evidence available on crime and justice issues costing hundreds of billions of dollars for governments to address world-wide. We are grateful to both Springer and the Campbell Collaboration Crime and Justice Steering Group for their support in developing this series. And while the dedication of each book is the privilege of the authors, we would like to dedicate the series to the steadfast support of Jerry Lee, the greatest champion of evidence-based policy we know. Washington, DC April, 2011

Heather Strang Lawrence W. Sherman

Notes i. Reuter, P. (1987). “The (continued) vitality of mythical numbers”. Public Interest 75: 79–95. ii. The most elaborate attempt to do so can be found in a 2009 report of the National Research Council and Institute of Medicine, Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. Committee on Prevention of Mental Disorders and Substance Abuse Among Children, Youth and Young Adults: Research Advances and Promising Interventions. Mary Ellen O’Connell, Thomas Boat, and Kenneth E. Warner, Editors. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. iii. Sherman, L. W., D. P. Farrington, B. C. Welsh and D. L. Mackenzie (eds) (2002), EvidenceBased Crime Prevention. London, Routledge. iv. Sherman, L. W. Evidence Based Policing. Washington, DC, Police Foundation http://www. policefoundation.org/pdf/Sherman.pdf.

Acknowledgements

There are many people we would like to thank who have helped in producing this book. First, we owe a great deal to Professor Brian Lees from the University of New South Wales at the Australian Defence Force Academy, who was the principal PhD supervisor for both the Wollongong and Kings Cross projects. In general we are both grateful for the opportunities that have opened up through undertaking the research and for Brian’s guidance and feedback throughout. We are just two of many students who have benefited from his experience in GIS-based research and his ability to find topics that deal with relevant and interesting issues. It was his vision that identified a need for spatially explicit research into fear of crime. We would also like to extend our gratitude to Professor Peter Grabosky, who suggested that the research conducted in Wollongong and Kings Cross would make a valuable contribution to this series. Peter’s generosity, encouragement and willingness to promote our work have been of significant value. There are a number of specific acknowledgements to make regarding the Wollongong study, presented in Chapter 6 Dr Ron Horvarth provided important advice and introductions to personnel within the NSW Police Service during the initial stages of the project. Dr Chris Devery, NSW Police Force, provided feedback at various stages of the project and guidance on protocols for working alongside the NSW Police Service. Dr Jerry Ratcliffe shared his considerable expertise on crime mapping and policing issues and gave valuable comments on PhD thesis chapters and papers related to the study. Various members of the NSW Police Service, Wollongong Local Area Command, gave specific advice relating to the study site, helping to gain access to crime data for the region. A number of officers also attended seminars at the Wollongong City Council where they gave feedback on early results from the project. Bronwyn Richards, Sand Hall, Rada Jordan and Greg Doyle from the Wollongong City Council were all very supportive of the project from the fieldwork stage onwards. We are very grateful for the ideas they shared and for the opportunities they created for me to discuss and implement my research through workshops and seminars. I am also in their debt for guiding me to a number of secondary sources that were relevant to my project. Towards the end of the project, the Australian Institute of Criminology and the Local Government Association provided funding to travel to Brisbane and present a paper at a conference looking at graffiti and disorder. This was valuable for many reasons – most xiii

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notably initial for the discussions held with NSW Police Superintendent Dave Darcy of the Kings Cross Local Area Command that ultimately led to the Kings Cross Study, presented in Chapter 7. Dave provided much insight regarding the implementation of the project. His early endorsement and continuing interest in fear-of-crime research is also valued. Inspector Gary Groves, NSW Police Force, was also instrumental during the interviewing stage of the project. Gary provided the materials necessary for interviewing, helped with interviewer training, a temporary office and assisted in distributing information fliers. All NSW Police officers stationed in Kings Cross and Woolloomooloo during 2004 are acknowledged for accommodating the research during this period. I thank Chris Devery, NSW Police Force, for liaising between the NSW Police Legal Services and the ANU regarding the exchange of crime data. Associate Professor Julie Stubbs, University of Sydney, also provided thorough and constructive feedback on my thesis chapter drafts. Julie gave particularly useful advice on the interviewing procedure and also liaised with her 2004 Masters of Criminology students to conduct the interviewing for this study. These students and Volunteers in Policing (VIP) are acknowledged for their time, professionalism and assistance in conducting the interviews. In particular, VIPs Warwick and Jim are acknowledged for their outstanding participation. Their assistance was central to the acquisition of the large dataset used in the research. Helen Steptoe, VIP, also provided immense support during the data entry phase of the project. Emeritus Professor Diana Howlett is acknowledged for funding the Howlett Honours Prize for Geography. Melissa was awarded this prize in 2004 and used the financial gift to purchase numerous fear-of-crime books that could not be sourced in Australian libraries. I specifically thank Douglas Grand, General Manager of the Kings Cross Licensing Accord, for his donation in 2004 to help with costs associated with the interviewing stage of the project. As with many research projects, special thanks should go to staff from the research department – the School of Resources, Environment and Society, now the Fenner School of Environment and Society at the Australian National University. Professor Peter Kanowski was head of department at the time and was always encouraging and willing to support the projects with conference and fieldwork funding. Karl Nissen and Steve Leahy have provided help with computer-related issues over many years. To the various members of the tea club over the years – Shawn Laffan, Kimberly Van Neil, Brian Lees, Clive Hilliker, Steve Leahy, Karl Nissen, Eugene Wallensky, Paul Carlile, Sanjeev Shrivastava, Sunil Sharma, Sandy Gilmore, Piers Bairstow and many others.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . The Emergence of Fear of Crime As an Area of Research The Paradoxical Nature of the Fear of Crime . . . . . . . Current Trends in Fear of Crime Research . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Why Is Fear of Crime a Serious Social Problem? . . . . . . . . . Individual Reactions . . . . . . . . . . . . . . . . . . . . . . . . . Hypothesized Links Between the Fear of Crime, Disorder and Crime Disorder and Decline Hypothesis . . . . . . . . . . . . . . . . . . . Economic Impact of Behavioural Responses to Fear of Crime . . . . Chapter Review: Potential Problems Not to Be Ignored and a Need for Spatially Explicit Research . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 What Causes Fear of Crime? . . . . . . . . . . . . . . . . . Criminal Opportunity and Risk of Victimization Theories . . . Demographic Theories Explaining Fear of Crime . . . . . . . Victimization Hypothesis . . . . . . . . . . . . . . . . . . . Indirect Victimization Hypothesis . . . . . . . . . . . . . . Vulnerabilities Hypothesis . . . . . . . . . . . . . . . . . . Review: An Abundance of Contested Demographic Studies . . Social Theories Explaining Fear of Crime . . . . . . . . . . . Risk Society Hypothesis . . . . . . . . . . . . . . . . . . . Social Disorganization Hypothesis . . . . . . . . . . . . . . Review: Social Studies Emphasize the Inherent Complexity of ‘Fear’ of ‘Crime’ . . . . . . . . . . . . . . . . . . . . . . . . Environmental Theories Explaining Fear of Crime . . . . . . . The Disorder/Incivilities Hypothesis . . . . . . . . . . . . . Threatening and Safe Environments Theories . . . . . . . . Signal Crimes Perspective . . . . . . . . . . . . . . . . . . Review: Intuitive Environmental Studies into Cues Triggering Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter Review: An Opening for Pertinent Environmental Studies . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Managing Fear of Crime . . . . . . . . . . . . . . . . . . . . . Policing Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . Case Study: The New York Police Department’s (NYPD) Policing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental Design and Fear of Crime . . . . . . . . . . . . . Chapter Review: Police, Community and Government Cooperation References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Investigating Fear of Crime . . . . . . . . . . . . . . . . . . . . . Defining Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . . Fear Is an Emotion, Not Cognition . . . . . . . . . . . . . . . . . Fear in Relation to Other Emotional Reactions and Stimuli that Trigger Fear . . . . . . . . . . . . . . . . . . . . . . . . . . Crime Involves a Violation of Criminal Law . . . . . . . . . . . . Types of Fear of Crime: Personal and Altruistic Points of View . . Review: Key Issues to Consider When Defining Fear of Crime . . . Measuring Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . Problems with Cognitive Approaches to Measuring Fear of Crime Improvements Through Affective Approaches to Measuring Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behavioural Approaches to Measuring Fear of Crime . . . . . . . Review: A Preference for Avoidance-Based Measures in Fear-of-Crime Studies . . . . . . . . . . . . . . . . . . . . . . . Analysing Fear-of-Crime Data . . . . . . . . . . . . . . . . . . . . Advantages of Spatial Analyses of Fear of Crime . . . . . . . . . Spatial Cognition and Cognitive Mapping . . . . . . . . . . . . . The Beginning of Fear Mapping . . . . . . . . . . . . . . . . . . Activity Diaries and Daily Routines . . . . . . . . . . . . . . . . Geographic Information Systems and Fear of Crime . . . . . . . Chapter Review: A New Direction with Avoidance Mapping . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 The Wollongong Study . . . . . . . . . . . . . . . . . . . The Goals of the Wollongong Study . . . . . . . . . . . . . Research Setting . . . . . . . . . . . . . . . . . . . . . . . . Logic Behind Study Site Selection . . . . . . . . . . . . . The Central Business District of Wollongong . . . . . . . Crime and Fear of Crime in Wollongong . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion of Spatial Outputs . . . . . . . . . . . . . . . Integrating the Key Spatiotemporal Findings with Police and Community Initiatives in Wollongong: The Degree of Institutional Involvement . . . . . . . . . . . . . . . . . .

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Assessments of Techniques and Approaches Developed in Wollongong Study . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 The Kings Cross Study . . . . . . . . . . . . . . . . . . . . . . . Background to the Kings Cross Study . . . . . . . . . . . . . . . . Goals of the Kings Cross Study . . . . . . . . . . . . . . . . . . . . Research Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographic Location . . . . . . . . . . . . . . . . . . . . . . . . Historical Background . . . . . . . . . . . . . . . . . . . . . . . Demographic Characteristics . . . . . . . . . . . . . . . . . . . . Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fear of Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interviewing Approach . . . . . . . . . . . . . . . . . . . . . . . Survey Design and Questions . . . . . . . . . . . . . . . . . . . Spatial Data Visualization . . . . . . . . . . . . . . . . . . . . . Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . Sample Characteristics . . . . . . . . . . . . . . . . . . . . . . . People Are Afraid of Crime in Kings Cross . . . . . . . . . . . . People Avoid Specific Areas of Kings Cross Due to Fear of Crime Exploring the Underlying Reasons for Fear of Crime . . . . . . . Integrating the Fear Mapping Results with Policy and Community Crime and Fear-of-Crime Prevention . . . . . . . . Addressing Crime . . . . . . . . . . . . . . . . . . . . . . . . . Targeting Pertinent Signs of Disorder and Incivility . . . . . . . . Assessments of Techniques and Approaches Developed in the Kings Cross Study . . . . . . . . . . . . . . . . . . . . . . . . . General Summary of the Kings Cross Study . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Future Avenues for Fear Mapping: Potential Applications and Improvements . . . . . . . . . . . . . . . . . . . . . . . Has Collective Avoidance Behaviour Changed in Wollongong and Kings Cross? . . . . . . . . . . . . . . . . . . . . . . . . Investigating Behavioural Responses in Relation to Different Types of Crime . . . . . . . . . . . . . . . . . . . . . . . . . Further Avenues for Investigating Links Between Fear, Crime and Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . Broken Windows Theory in the Transit Context . . . . . . . . Fear Mapping and Advances in Spatial Technology . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Introduction

The Emergence of Fear of Crime As an Area of Research The fear of crime first began to emerge as an issue of concern in the mid-1960s when national public opinion polls in the United States began to incorporate open-ended questions relating to the public perception of crime (Furstenberg, 1971; McIntyre, 1967; Poveda, 1972). Furstenberg (1971) notes that it is difficult to pinpoint exactly when the issue began to gain momentum but broadly links this to a general concern about crime, and racial and economic conflict in the 10 years prior to the 1970s. Before this, crime had only been given slight attention in public opinion polls (McIntyre, 1967), with the surveys conducted in 1966 by the President’s Commission on Crime providing virtually the only source of information on the public reaction to crime (Furstenberg, 1971). The findings from these surveys were published in a large volume entitled “The Challenge of Crime in a Free Society”, which involved the work of numerous commissioners, staff members of the President’s Commission on Crime and consultants from every part of America (PCLEAJ, 1967). The report was forthright in arguing that the fear of crime was eroding the basic quality of life of many Americans. Studies in two high-crime areas showed that fear of crime was causing 43% of respondents to stay off the streets at night, 35% to not speak to neighbours and 21% to use cars or cabs at night. In addition, 20% of respondents said they would like to move to another neighbourhood because of their fear of crime. The findings from the national survey were generally found to support the results from these local studies with one-third of a representative sample of Americans stating they felt unsafe to walk alone in their neighbourhoods at night. One-third of respondents also said they kept firearms or watchdogs for protection against criminals. The report also found that fear of crime varied according to race, income, sex and experience of victimization. Women, people of non-Caucasian origin and of lower income levels were found to have the highest average scores of fear. The report emphasized that a number of the findings were less intuitive than would be imagined. Fear of crime was found to be less closely associated with having been a victim of crime than might have been supposed. On a broader level, fear of crime was not always highest in areas that had high rates of crime, according to official

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crime data, or victimization surveys. People were also found to be most fearful of the types of crimes that occurred least frequently. The general conclusions of the report were alarming. The commission felt that it could not state that the public’s fear of crime was exaggerated and concluded that people’s fears must be respected. Further, fear of crime was seen as a complex response, not simply a fear of death or injury but, at bottom, a fear of strangers. This was seen as one of the most dangerous aspects of the fear of crime as it damaged social order and, by reducing the level of sociability and mutual trust, could indeed make streets and public places more dangerous. The results from the study provided the impetus for further investigation (e.g. Conklin, 1971; Furstenberg, 1971; McIntyre, 1967; Poveda, 1972; Brooks, 1974). The findings from other national level surveys such as Gallup and Harris polls supported the general results from the President’s Commission on Crime (PCLEAJ, 1967) report and also showed that fear of crime had risen steadily since 1965 (Erskine, 1974; McIntyre, 1967). The midto-late 1970s saw a plethora of studies looking specifically into the fear of crime (e.g. Brooks, 1974; Clemente, 1977; Balkin, 1979; Hartnagel, 1979; Thomas and Hyman, 1977).

The Paradoxical Nature of the Fear of Crime The focus of much early research into the fear of crime centred on the degree to which fear was seen to be rational or irrational in relation to the actual occurrence of crime (e.g. Poveda, 1972; Brooks, 1974; Balkin, 1979). While fear of crime is not always negative, provoking people to protect themselves when they are threatened, it becomes problematic when out of proportion with the objective risks of victimization (Clark, 2003; Warr, 2000). Results from public opinion polls frequently showed that high levels of fear were being recorded not only in areas characterized by high rates of recorded crime, but those with low rates as well (e.g. PCLEAJ, 1967; Furstenberg, 1971). Similarly, the public was generally found to fear most the crimes that occurred least frequently (PCLEAJ, 1967; McIntyre, 1967). At the time, recorded crime rates were seen as an objective measure and the observed inconsistencies between fear of crime and victimization rates were often attributed to irrational individual perceptions (Balkin, 1979). Since then, the mismatch between the fear of and the incidence of crime has been found in numerous broad level studies set in cities in the United Kingdom, Switzerland, New Zealand and Australia (Borooah and Carcach, 1997; Box et al., 1988; Doeksen, 1997; Killias and Clerici, 2000). Even those considering high levels of unreported incidents have found fear of crime to exceed the real risk of crime (Liska et al., 1988; Painter, 1996; Taylor and Hale, 1986). This discrepancy between fear and actual risk has become known as the “paradox of fear” (e.g. Hollway and Jefferson, 1997; Warr, 1984). The paradox is most evident among women and the elderly who, despite experiencing lower rates of victimization, are consistently found to have higher rates of fear (e.g. Smith and Tortensson, 1997; Warr, 1984).

Current Trends in Fear of Crime Research

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Rather than dismissing the fear of crime as an unwarranted area of research, many researchers have seen the discrepancies between official crime data and fear of crime surveys as justification for the fear of crime to be treated as a serious social problem in its own right (e.g. Poveda, 1972; Brooks, 1974). Garofalo (1981) went so far as to suggest that discussions over the apparent irrationality of fear had become an unnecessary impediment to researchers looking into the phenomenon. These sentiments were echoed much later by Lupton and Tulloch (1999). However, one could argue that this was an extreme stance as many of the studies looking into the paradoxical nature of fear of crime have been helpful in furthering the understanding of the sources of fear of crime among particular groups of society (e.g. Clemente, 1977; Hanson et al., 2000; Smith and Tortensson, 1997; Warr, 1984). Studies such as these essentially seek to answer calls for a deeper knowledge of the determinants of fear, without which many authors have argued fear of crime would remain elusive to address (e.g. Brooks, 1974; Balkin, 1979).

Current Trends in Fear of Crime Research As a research area, the fear of crime is now one of the most researched topics in contemporary criminology (Farrall et al., 2000). It receives considerable attention in other disciplines such as social ecology (e.g. Taylor and Covington, 1993; Wilson Doenges, 2000), social psychology (e.g. Van der Wuff et al., 1989; Farrall et al., 2000) and geography (e.g. Smith, 1987; Valentine, 1989; Pain, 1991, 1997, 2000; Koskela, 1999; Koskela and Pain, 2000; Thomas and Bromley, 2000). Hale (1996) estimated that over 200 articles, monographs or books had been devoted to the fear of crime. Some 15 years later, a search using the Current Contents engine lists over 400 published journal articles of crime between 1993 and 2011 that include the term “fear of crime” in the title or abstract. Further, research into the fear of crime has increased in countries outside of the United States, most noticeably the United Kingdom (e.g. Smith, 1987; Mayhew and White, 1997; Mirrlees-Black and Allen, 1998; Pain, 1997) and Australia (e.g. Brown and Polk, 1996; NCAVAC, 1998; Grabosky, 1995; Tulloch, 2000). There have been far fewer studies of fear of crime in developing nations but this seems to be changing (e.g. Chadee and Ditton, 2003; Karakus et al., 2010; Zhang et al., 2009). The seriousness and extent of the phenomenon is often illustrated by statistics from national or international crime surveys (e.g. Koskela, 1999; Smith, 1987). The findings from such surveys continue to show that 20–30% of people indicate that they feel very unsafe or fairly unsafe while out alone after dark (e.g. Mayhew and White, 1997; Mirrlees-Black and Allen, 1998). For some sectors of society, up to 60% of people report a degree of fear in this situation (e.g. Joseph, 1997; Thomas and Bromley, 2000). When described in these terms, the fear of crime appears to be a problem of truly striking dimensions (Farrall et al., 1997, 2000) which plagues many, if not most, communities (Reid et al., 1998). Scarborough et al. (2010) notes that the consistently identified relationships between demographic characteristics

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Introduction

(e.g. age, race, gender) and fear of crime provide an “enduring frustration” for policy makers as these factors cannot be altered by government policy. Some have suggested that the fear of crime is a problem as great or greater than crime itself (Clemente, 1977; Brown and Polk, 1996; Oc and Tiesdell, 1997). Such claims are based upon the assumption that, in terms of impact upon urban living, perceptions of crime are often more important than the actuality (Oc and Tiesdell, 1997). Unlike crime, fear of crime is not restricted in its distribution in space and time, giving it the potential to be more widespread (Perkins and Taylor, 1996; Smith, 1987). In essence, unlike crime, which requires the convergence of a victim and an offender in time and space (Cohen and Felson, 1979), fear of crime only requires a victim. Further elevating fear of crime is the fact that those not directly victimized are indirectly victimized when they hear of the experiences of others (Covington and Taylor, 1991). A number of authors have noted an increased interest in the fear of crime in policy arenas over more recent years (Smith, 1987; Fishman and Mesch, 1996; Farrall et al., 1997; Keane, 1998; Farrall et al., 2000). Walklate (1998) attributes much of the interest in media and policy circles to the results from broad-scale victimization surveys which give rise to disturbing statements like the oft-quoted assertion that fear of crime causes many people to become prisoners in their own homes (e.g. Joseph, 1997). Such comments are intrinsically disturbing (Box et al., 1988) and demand that efforts be made to alleviate the fear of crime (Clemente, 1977). It is not surprising, therefore, that fear of crime has been paid close attention in political campaigns over time (e.g. Brown and Polk, 1996; Kelling and Coles, 1997). A further factor influencing the relationship between researchers and policy makers is that the motivation for many studies into fear of crime will translate into practical policies for reducing fear (Box et al., 1988). The continued research and interest in the topic reinforces the assertion that fear of crime is an intractable and resistant phenomenon (Nair et al., 1993; Tulloch et al., 1998). Hollway and Jefferson (1997) argue that despite the voluminous literature on fear of crime, it is fair to say that the area remains conceptually undeveloped and that most work remains largely descriptive. To some extent this provides support for Brooks’ (1974) suggestion that, because of its irrational qualities, fear of crime may be more difficult to combat than criminality itself. Garofalo (1981) noted that every advance that is made in the field seems to generate more questions than answers. However, the author also suggested that this should be expected, as part of the nature of complex social phenomena is that their complexity becomes more apparent the more closely they are examined. In general, it seems likely that the fear of crime will continue to remain high on the agendas of researchers and policy makers alike.

References Balkin, S. (1979). “Victimization rates, safety and fear of crime”. Social Problems 26(3): 343–358. Borooah, V. and C. Carcach (1997). “Crime and fear. Evidence from Australia”. The British Journal of Criminology 37(4): 635–657.

References

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Box, S., C. Hale, et al. (1988). “Explaining fear of crime”. The British Journal of Criminology 37(4): 340–356. Brooks, J. (1974). “The fear of crime in the United States.” Crime and Delinquency 20: 241–245. Brown, M. and K. Polk (1996). “Taking fear of crime seriously: the Tasmanian approach to community crime prevention”. Crime and Delinquency 42(3): 398–420. Chadee, D. and J. Ditton (2003). “Are older people most afraid of crime? Revisiting Ferraro and LaGrange in Trinidad”. The British Journal of Criminology 43(2): 417–433. Clark, J. (2003). “Fear in fear-of-crime”. Psychiatry, Psychology and Law 102(267–282). Clemente, F. (1977). “Fear of crime in the United States: a multivariate analysis”. Social Forces 56(2): 519. Cohen, L. E. and M. Felson (1979). “Social change and crime rate trends: a routine activity approach”. American Sociological Review 44: 588–608. Conklin, J. E. (1971). “Dimensions of community response to the crime problem”. Social Problems 18: 373–385. Covington, J. and R. B. Taylor (1991). “Fear of crime in urban residential neighbourhoods: implications between – and within – neighbourhood sources for current models”. The Sociological Quarterly 32(2): 231–249. Doeksen, H. (1997). “Reducing crime and the fear of crime by reclaiming New Zealand’s suburban street”. Landscape and Urban Planning 39(2–3): 243–252. Erskine, H. (1974). “The polls: fear of violence and crime”. Public Opinion Quarterly 38(1): 131–145. Farrall, S., J. Bannister, et al. (1997). “Questioning the measurement of the ‘fear of crime’: findings from a major methodological study”. The British Journal of Criminology 37(4): 658–679. Farrall, S., J. Bannister, et al. (2000). “Social psychology and the fear of crime”. The British Journal of Criminology 40(3): 399–413. Fishman, G. and G. S. Mesch (1996). “Fear of crime in Israel: a multidimensional approach”. Social Science Quarterly 77(1): 76–89. Furstenberg, F. F., Jr. (1971). Public reaction to crime in the streets “The American Scholar”. The fear of crime. J. Ditton and S. Farrall (Eds.). Ashgate, Aldershot: 3–12. Garofalo, J. (1981). “The fear of crime: causes and consequences”. Journal of Criminal Law and Criminology 72(2): 839. Grabosky, P. N. (1995). “Fear of crime, and fear reduction strategies”. Current Issues in Criminal Justice 7(1): 7–19. Hale, C. (1996). “Fear of crime: a review of the literature”. International Review of Victimology 4: 79–150. Hanson, R. F., D. W. Smith, D. G. Kilpatrick and J. R. Freedy (2000). “Crime-related fears and demographic diversity in Los Angeles county after the 1992 civil disturbances”. Journal of Community Psychology 28(6): 607–623. Hartnagel, T. F. (1979). “The perception and fear of crime: implications for neighbourhood cohesion, social activity and community affect”. Social Forces 58(1): 176–193. Hollway, W. and T. Jefferson (1997). “The risk society in an age of anxiety: situating fear of crime”. The British Journal of Sociology 48(2): 255. Joseph, J. (1997). “Fear of crime among black elderly”. Journal of Black Studies 27(5): 698–717. Karakus, O., E. F. McGarrell, et al. (2010). “Fear of crime among citizens of Turkey.” Journal of Criminal Justice 38(2): 174–184. Keane, C. (1998). “Evaluating the influence of fear of crime as an environmental mobility restrictor on women’s routine activities”. Environment and Behavior 30(1): 60–74. Kelling, G. L. and C. M. Coles (1997). Fixing broken windows: restoring order and reducing crime in our communities. New York, NY, Touchstone. Killias, M. and C. Clerici (2000). “Different measures of vulnerability in their relation to different dimensions of fear of crime”. The British Journal of Criminology 40(3): 437–450. Koskela, H. (1999). “Gendered exclusions: women’s fear of violence and changing relations to space”. Geografiska Annaler 81B(2): 111–124.

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Koskela, H. and R. Pain (2000). “Revisiting fear and place: women’s fear of attack and the built environment”. Geoforum 31(2): 269–280. Liska, A. E., A. Sanchirico, et al. (1988). “Fear of crime and constrained behavior specifying and estimating a reciprocal effects model”. Social Forces 66(3): 827–838. Lupton, D. and J. Tulloch (1999). “Theorizing fear of crime: beyond the rational/irrational opposition”. The British Journal of Sociology 50(3): 507–523. Mayhew, P. and P. White. (1997). “Home Office Research and Statistics Directorate Findings No 57: The 1996 international crime victimisation survey.” From http://www.homeoffice.gov.uk/ rds//pdfs/r57.pdf. McIntyre, J. (1967). “Public attitudes toward crime and law enforcement”. The Annals of the American Academy (November): 34–46. Mirrlees-Black, C. and J. Allen (1998). Concern about crime: Findings from the 1998 British Crime Survey. Research Findings No 83. London, Home Office Research, Development and Statistics Directorate. Nair, G., J. Ditton, et al. (1993). “Environmental improvements and the fear of crime: the sad case of the ‘Pond’ area in Glasgow”. The British Journal of Criminology 33(4): 555–561. National Campaign Against Violence and Crime (1998). Fear of crime – summary volume. Canberra, NCAVAC. Oc, T. and S. Tiesdell (1997). Safer city centres: reviving the public realm. London, Chapman. Pain, R. (1991). “Space, sexual violence and social control: integrating geographical and feminist analyses of women’s fear of crime.” Progress in Human Geography 15(4): 415–431. Pain, R. (2000). “Place, social relations and the fear of crime: a review.” Progress in Human Geography 24(3): 365–387. Pain, R. H. (1997). “ ‘Old age’ and ageism in urban research: the case of fear of crime”. International Journal of Urban & Regional Research 21(1): 117–128. Painter, K. (1996). “The influence of street lighting improvements on crime, fear and pedestrian street use, after dark”. Landscape and Urban Planning 35(2–3): 193–201. Perkins, D. D. and R. B. Taylor (1996). “Ecological assessments of community disorder: their relationship to fear of crime and theoretical implications”. American Journal of Community Psychology 24(1): 63–107. Poveda, T. G. (1972). “The fear of crime in a small American town”. Crime and Delinquency 18(2): 147–153. President’s Commission on Law Enforcement and Administration of Justice (1967). The Challenge of Crime in Free Society. Washington United States Government Printing Office. Reid, L., J. T. Roberts, et al. (1998). “Fear of crime and collective action: an analysis of coping strategies”. Sociological Inquiry 68(3): 312–328. Scarborough, B. K., T. Z. Like-Haislip, et al. (2010). “Assessing the relationship between individual characteristics, neighborhood context, and fear of crime”. Journal of Criminal Justice 38(4): 819–826. Smith, S. J. (1987). “Fear of crime: beyond a geography of deviance”. Progress in Human Geography 11: 1–23. Smith, W. R. and M. Tortensson (1997). “Gender differences in risk perception and neutralising fear of crime”. The British Journal of Criminology 37(4): 603–634. Taylor, R. B. and J. Covington (1993). “Community structural change and fear of crime”. Social Problems 40(3): 374–395. Taylor, R. and M. Hale (1986). “Criminology: testing alternative models of fear of crime”. Journal of Criminal Law and Criminology 77: 151–189. Thomas, C. and R. Bromley (2000). “City-centre revitalisation: problems of fragmentation and fear in the evening and night-time city”. Urban Studies 37(8): 1403–1429. Thomas, C. W. and J. M. Hyman (1977). “Perceptions of crime, fear of victimization and public perceptions of police performance”. Journal of Police Science and Administration 5(3): 305–317. Tulloch, M. (2000). “The meaning of age differences in the fear of crime.” The British Journal of Criminology 40(3): 451–467.

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Tulloch, J., D. Lupton, et al. (1998). Fear of crime volume one. Canberra, Centre for Cross Cultural Research. Valentine, G. (1989). “The geography of women’s fear”. Area 21(4): 385–390. van der Wuff, A., L. van Staaldiunen, et al. (1989). Fear of crime in residential environments: testing a social psychological model. The fear of crime. J. Ditton and S. Farrall (Eds.). Ashgate, Aldershot: 395–414. Walklate, S. (1998). “Crime and community: fear or trust?”. The British Journal of Sociology 49(4): 550–569. Warr, M. (1984). “Fear of victimization: why are women and the elderly more afraid”. Social Science Quarterly 65(3): 681–702. Warr, M. (2000). “Fear of crime in the United States: avenues for research and policy.” Criminal Justice 4: 452–489. Warr, M. and C. G. Ellison (2000). “Rethinking social reactions to crime: personal and altruistic fear in family households”. American Journal of Sociology 106(3): 551–578. Wilson-Doenges, G. (2000). “An exploration of sense of community and fear of crime in gated communities”. Environment and Behavior 32(5): 597–611. Zhang, L. N., S. F. Messner, et al. (2009). “Guanxi and fear of crime in contemporary urban China”. The British Journal of Criminology 49(4): 472–490.

Chapter 2

Why Is Fear of Crime a Serious Social Problem?

Individual Reactions There is a general consensus in the literature that the most significant effect of fear of crime is the reduced quality of life it imposes on those affected by it (Bannister and Fyfe, 2001; Box et al., 1988; Brown and Polk, 1996; Fisher and Nasar, 1992; Grabosky, 1995; Green et al., 2002; Fishman and Mesch, 1996; Mirrlees-Black and Allen, 1998; Nasar et al., 1993; Wilson-Doenges, 2000; Oc and Tiesdell, 1997; Tiesdell and Oc, 1998). The impact of fear of crime ranges from detrimental physiological changes to psychological reactions and behavioural adaptations. In terms of physiological changes, fear of crime is associated with increased heart rate, rapid breathing, decreased salivation and increased galvanic skin response (Warr, 2000). Endocrinic changes, such as the release of adrenaline into the bloodstream, may also occur to prepare us for a ‘fight or flight’ response (Skogan and Maxfield, 1981). Additonally, according to Kovecses (1990), fear is more generally associated with physical agitation; increased heart rate; lapses in heart beat; blood leaving face; shrinking of skin; straightening of hair; drop in body temperature; inability to move, breathe or speak; involuntary releases of bowels or bladder; sweating; nervousness; and dryness in the mouth. From a psychological perspective, fear of crime can produce negative feelings of anger, outrage, frustration, violation and helplessness (Ferraro and LaGrange, 2000; Warr, 2000). These feelings can extend to those of anxiety, distrust of others, alienation and dissatisfaction with life (Miceli et al., 2004; Morrall et al., 2010). Fear of crime is also strongly correlated with mental health and sometimes triggers mental illness (Green et al., 2002; Miceli et al., 2004), which in more acute or chronic cases can lead to advanced states of depression and long-term trauma (Ferraro and LaGrange, 2000; Spelman, 2004). Alongside these wide-ranging physiological and psychological effects, fear of crime can prompt people to change their behaviour. At the level of the individual, people generally respond to the fear of crime by adopting protective or avoidance behaviours (Box et al., 1988; Keane, 1998; Liska et al., 1988; Reid et al., 1998; Riger et al., 1982; Warr, 1985). The structural constraints and role obligations dictated by lifestyles and routine daily activities may circumscribe people’s ability to use precautionary tactics such as avoidance behaviours (Riger et al., 1982). Under

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these conditions, it appears that people are more likely to adopt protective measures, such as carrying a weapon, learning self-defence techniques, installing anti-burglary equipment or acquiring watch dogs (Cubbage and Smith, 2009; Krahn and Kennedy, 1985; Liska et al., 1988). Nasar et al. (1993) and Nasar and Jones (1997) conducted a series of investigations into the fear of crime at the Ohio State University campus which had a focus on protective and avoidance behaviours. The studies revealed that the campus was characterized by a climate of fear (Nasar and Jones, 1997), as 50% of survey respondents expressed safety concerns about routes they used on campus, while 73% indicated that they avoided areas they deemed unsafe (Nasar et al., 1993). When asked if they would carry some form of protection if they had to walk a particular route at night, 91% of the sample said they would (Nasar and Jones, 1997). On a broader scale, Teske and Arnold (1991) discuss results from a comparative victimization study in the United States and the Federal Republic of Germany which further indicate that people in a climate of fear are more likely to adopt protective measures. The authors found that survey respondents from Texas were 12 times more likely to have a gun in their houses for security purposes and were generally more likely to have installed security devices than respondents from Baden-Württemberg. The authors emphasize that Texas respondents were much more likely to have been the victims of a burglary, to know victims of a burglary and to feel that they may be victims of a burglary in the next year. In contrast to protective measures, avoidance behaviour primarily aims to reduce the risk of individuals being exposed to victimization, rather than reduce the risk of being victimized when exposed to threat (Skogan and Maxfield, 1981). Avoidance strategies often cause people to restrict their behaviour to places or times perceived to be safe or avoid certain activities they may perceive as dangerous, such as travelling by public transport, walking on certain streets or attending social activities (Box et al., 1988; Liska et al., 1988; Pantazis, 2000). Such behaviour, despite being a rational human reaction (Oc and Tiesdell, 1997), leads people to remove themselves from social activities and increases levels of distrust for others (Smith, 1987; Ross and Mirowsky, 2000; Wilson-Doenges, 2000). Keane (1998) investigated the influence of fear of crime as an environmental mobility restrictor on women’s routine movements. He found that a significant number of women were worried about walking alone in their area after dark and walking alone to their cars in a parking area. Of these women, a considerable number reported that they would change their behaviour and walk alone in their neighbourhoods and use parking areas more often if they felt safer. Keane (1998) concluded that increasing feelings of safety would increase women’s lifestyle choices and freedom of movement. Similar evidence for avoidance behaviours having a negative impact on the quality of people’s lives has been found by Liska et al. (1988). The authors found that constrained or avoidance behaviour increased, rather than decreased, fear. They suggest that avoidance behaviours may serve to decrease emotion-based fear in a dangerous situation, but may accentuate risk-based fear associated with anticipating a dangerous situation. Pantazis (2000) has likened the patterns associated with avoidance behaviours to current debates on poverty and social exclusion, which focus on people’s ability

Hypothesized Links Between the Fear of Crime, Disorder and Crime

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to participate in activities that others take for granted. A further parallel between social exclusion and the fear of crime relates to the unequal impact these problems have upon different elements of society. In general, levels of crime and poverty are higher among groups in society that experience a greater degree of social exclusion (Brennan et al., 2000; Hirschfield and Bowers, 1997). In a similar vein, the fear of crime has been consistently found to be higher in the poorest and most deprived neighbourhoods (Smith, 1987) and among women, the elderly and those with less education (e.g. Ferraro, 1995; Garofalo, 1979; Smith and Hill, 1991; Thomas and Bromley, 2000; Warr, 1984). Indeed, there is a common assertion that older people are prone to becoming “prisoners of fear” (Joseph, 1997; Pain, 2000; Stephens, 1999). Thus, the avoidance behaviours that individuals adopt in relation to their fear of crime have the potential to exert a substantial effect on the autonomy of many social groups and are a worthy area for ongoing research. However, the influence of such responses is not contained to the level of the individual, as fear of crime and the behavioural adaptations it prompts can have wide-ranging impact at the community level.

Hypothesized Links Between the Fear of Crime, Disorder and Crime In their widely quoted1 paper titled ‘Broken Windows’, Wilson and Kelling (1982) put forth a theory outlining a negative feedback loop whereby unchecked incivilities and disorder not only lead to fear of crime, but also crime itself. Using the broken window as a symbol for all types of disorder, their account of this causal relationship between disorder, fear and crime is now commonly referred to as the broken windows hypothesis or thesis (e.g. Harcourt, 1998; Sampson and Raudenbush, 1999; Loukaitou-Sideris, 1999). Broken windows hypothesis has proven highly influential in subsequent research and policy developments (e.g. Bratton, 1995, 1996; Skogan, 1990; Taylor and Covington, 1993; Tiesdell and Oc, 1998; Sampson and Raudenbush, 1999). The underlying tenet of the broken windows hypothesis is based on the assumption that if a window is broken and left unrepaired (or disorder is left unchecked) then more windows will be broken (more disorder will occur) (Wilson and Kelling, 1982). The authors of the thesis draw on the incivilities/disorder hypothesis to suggest that an unrepaired broken window (untended disorderly behaviour) becomes a signal that no one cares and leads to a breakdown in community controls. This lack of response creates conditions under which social and physical disorder can flourish. Responding prudently and fearfully, both residents and passers-by perceive these areas as uncontrolled and unsafe. They accordingly change their activities to stay

1 For additional information and interpretations see Doran and Lees, 2005; Gibbons, 2004; Greene, 1999; Harcourt, 1998; Millie and Herrington, 2005.

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off the streets and avoid areas perceived as unsafe. By doing so, the general public relinquish their roles of mutual support with fellow citizens and weaken forms of informal social control such as natural surveillance. Where the social fabric of a neighbourhood is undermined in this way, criminals, both opportunistic and professional, believe they have reduced chances of being caught or identified and will consequently operate more actively or invade the area (Wilson and Kelling, 1982). This leads to an influx of criminals, increased social and physical disorder and eventually the onset of serious crime. Various studies have supported the notion that social and physical incivilities and the presence of serious crime may act to increase the fear of crime (e.g. Borooah and Carcach, 1997; Covington and Taylor, 1991; Perkins and Taylor, 1996; Rountree and Land, 1996; Taylor and Covington, 1993). Thus, the fear of crime can be seen as one of the first steps in a positive feedback loop, because it results in residents adopting protective and avoidance behaviours which contribute to the breakdown of informal social control, more fear of crime and crime itself. This feedback cycle is illustrated in Fig. 2.1 below. There has been considerable debate over the validity of the broken windows hypothesis. Many researchers and practitioners readily accept the theory and it has therefore had considerable influence on research, policy and practice (see Doran and Lees, 2005; Harcourt, 1998; Stephens, 1999; Xu et al., 2005). The elements of broken windows hypothesis have also been used as a basis for the disorder and decline hypothesis (Skogan, 1986, 1990) which is described in more detail below. However, numerous critics also discount the fundamental assumptions of the broken windows

A ‘broken window’ is left unrepaired

This signals a breakdown in informal social controls

People physically and socially withdrawl from the community, avoiding uncontrollable/unsafe areas

More disorderly behaviour & broken windows (increased social and physical incivilities)

People become afraid of crime

There is an influx of criminals & more serious crime

Fig. 2.1 Flow chart illustrating the cycle of the broken windows hypothesis, highlighting the role of fear of crime

Disorder and Decline Hypothesis

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hypothesis (e.g. Bowling, 1999; Greene, 1999; Harcourt, 1998; Taylor, 2001). Innes (2004) contends that there is a serious lack of empirical evidence supporting the thesis. Harcourt (1998) criticizes broken windows hypothesis and policing strategies based on it, highlighting the fact that they neglect numerous other complex factors that also contribute to crime. The proposition that people respond equally to both ‘broken windows’ and ‘broken people’ has also been challenged (Innes, 2004). One avenue that has not yet been explored thoroughly comprises the spatial and temporal components of the hypothesis – many of the links outlined in the cycle relate to the areas where social and physical disorder become concentrated, or the general public adopt behaviours which, over time, create conditions under which crime can flourish. The spatial and temporal scales at which these processes are likely to be operating are likely to vary considerably from short term (hours or days) to much longer term (years).

Disorder and Decline Hypothesis Skogan’s (1986, 1990) disorder and decline hypothesis expands upon the broken windows hypothesis (see Fig. 2.2 below). Like the broken windows hypothesis, the disorder and decline hypothesis begins with the justification that people gather information about the level of crime and safety in their neighbourhood through environmental cues (Skogan and Maxfield, 1981). Skogan (1990) maintains that signs of disorder are associated with high levels of risk and imply that neighbourhood systems of social control have broken down.2 When people encounter signs of disorder they physically withdraw from those areas, confining their activities to those times and routes perceived as the safest. This reduces the amount of informal social surveillance that occurs naturally with pedestrian activity (Skogan, 1986; Skogan and Maxfield, 1981). However unlike Wilson and Kelling, Skogan elaborates on the added psychological withdrawal of residents from the streets (Skogan, 1986). Skogan and Maxfield (1981) assert that crime and disorder, through fear of crime, generate suspicion and distrust. This, in turn, has an atomising effect upon individuals and households (Skogan and Maxfield, 1981).3 Skogan then argues that disorder restricts the neighbourhood potential for organizational life and mobilization (Skogan, 1986). In addition Skogan (1986) emphasizes spatial considerations and proposes that perceptions of disorder could cause a decrease in the geographic area that people feel responsible for. This further serves to weaken community mechanisms of

2

Skogan (1990) specifically defines disorder as ‘direct, behavioral evidence of disorganization’. Crime and disorder undermine people’s trust that their neighbours share common goals and norms (Skogan and Maxfield, 1981). This can lead to hostility and antipathy (Skogan, 1990). Disorder reduces resident confidence that their individual and collective actions can overcome disorder, (Skogan, 1990; Skogan and Maxfield, 1981). 3

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Disorder / Incivilities

Interpreted as a breakdown in community controls

People become afraid of crime

Why Is Fear of Crime a Serious Social Problem?

Demographic collapse of the neighborhood

Deteriorating business conditions & local housing market

There is an influx criminals & crime

Physical & psychological withdrawal of the community

Delinquency and deviance among youth

Weakening of the processes of informal social control

Decline in the organizational life of the neighborhood

Fig. 2.2 Flow chart illustrating the disorder and decline hypothesis

informal social control and surveillance.4 With a decrease in social control and community-level capacity to combat disorder, Skogan mirrors Wilson and Kelling’s argument in stating the neighbourhood will invite ‘outside troublemakers’ who bring additional crime and disorder (Skogan, 1986). Skogan also elaborates on the economic impact of disorder on affected neighbourhoods. The first point he makes is in relation to a deterioration of local business conditions (Skogan, 1986). With fewer people on the streets, there will be fewer business customers resulting in shops being forced to close down. These empty shops are likely to remain abandoned

4 Skogan explains this using the concept of ‘territoriality’, which is a ‘set of attitudes and behaviours regarding the regulation of the boundary that surrounds people’s personal household space’ (Skogan, 1986). He claims that with healthy levels of territoriality residents will conduct surveillance over a wide area (Skogan, 1986). Surveillance is facilitated by personal recognition of one’s neighbours and a belief that local standards of appropriate public behaviour are widely shared (Skogan, 1990). These factors diminish, thereby negating the underlying necessities for social surveillance and the psychological defence of public space.

Disorder and Decline Hypothesis

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or be converted to non-retail establishments. Economic forces favour those traditionally ‘unsavoury’ businesses, such as bars, transient hotels, x-rated outlets and massage parlours. The author argues that these businesses, and the ‘unsavoury’ people they attract, will further decrease the desirability of the area for people with a low tolerance for disorder (Skogan, 1986). Skogan’s second assumption is that, with an increasingly bad reputation, the local housing market becomes unstable (Skogan, 1990). Residents who are able to move relocate to other areas, and fewer people want to move into or invest in the area. Skogan states that this leads to a downward turn in the real estate market of affected areas and causes further deterioration and abandonment of buildings (Skogan, 1990).5 At this point, the disorder and decline hypothesis implies that disorder and these consequent social and economic problems continue to ‘feed on themselves, spiralling neighbourhoods deeper into decline’ (Skogan, 1986). Feedback processes ensure fear of crime increases until it is ‘incapacitating’ (Skogan, 1986; Skogan and Maxfield, 1981). The end of this cycle is characterized by a demographic collapse of the neighbourhood, when crime and disorder continue but there are few residents left to define it as a problem (Skogan, 1986). Schuerman and Kobrin (1986) argue that those areas characterized by at least three decades of high crime are ‘lost territory to the rest of society’ (in Skogan, 1986). Skogan (1990) cemented his theory on the links between disorder and serious crime with empirical research. Disorder was linked more strongly with higher crime levels than were other neighbourhood characteristics, such as poverty, instability in the housing market, and predominantly minority racial composition among residents. Further, the investigation found that disorder, both directly and as a precursor to crime, played an important role in neighbourhood decline. A number of researchers have supported Skogan’s (1990) findings. For example Borooah and Carcach (1997) investigated fear of personal and housing crime in relation to a common set of explanatory variables. The authors concluded that lack of neighbourhood cohesion, neighbourhood incivility and perception of relatively high neighbourhood crime levels contributed significantly to the probability of being afraid and to the risk of victimization. Similarly, in their own study, Ross and Mirowsky (2000) declare disorder and decay are highly correlated with crime and share many indicators. Kelling and Coles (1997) also stated that Skogan’s research supports the broken windows hypothesis. Thus some researchers have also concluded that fear of crime creates an environment where crime is more likely (Millie and Herrington, 2005). Others have also gone so far as to say that fear of crime is now a larger problem than crime itself (Bennett, 1991; Farrall et al., 2000; Warr, 1984; Hale, 1996). In contrast, Markowitz et al. (2001) point out that studies supporting the broken windows and disorder and decline theories are largely based on cross-sectional data. As the theory is longitudinal in nature more evidence is necessary to confirm

5 Nevertheless, Skogan does recognize that other factors play an important role in determining demand for property (Skogan, 1986).

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the causal effect of disorder. However, Markowitz et al. (2001) do acknowledge that disorder may increase crime indirectly through its effect in increasing fear of crime and decreasing social involvement and collective efficacy. Harcourt (1998) also found that Skogan’s data did not support the claim that crime is related to disorder. While Harcourt confirmed that certain crimes like physical assault and robbery are at first significantly related to disorder, he argues that this relationship disappears when the variables of neighbourhood poverty, stability and race are held constant. Similarly, Sampson and Raudenbush (1999) did not find convincing evidence to support the strong versions of the broken windows or disorder and declines theories. Disorder was only a moderate correlate of predatory crime, and varied consistently with antecedent neighbourhood characteristics. Despite the lack of evidence for a direct association between disorder and crime, the authors suggest that if disorder operates in a cascading fashion by undermining residential stability and discouraging efforts of building collective responses, it would indirectly have an effect on crime. While emphasizing that it is not the disorder that causes the crime, but rather poor social control that causes both, this scenario is essentially the same as that outlined by Skogan (1990), where fear of crime plays an important role in determining the actions of residents within a community.

Economic Impact of Behavioural Responses to Fear of Crime The potential for the fear of crime to have a negative economic impact upon society has been recognized by a number of authors other than Skogan in his disorder and decline hypothesis (e.g. Brown and Polk, 1996; Grabosky, 1995; Hamermesh, 1999b; Liska et al., 1988; Oc and Tiesdell, 1997). Individuals who respond to the fear of crime by adopting avoidance behaviours incur a cost to both themselves and society (Oc and Tiesdell, 1997), as they keep away from the restaurants, shops, jobs and residences located in areas they perceive as dangerous (Liska et al., 1988). The opportunity costs associated with such behaviour, while difficult to quantify, are likely to be substantial (Oc and Tiesdell, 1997; Ayers and Levitt, 1998). Jackson and Gray (2010) note that there can be ‘hidden costs’ associated with such actions, through spending time or money on protective measures. A number of researchers have paralleled Skogan’s assertion that fear of crime has a negative impact on the housing market as a result of discouraging homebuyers and causing out migration (Katzman, 1980 in Smith, 1987; Gibbons, 2004; Oc and Tiesdell, 1997). Retail businesses suffer a shortage of customers as the most affluent people leave the neighbourhood and people generally avoid the streets (Conklin, 1971; Oc and Tiesdell, 1997). In turn businesses close down, relocate and new investment is suppressed, further reducing the activity and attraction of the area (Garofalo, 1981; Spelman, 2004; Oc and Tiesdell, 1997). The negative economic impact associated with the avoidance of retail areas has been linked to the attraction of youths to such environments. For example, Brown and Polk (1996) discuss what they term the ‘mall problem’ in Australia. By providing a day and night gathering and entertainment venue, shopping malls often prove an attractive environment for

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17

unemployed and disengaged youths. This frequently results in malls becoming associated with problems, such as drinking, abusive language, fighting and drug use. The authors argue that such behaviours serve to work against the intended commercial function of malls by frightening away potential customers. A number of authors have identified similar trends in Britain (e.g. Oc and Tiesdell, 1997; Thomas and Bromley, 2000; Tiesdell and Oc, 1998). Thomas and Bromley (2000) observe that, despite the fact that many British cities have a thriving night-time economy, entertainment is largely centred around the ‘pub-and-club’ youth culture. The authors argue that the association of youth with threatening behaviour, such as heavy drinking, drugs and violent incidents has reduced the attraction of many city centres for a broader spectrum of the population. Oc and Tiesdell (1997) suggest that this denies large numbers of men and even greater numbers of women the use of city centres at night and has a significant economic and employment cost. On a broader scale, Warr and Ellison (2000) state that fear of crime and the consequent avoidance of dangerous places is so common and recognized in urban areas that it affects the ecology and economies of US cities. Avoidance behaviours resulting from safety concerns may lead to mass cancellations and financial problems in tourist destinations (Ferraro, 1995; Mawby et al., 2000). Brunt et al. (2000: 422) found in a survey of British holidaymakers that 42% of respondents said they had ruled out at least one country because of crime-related problems. Cothran and Cothran (1998) term this dependence of tourism demand upon perceptions of safety the ‘safety elasticity of demand’. The authors argue that tourism is a discretionary activity and, no matter how attractive a destination is, tourists will stay away if they feel their safety cannot be guaranteed. In the case of Mexico, they suggest that if American tourists began to act upon increasing levels of fear of crime by visiting alternative destinations the results for the Mexican tourist industry would be disastrous. Hamermesh (1999a) investigated the timing of work in the United States and found that work in the evenings and at night had declined sharply between the 1970s and 1990s. Using the assumption that fear of crime is most likely to have an effect during the evening and at night, Hamermesh (1999b) investigated the effect of crime and the fear of crime on the timing of work. The author found that higher homicide rates significantly deterred working in the evening and at night and argued that criminal activity imposes a negative externality on the labour market because crime, or the fear of crime, generates departures from optimal patterns of work timing. The author describes this behaviour in terms of a trade-off where higher crime rates reduce the incentive to labour to the point where it becomes insufficient for some of the workers to overcome their fear of crime. This impacts upon workers as they implicitly forego some earnings, and affects society because production shifts away from times when the marginal worker will be more productive. The author estimates that the impact of homicide rates on work timing costs the USA between $4 and $10 billion a year. Protective behaviours can also have direct economic impact on individuals and communities. Target hardening through the use of various security measures in fortifying their homes and places of work, such as outside lighting systems, watch dogs,

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extra locks and weapons (Liska et al., 1988; Skogan and Maxfield, 1981; Teske and Arnold, 1991) incur a direct cost to the individual. Helsley and Strange (1999) suggest that fear of crime in the United States has led to increased spending on private security. Ayers and Levitt (1998) emphasize the fact that private expenditure on selfprotection potentially dwarfs the $100 billion spent on criminal justice each year in the United States. Not only does fear of crime affect the economies of the local neighbourhood and individuals, but also that of the wider government. Schemes designed by governments to reduce the fear of crime also involve significant cost. For example, investment in CCTV surveillance systems by central and local British government between 1994 and 1997 has been estimated to be in excess of £100 million (Norris and Armstrong, 1998 in Ditton 2000). There are significant time and monetary costs associated with increased public policing in affected communities (Murray et al., 2001). State or local council resources are also used in the upkeep of affected areas and the management of disorder. The firms providing security measures could be seen as deriving economic benefit from the fear of crime. Indeed Davis (1990) goes so far as to suggest that the market provision of security generates its own paranoid demand. Others express less extreme views but nonetheless attribute part of the rapid growth in the security industry to the fear of crime (e.g. Lymes, 1997; Helsley and Strange, 1999). The avoidance and protective behaviours that people adopt to cope with the fear of crime have the potential to generate negative, and in some cases, positive externalities. People who perceive that their neighbourhood is deteriorating often act on their fear of crime and choose to leave the city (Kelling and Coles, 1997). Where this takes place, the people and firms that reallocate their activities burden society with an indirect monetary cost (Hamermesh, 1999b). People remaining in areas where more prosperous citizens have left potentially lack the resources to protect themselves against crime. For example, Dililio (1996) argues that the relative lack of financial and political resources experienced by law-abiding people in inner-city black communities in the United States limits their ability to target-harden their homes, stores, parks and schools and may be partly responsible for the high rates of criminal victimization in these communities. Other studies have established strong links between the concentration of economic disadvantage and crime (Krivo and Peterson, 1996; Weatherburn et al., 1999). Freeman et al. (1996) suggest that the spatial concentration of crime in poor neighbourhoods is based on a positive externality that criminals create for each other. The externality exists because, if police resources are held constant, criminals stand a smaller chance of being caught if there are more of them in an area. Protective measures have also been linked to the redistribution of crime between communities. For example, Helsley and Strange (1999) argue that protective actions such as the building of gated communities or the adoption of target-hardening procedures have the sole objective of diverting or deterring criminals ex ante and have the potential to impose negative externalities which impact upon other sections of society. The authors investigate a number of aspects of gating on the level and spatial distribution of crime with the key result being that gating, by diverting crime to the business district, can reduce legitimate employment opportunities and increase

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the number of active criminals and the aggregate level of crime. Ayers and Levitt (1998) investigated the effect of Lojack, a small, unobservable radio transmitter hidden within vehicles, and found that its use yields positive externalities through general deterrence. However, the authors note that as most forms of personal protective measures are highly visible they are more likely to redistribute, rather than reduce, the occurrence of crime. Hence protective measures that generate positive externalities are likely to be in the minority.

Chapter Review: Potential Problems Not to Be Ignored and a Need for Spatially Explicit Research It is commonly accepted that fear of crime is a major social problem (Liska et al., 1988). Studies have confirmed that fear of crime disrupts neighbourhood cohesion (Nasar et al., 1993); fractures the sense of community and neighbourhood (Box et al., 1988; Ross and Mirowsky, 2000); creates interpersonal distrust (Garofalo, 1981); breaks down social relations and attachment (Spelman, 2004); leads to social isolation (Doeksen, 1997; Ross and Mirowsky, 2000); adds to an erosion of social control and social order (Ross and Mirowsky, 2000); damages the public image of a community and causes avoidance behaviour in potential visitors (Doeksen, 1997; Nasar et al., 1993; Skogan, 1990; Warr, 2000); and causes a removal of ‘eyes on the street’ and informal natural surveillance (Jacobs, 1961; Painter, 1996; Samuels and Judd, 2002). A common thread running through these varied and serious impacts are the protective and avoidance behaviours that people adopt in relation to their fear of crime. The well-known broken windows hypothesis (Wilson and Kelling, 1982) and Skogan’s (1986, 1990) disorder and decline hypothesis have provided theoretical frameworks which outline potential interactions over space and time between crime, disorder and fear. Despite rigorous debate about the efficacy of such hypotheses, there is a consensus among much extant research that fear of crime and the associated protective and avoidance behaviours evident at the individual level have the potential to have a collective and detrimental impact at the community level. Given the heavy emphasis of temporal factors and potential impact in specific areas or neighbourhoods, it is also clear that there are avenues for explicitly spatial research into the hypothesized links between crime, disorder and fear.

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Chapter 3

What Causes Fear of Crime?

Criminal Opportunity and Risk of Victimization Theories While Cohen and Felson’s (1979) routine activities hypothesis, also known as the criminal opportunity or risk of victimization hypothesis, was developed to explain where and when people engage in crime, it has also been adapted to assist understanding of fear of crime (e.g. Ferraro, 1995). It proposes that rationally motivated offenders commit crime when opportunities, in space and time, provide a potential victim and an absence of capable guardians (Cohen and Felson, 1979). These opportunities are systematically related to the routine activities of the potential victims and their guardians1 (Cohen and Felson, 1979). Variation in routine activities differentially places people at risk of victimization by structuring their convergence in time and space with motivated offenders. This convergence increases their likelihood of victimization2 (Cohen and Felson, 1979). In a similar vein to offenders who assess environments in order to increase their opportunity for crime, potential victims may also make judgements when defining places and times as risky or threatening (Brantingham and Brantingham, 1993; Ferraro, 1995). When applied in conjunction with micro-scale perspectives, such as symbolic interactionism, criminal opportunity hypotheses facilitate analyses which seek to explain the spatial and temporal distribution of fear of crime3 (Ferraro, 1995). However, multiple studies concur that fear of crime, and people’s perception of risk of victimization, far exceeds the reality of actual crime rates and levels (e.g. see: Cozens, 2002; Liska et al., 1988; Miceli et al., 2004; Nelson et al., 2001; Smith, 1987; Taylor and Hale, 1986; Tulloch, 1998). This applies even when assuming a liberal amount of unreported crime (Liska et al., 1988; Painter,

1

See also: Bursik, 1988; Cochran et al., 2000; Vold et al., 2002; Walklate, 2003 Criminal opportunity theory branches into numerous related theories focusing on routine activities affecting people’s risk of victimization. For example Clarke (1980) and Cornish and Clarke (1986) propose the rational choice theory. Similarly, Miethe (1990) propose the structural choice theory. 3 For example, the micro-scale environmental cues and the wider macro-scale structural and geographic influences are taken into account (Ferraro, 1995). 2

B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_3,  C Springer Science+Business Media, LLC 2012

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1996; Taylor and Hale, 1986). Fear of crime thus appears out of proportion to the objective risks of victimization (Warr, 2000). Therefore, it is paramount that researchers hoping to influence the design of fear-reduction strategies investigate other potential factors associated with fear of crime. The first of these factors relate to characteristics of demographic groups experiencing relatively high levels of fear of crime.

Demographic Theories Explaining Fear of Crime The demographic theories have dominated fear of crime research since its conception (Farrall et al., 2000). They examine whether people’s fear of crime is associated with their experiences of crime or feelings of vulnerability. Ultimately, each demographic hypothesis seeks to explain why some socio-demographic groups are more afraid of crime than others. This knowledge is important in providing an understanding of the nature of public fear of crime, which is a valuable component of many fear-reduction initiatives. The group of demographic theories comprises the victimization hypothesis, indirect-victimization hypothesis and vulnerabilities hypothesis.

Victimization Hypothesis The victimization hypothesis posits a positive relationship between direct experience of victimization and fear of crime (Crank et al., 2003; Skogan and Maxfield, 1981). Direct victimization recognizes only those victims who have been directly affected by the actions of an offender or incur some immediate loss following a victimization (Clark, 2003; Mesch, 2000). Under the victimization theory, previous experiences of direct victimization increase one’s sensitivity to risk. Past victims therefore have an increased likelihood of defining situations as dangerous and perceiving the risks of victimization as greater (Mesch, 2000). Drawing on Janoff-Bulman’s (1985) three ‘theories of reality’, Clark (2003) discusses the stages of emotional loss that victims endure following criminal victimization. The first of these losses is the desecration of the belief in one’s personal invulnerability, that victimization ‘won’t happen to me’. Similarly, the belief in the ‘social law’ that ‘good people do not get hurt’ is also defeated. In turn, this translates into the third emotional loss, which involves a detrimental turn in one’s selfimage (in Clark, 2003). Notions of self-worth are affected as victims ‘. . . recognise their self limitations, powerlessness, helplessness and neediness . . .’ (Clark, 2003). Societal attributions of blame are also said to reinforce these views and lead the victims to have less trust in themselves and others (Janoff-Bulman, 1985 in Clark, 2003). It is hypothesized that these reactions following victimization represent a new sense of personal vulnerability, which could result in increased fear of crime. In addition to this, victimization can create reactions of confusion, shock, helplessness,

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fear and anxiety and can lead to depression and post-traumatic stress disorder, reactions that could further increase fear of crime (Clark, 2003). In this sense, the victimization theory is related to the vulnerabilities and the indirect-victimization theses, which are discussed later in this section. A multitude of studies have investigated the victimization hypothesis, with different studies obtaining different results (Borooah and Carcach, 1997). Numerous studies find a positive relationship between experience of victimization and fear of crime (Crank et al., 2003). Of these, many confirm a strong and direct relationship in support of the theory (e.g. Akers et al., 1987; Cates et al., 2003; Ferraro, 1995; Katz et al., 2003; Smith and Hill, 1991; Skogan, 1990). Others find only a positive but weak relationship exists (e.g. Akers et al., 1987; Cates et al., 2003; Evans and Fletcher, 2000; Garofalo, 1979; Katz et al., 2003; Liska et al., 1988). In contrast, there are studies that either fail to find an association (e.g. Borooah and Carcach, 1997; Rader, 2004), or indeed find a negative association, between victimization and fear (Evans and Fletcher, 2000). Overall, the mixed results have prompted some researchers to conclude ‘. . . there is little consistent evidence to suggest that personal (direct) victimization has an impact on fear of crime’ (Katz et al., 2003). The victimization theory thus remains unsubstantiated (Borooah and Carach, 1997). While the conflicting evidence may be a consequence that fear and experience of victimization depends on other underlying factors, the surveying methods and fear of crime measures could also account for some of the variation. Generally, victimization is assessed in surveys by asking respondents about their experiences in the 12–14 months prior to the survey (Gray and O’Conner, 1990; Akers et al., 1987; Evans and Fletcher, 2000). The given time period may also not be relevant to many respondents. For example, people may either still feel the impact of victimization beyond this timeframe (Evans and Fletcher, 2000) or have long been implementing fear neutralization techniques. Regardless, as the victimization thesis makes intuitive sense (Crank et al., 2003), few researchers have been able to elucidate why previous victims of crime may not be afraid of crime (Katz et al., 2003). Agnew (1985) suggested that previous victims may neutralize their fear of crime by employing techniques, such as denial of injury or damage, acceptance of responsibility or denial of future vulnerability (cited in Katz et al., 2003). Although it is a major coping task for victims to rebuild their views of the world and themselves following victimization, a victim’s sensitivity to fear of crime is reduced over time (Mukherjee and Carach, 1998 in Clark, 2003).

Indirect Victimization Hypothesis The indirect victimization hypothesis accounts for the host of studies which find that ‘non-victims’ also experience fear of crime. The indirect victimization hypothesis recognizes people can experience victimization vicariously and may experience the same emotions that result from a direct victimization when they hear of others’ crime encounters (Clark, 2003; Hanson et al., 2000). The signal crimes perspective, discussed later, even suggests that crime and disorder have the same effect

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regardless of whether they are encountered in person or indirectly (Innes et al., 2002).4 Indirect-victimization research focuses on how crime information is obtained. Findings point towards exposure to crime through media accounts and interpersonal communication (Rountree and Land, 1996). The Media and Fear of Crime Many studies suggest that fear of crime is a product of media exposure (Killias and Clerici, 2000; Romer et al., 2003; Weitzer and Kubrin, 2004). Researchers supporting indirect victimization through the media have taken a number of different approaches. These are known as the cultivation,5 substitution,6 resonance,7 socialcomparison8 and interpersonal-diffusion9 hypotheses. Overall, they argue the media exacerbates perceptions of risk of victimization, and therefore induces fear of crime (Lane and Meeker, 2003a). In contrast, some researchers discredit the link between media exposure and fear of crime (Lane and Meeker, 2003b; Romer et al., 2003). Other researchers find no relationship between fear of crime and the media when demographic characteristics or neighbourhood levels of crime are examined (Katz et al., 2003).10

4 The signal crimes theory names directly encountered crimes ‘situated signal crimes’ and indirectly encountered crimes ‘disembedded signal crimes’ (Innes et al., 2002). 5 Cultivation theorists argue the media portrays an unrealistic world rife with crime, thereby fostering perceptions of increased risk and provoking fear of crime. The cultivation thesis argues media crime coverage has a uniform effect regardless of the audience (see Heath and Gilbert, 1996; Jopson, 1995; Lupton and Tulloch, 1999; Romer et al., 2003; Skogan and Maxfield, 1981; Totaro, 1988; Tulloch, 2000; Weitzer and Kubrin, 2004; Williams and Dickinson, 1993). 6 In contrast, the substitution thesis suggests that audience characteristics and contextual differences affect the impact of media on fear of crime. It propounds media exposure has a greater influence on fear of crime experienced by non-victims than victims (see Chiricos et al., 1997; Heath and Gilbert, 1996; Lane and Meeker, 2003b; Weitzer and Kubrin, 2004). 7 The resonance thesis, while also acknowledging that media affects audiences differently, expects the opposite reaction to the cultivation thesis. It considers the media influences fear of crime only when the crime coverage resonates with crime experiences of the audience, acting to mutually reinforce present feelings of fear (Weitzer and Kubrin, 2004). 8 In line with the resonance thesis, the social comparison thesis focuses on crime coverage pertinent to the audience’s locality. It proposes that crime reported in one’s neighbourhood fosters fear of crime, whereas crime reported in remote areas does not (Romer et al., 2003). 9 The interpersonal diffusion thesis also reflects the regional relevance of crime reports. It argues fear of crime is amplified when crime accounts resonate with the audience’s direct or indirect experiences of victimization. Only when media reports are compounded with other local sources of information about crime does fear of crime increase (Romer et al., 2003). 10 Supporters of the real-world thesis argue that fear of crime is more a result of objective conditions including personal victimization, actual crime rates and neighbourhood characteristics, than sensationalist stories reported by the media (Chiricos et al., 2000; Lupton and Tulloch, 1999; Weitzer and Kubrin, 2004). Additionally the operationalization and measurement of fear of crime can alter the relationship between media exposure and fear of crime (Heath and Gilbert, 1996).

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Interpersonal Communication and Fear of Crime The second element of the indirect victimization hypothesis focuses on the relationship between interpersonal communication, rather than the media, and fear of crime. The interpersonal communication hypothesis assumes that knowledge of others’ experience of criminal victimization spreads throughout the social networks of a community (Mawby et al., 2000; Taylor and Hale, 1986). Using the same explanation as cultivation theorists, it is presumed that knowledge about crime attained through interpersonal communication adds a crime multiplier and therefore increases the perceived risk of victimization (Taylor and Hale, 1986). It is thought that this effect will be maximized for people who are well entrenched in social networks (Lewis and Salem, 1986; Skogan and Maxfield, 1981; Crank et al., 2003). Generally, researchers find that there is a stronger relationship between fear of crime and indirect victimization than direct victimization (Katz et al., 2003; Mawby et al., 2000). For instance, using the same dataset many researchers have found that vicarious experience of victimization significantly increases fear of crime, while direct experience of victimization was not significantly related to fear of crime (Lewis and Salem, 1986; Skogan and Maxfield, 1981; Katz et al., 2003). Skogan and Maxfield (1981) concluded that indirect victimization is more common and widespread than direct victimization and should logically have a stronger effect on fear of crime. Later, Hale (1996) stated that the fear-of-crime response could be greater via indirect victimization because hearing about crime ‘allows one’s imagination full scope without perhaps the same urgency to find some coping strategy . . .’ (quoted in Katz et al., 2003). It is also likely these stories will be about local events and local victims, and hold the potential for greater personal impact for those hearing about them (Skogan and Maxfield, 1981). Once indirect knowledge about victimization is obtained, fear of crime is also unlikely to dissipate rapidly (Taylor and Hale, 1986). Adding to this, many cultural geographers have gone on to state that certain areas of a neighbourhood are feared because of their reputation, which can be considered a consequence of interpersonal communication (Koskela and Pain, 2000). Ferraro (1995) argues that indirect victimization has a strong effect on constrained behaviour in such areas (Ewald, 2000). When non-victims hear about a criminal victimization they will compare themselves to the victim. They may distinguish themselves from, or relate to, the victim by either what they did or the kind of people they are (Clark, 2003). Thus, the indirect victimization theory is influenced by notions of vulnerability and socio-demographic background (Skogan and Maxfield, 1981 in Taylor and Hale, 1986).

Vulnerabilities Hypothesis The vulnerabilities hypothesis is based on the assumption that different sociodemographic groups experience different levels of fear of crime and exhibit this fear differently (Warr, 2000; Liska et al., 1988). The vulnerabilities hypothesis also

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explains two other trends. After taking the risk of victimization into account, many studies conclude that typically fearful socio-demographic groups, like women and the elderly, are the least likely to be victimized (Katz et al., 2003; Painter, 1996; Pantazis, 2000; Taylor and Hale, 1986). Vulnerabilities are used to account for this discrepancy and its converse, namely an apparently lower-than-warranted fear of crime in some groups, such as young men, who have greater actual risks of victimization (Katz et al., 2003; Lane and Meeker, 2003b). Stinchcombe (1978) first introduced the concept of vulnerability. Perloff (1983: 43) later defines it as ‘. . . a belief that one is susceptible to future negative outcomes and unprotected from danger or misfortune’. Vulnerability is determined by three major factors, namely exposure to risk, loss of control and seriousness of consequences (Killias, 1990). Essentially it is not based on objective threat, yet occurs if one perceives himself or herself as vulnerable to criminal victimization (Katz et al., 2003). The concept of vulnerability highlights the importance of including anticipatory fear, or anxiety, in fear-of-crime research (Sacco and Glackman, 1987). It also explains that fear of crime, in contrast to perceived risk, depends on one’s perception of the seriousness of a particular offence and one’s risk sensitivity to it (Cates et al., 2003; Mesch, 2000). This is mirrored by other researchers such as Wurff et al. (1988) who argue that fear is ‘. . . the perception of a threat to some aspect of well-being, concurrent with the feeling of inability to meet the challenge . . .’. Skogan and Maxfield (1981) distinguish physical vulnerabilities from social vulnerabilities. Physical vulnerability refers to one’s perception of his/her susceptibility to attack, ability to resist an attack and ability to recover health following an attack (McCoy et al., 1996; Skogan and Maxfield, 1981). Such physical vulnerabilities include health, body size, self-defence capabilities and disabilities. Social vulnerability reflects how a person’s position in society differentially affects his/her exposure to victimization and his/her capacity to cope with the consequences of victimization (McCoy et al., 1996; Ortega, 1987; Skogan and Maxfield, 1981). Social vulnerabilities are a function of an individual’s position in society. They include income, residential status, educational level, ethnic background, living alone and experiences of victimization (Skogan and Maxfield, 1981). Purist vulnerabilities theorists do not see objective conditions in the external world as the source of the public’s fear of crime. Instead they encourage research on those who experience fear of crime to be more sensitive to the ‘. . . biographies, characteristics, and social circumstances of the fearful . . .’ and how they ‘acquire a sense of subset ability’ (Sacco and Glackman, 1987). Some researchers extend this further by pointing out that using general socio-demographic predictors to account for fear of crime masks potentially significant individual psychological factors, which should be considered (Farrall et al., 2000).

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Review: An Abundance of Contested Demographic Studies The demographic theories look at people’s experiences of victimization, indirect victimization and their vulnerabilities to explain fear of crime. The demographic theories largely account for the seemingly different levels of fear exhibited by different socio-demographic groups. Despite an abundance of research, the demographic theories remain contested. This adds credence to the notion that additional factors and complexities may be associated with fear of crime.

Social Theories Explaining Fear of Crime The social theories discussed in this section argue that fear of crime reflects a general state of anxiety caused by a change or breakdown of a range of different societal factors. This section starts with the two most prominent hypotheses, the risk society and social disorganization hypotheses. The social disorganization hypothesis branches into a framework of various independent, but inter-related models (Covington and Taylor, 1991), which are also discussed below. These models include the subcultural diversity, social integration, community concern and social change hypotheses. The purpose of this chapter is to review the core components of different theories, rather than test them rigorously and as such the chapter does not fully engage with all angles of debate evident in the literature on this issue. Rather, social theories are discussed because they attempt to explain fear of crime and frequently contribute towards fear-reduction strategies

Risk Society Hypothesis Drawing upon notions of the ‘risk society’, fear of crime is conceptualized as an expression of people’s wider feelings of insecurity or uncertainty about life. Risk society theorists commonly propose that fear of crime provides an outlet to express general feelings of anxiety that predominate in everyday lives. While the literature on risk society is extensive, a few pertinent points are emphasized here. According to Beck (1992), the founder of the hypothesis, processes of industrialization produce numerous new, unforeseen, unpredictable and uncontrollable risks (Dean, 1999; Ewald, 2000; Lupton, 1999). The risks are extensive, irreversible and affect all individuals regardless of their social position or class (Beck, 1992; Ewald, 2000; Girling et al., 2000). Furthermore the risks are incalculable and unsatisfactorily insurable, making them additionally threatening and anticipatory (Beck, 1992; Dean, 1999; Ewald, 2000). According to Beck (1992), a risk society, defined by the statement ‘I am afraid’, emerges with these risks.

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Lianos and Douglas (2000) come to a similar conclusion. They contend present societies are in a state of ‘dangerization’11 which is portrayed by a continuous detection of potential threats, which ultimately results in fear and anxiety.12 When in a state of dangerization, the unknown ‘other’ is perceived as dangerous (Lianos and Douglas, 2000). This person usually operates beyond one’s managed territory and possesses differences in his/her appearance or behaviours. As a result deviance is associated with unknown individuals or groups, who consequently trigger fear and avoidance behaviours (Lianos and Douglas, 2000).13 In turn, the signs and behaviours associated with these groups become automatic indicators of dangerousness (Lianos and Douglas, 2000; Rose, 2000). Beck (1992) similarly claims that it is not the risks themselves that cause fear and unease but those people who represent the risks. The underlying state of anxiety14 is projected onto other individuals or social groups. Numerous other theorists agree that crime becomes a scapegoat for intangible insecurities and anxieties (Ewald, 2000; Hollway and Jefferson, 2000; Lupton and Tulloch, 1999).15 In a risk society not only is anxiety a part of everyday life, but so too is crime and the threat of crime (Stanko, 2000).16 Researchers should be aware of this possibility, as it affects fear-of-crime measurement approaches. Survey questions should therefore be as specific and precise as possible in targeting fear of actual ‘crime’. Similarly, survey questions should be specific in targeting ‘fear’ of, not concern about, crime. This is pertinent to the social disorganization group of hypotheses, discussed below.

Social Disorganization Hypothesis The social disorganization hypothesis implies that fear of crime is linked to concern about the destruction of social organization. Since its origins in the 1920s and 11

Like Beck’s thesis, dangerization is brought about by a change in institutional control over collective social interaction (Lianos and Douglas, 2000). 12 Stanko (2000) argues that we live in an age fraught with uncertainty. Hope and Sparks (2000) echo similar sentiments and state that ‘. . . fear reaches down into the unilluminated corners of the inner life . . .’ 13 These ‘others’ are generally depicted as dangerous in adherence with existing biases and discriminations (Lianos and Douglas, 2000). 14 Sparks also argues that fear is never caused by a specific target (Sparks, 1992). See also Dammert and Malone, 2003; Hope and Sparks, 2000; Gottfredson, 1984; Lupton and Tulloch, 1999; Mawby et al., 2000; Pain, 2000; and Stanko, 2000. 15 Hollway and Jefferson (2000) argue ‘unconscious’ anxieties are displaced onto more tangent external threats (Hollway and Jefferson, 2000). They report criminals are easily identifiable targets and thus provide ‘. . . a repository for anxieties about other fears that are more intractable and are diffuse for the individual . . .’ (Lupton and Tulloch, 1999). Ewald (2000) also asserts that the psychological experiences associated with victimization, such as feelings of loss of control, are similar to those anxieties of the risk society and therefore crime becomes a suitable scapegoat. 16 With fear of crime at the forefront of the risk society, the control and prevention of risk becomes a preoccupation of everyday living (Vaughn, 2002; Walklate, 2000).

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formal naming in 1942 by Shaw and McKay, the social disorganization hypothesis has dominated criminological perspectives attempting to explain neighbourhood crime (Cochran et al., 2000; Sun et al., 2004; Taylor and Covington, 1993). While originally focusing on how the destruction of community social organization can ultimately lead to crime and delinquency, it now encompasses fear of crime (Bursik, 1988; Taylor and Covington, 1993). Bursik (1988) defines social disorganization as ‘the inability of local communities to realise the common values of their residents or solve community experienced problems’. Sampson and Groves (1989) amend this description to include the concept of social control,17 defining social disorganization as ‘the inability of a neighbourhood to achieve the common goals of its residents and maintain effective social controls’. Social disorganization hypothesis is therefore dependent upon a community having common values and social norms. The inherent proposition underlying these definitions is that community structures affect a community’s ability to maintain public order, constrain residents from violating social norms and therefore fend off crime and fear (Markowitz et al., 2001; Taylor and Covington, 1993). Social disorganization theory proposes that the destruction of community social organizations ultimately leads to crime and delinquency (Bursik, 1988; Taylor and Covington, 1993). Early work focused on processes of urbanization that led to the erosion of the informal social controls that had governed traditional rural communities of the United States (Taylor and Covington, 1993). Heterogeneity and rapid population turnover seemingly undermined the social ties between neighbours, ‘limiting their ability to agree on common sets of values or to solve commonly experienced problems’ (Bursik, 1988). In turn this prevented residents from organizing collectively against those groups migrating into their neighbourhoods and prevented them from adequately controlling public antisocial behaviour, particularly that of new immigrants (Bursik, 1988; Taylor and Covington, 1993). A breakdown in familial controls and the presence of unsupervised youth groups within a neighbourhood were also central to the social disorganization theory (Taylor and Covington, 1993). The urban settings for social disorganization research were subject to rapid urbanization following an influx of immigrants from rural United States and Europe (Taylor and Covington, 1993). These immigrants were believed to have been ‘ill-equipped to supervise children assimilating the values of urban United States’. Due to the high population turnover and concern about the values of others within the neighbourhood, local adults were reportedly hesitant to reprimand youths participating in deviant activities (Taylor and Covington, 1993). Social mistrust also caused local adults to withdraw from nonconforming families, anticipating opposition and anxious that retaliation may result should they attempt to reform and informally control delinquent behaviour (Maccoby et al., 1958 in Taylor and Covington, 1993). In the event that residents did not exercise order, it was feared

17 Social control refers to the ‘capacity of the society to regulate itself according to the desired principles and values’ or the ‘ability of social groups or institutions to make norms all rules effective’ (Janowitz, 1975 and Reiss, 1951 cited respectively in Sampson, 1986).

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that the youth would continue their delinquencies, which would eventually escalate in severity (Wilson and Kelling, 1982; Taylor and Covington, 1993). Thus, ineffective parental supervision of youths, a traditional means of informal control, is an essential tenet of social disorganization theory (Taylor and Covington, 1993). Similar processes of social disorganization have also been put forth as mechanisms that lead to fear of crime. In 1978, Hunter proposed that social disorganization produces signs of physical and social incivility (Taylor and Covington, 1993). These incivilities, such as the presence of unsupervised youth in the streets, are negatively interpreted by residents as alluding to a lack of social control in the neighbourhood (Taylor and Covington, 1993). The idea of social disorganization has been supported in various longitudinal studies (Bursik and Webb, 1982; Markowitz et al., 2001; Sun et al., 2004; Taylor and Covington, 1993). These, and other cross-sectional studies, generally suggest that changes in racial composition are most strongly associated with disorder (Taylor and Covington, 1993). However, Sampson and Groves (1989) argue this research has failed to measure any mediating variables and therefore cannot be used to support the hypothesis. They proposed a model that has been hailed as ‘the most complete examination of the systemic social disorganisation model’ (Bursik and Grasmick, 1993). Sampson and Grove’s (1989) model predicted that neighbourhoods with low socio-economic status, high residential mobility, racial heterogeneity and family disruption would have disrupted local social organizations (Sun et al., 2004). Social disorganization was characterized by weak local friendship networks, low organizational participation and unsupervised youth groups. Sampson and Groves then predicted that these characteristics limit the capacity residents have to control behaviour, which in turn leads to increased neighbourhood crime and delinquency. In testing their model, Sampson and Groves confirmed crime rates were higher in areas of social disorganization, and that the aforementioned characteristics mediated the effect of ethnic heterogeneity, population turnover and social class on crime rates (Markowitz et al., 2001; Sun et al., 2004). However, social disorganization theory encountered some inevitable criticism – the theoretical concept has been rebuked as being poorly defined, and failing to distinguish between the causes and consequences of social disorganization (Markowitz et al., 2001; Sun et al., 2004; Taylor and Covington, 1993). This combined with the longitudinal component of the theory and problems of empirical testing saw a decline in its prevalence among the literature until the mid-1980s (Markowitz et al., 2001; Sun et al., 2004). In an attempt to counter criticisms of this nature, Bursik (1988) more succinctly defined social disorganization as ‘the inability of local communities to realise the common values of their residents or solve community experienced problems’ (Lane and Meeker, 2003b). Sampson and Groves (1989) later amended this description slightly to include the concept of social control, defining social disorganization as ‘the inability of a neighbourhood to achieve the common goals of its residents and maintain effective social controls’ (Markowitz et al., 2001; Sun et al., 2004; Taylor and Covington, 1993). Social disorganization theory is therefore dependent upon a community having common or dominant values and social norms. The inherent proposition underlying both of these definitions is that it is community structure that affects the ability of a neighbourhood to

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maintain public order, constrain its residents from violating such social norms and therefore fend off crime and fear (Markowitz et al., 2001; Taylor and Covington, 1993). Despite criticisms, the prominence of the social disorganization hypothesis means that it should be acknowledged. Furthermore, the presence of the hypothesis indicates fear-of-crime survey questions should be developed to target fear, so results are not confused with concern about crime or social disorganization. This would save confusion when interpreting research findings. The same conclusion can be made from the following discussion of the related subcultural diversity, social integration, community concern and social change hypotheses. Subcultural Diversity Hypothesis The subcultural diversity hypothesis proposes that fear of crime develops when residents live in close proximity to individuals of differing cultural backgrounds. This was presented by Merry (1981) who theorises that the behaviours of those who are racially, ethnically and culturally different are difficult to interpret (cited in Lane and Meeker, 2003b). When residents cannot understand different behaviours, they become uncertain about and mistrust these ‘others’. The residents believe the ‘others’ have different social values, attitudes and community commitment (Covington and Taylor, 1991; Lane and Meeker, 2003b). In the longer term, they are consequently perceived as being dangerous and fear of crime results (Lane and Meeker, 2003b).18 Numerous studies support the hypothesis, finding racial diversity is related to increased fear of crime (Chiricos et al., 1997; Covington and Taylor, 1991; Lane and Meeker, 2003b; Taylor and Covington, 1993).19 In opposition to the subcultural diversity hypothesis,20 Chiricos et al. (1997) found that racial composition has no consequence on fear of crime when other relevant factors are controlled. With the subcultural diversity hypothesis, fear of crime can be thought of as an ‘anxiety endangered through the confrontation of difference’ (Ditton et al., 2000).21 This further emphasizes the need for fear-of-crime survey questions to focus on fear of a specific crime, in order to minimize the potential for confusion with anxiety related to diversity.

18 This is considered particularly pertinent in communities with poor social networks (Lane and Meeker, 2003b). 19 Katz et al. (2003) note that the majority of research supporting the subcultural diversity theory use ethnicity or racial heterogeneity to measure cultural background (Katz et al., 2003). They argue these measures are less relevant to subcultural diversity than to conflict theory. While similar, conflict theory proposes ‘the greater the presence of minority populations, the more threatened majority group members will feel’ (Blalock, 1967; Katz et al., 2003). 20 As with any of the explanatory theories, the effect of subcultural diversity may also be dependent on the measure of fear used. For example, Thompson et al. (1992) found that perceived safety was related to racial composition, while fear of being the victim of specific crimes was not. 21 A variation of the subcultural diversity theory posits that flux in subcultural diversity, as opposed to static subcultural diversity, causes residents to perceive their neighbourhood as in a state of disorder and decline (Lane and Meeker, 2003b).

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Social Integration/Neighbourhood Cohesion Hypotheses The social integration hypothesis, also known as the neighbourhood cohesion hypothesis, proposes that poor social integration in a community leads to increased fear of crime (Crank et al., 2003). Social integration can be considered as ‘the capacity of the community to exert social control over its members and passersby, thereby enforcing local versions of right and seemly conduct’ (Janowitz, 1978 cited in Skogan and Maxfield, 1981). The social integration hypothesis depends upon additional concepts of social support, social capital and collective efficacy. Like many sociological terms these concepts are multifaceted and arguably ill-defined. The main descriptions are covered here. Social support refers to the frequency of contact residents have with one another, the amount of help they provide to one another and how satisfied they are with that support (Thompson and Krause, 1998). Social capital relates to social contact through community networks or associations and resident feelings of trust in one another, while collective efficacy concerns the level of cohesion between residents and their willingness to intervene on behalf of the common good (Lindstrom et al., 2003). A number of researchers find that low levels of social integration, social support, social capital and collective efficacy lead to fear of crime (Bellair, 1997; Markowitz et al., 2001). In contrast, Gibson et al. (2002) state it is ‘challenging to derive any definitive conclusions of the effects of social integration on fear of crime’.22 Community Concern Hypothesis The community concern hypothesis draws upon the disorder/incivilities and disorder and decline hypotheses, discussed shortly. The community concern hypothesis implies that fear of crime represents the opinion that one’s community is in a state of decline (Lane and Meeker, 2003b). People become concerned about the vitality, viability and quality of their neighbourhood when they encounter signs of physical and social decay (Taylor and Hale, 1986). They consequently worry that the problems present in their community may worsen and that their community, as a whole, is in a state of decline (Taylor and Hale, 1986). Residents become concerned that their neighbourhood is less safe than it was in the past and consequently feel afraid of crime (Covington and Taylor, 1991; Lane and Meeker, 2003b). The community concern theory also concludes that fear of crime is intensified when local social ties are weak (Conklin, 1971; Covington and Taylor, 1991; Garofalo and Laub, 1978 in Lane and Meeker, 2003b). Thus the theory is also related to notions of social integration. This temporal component of the community concern hypothesis lends the hypothesis its secondary title, known as the decline model (Lane and Meeker, 2003b). Researchers such as Taylor and Hale (1986) support the community concern hypothesis, finding that concern predicts fear of crime.

22 However, in comparing such studies it is important to consider the varying operationlizations of the concepts inherent in the theory and how they are measured (Crank et al., 2003)

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Social Change Hypothesis Furstenberg (1971) first put forward the social change model, and suggested that people most disturbed by social change are more concerned about crime (Furstenberg, 1971). According to the hypothesis, fear of crime eventuates when people resent the processes and features of social change, particularly those that denote adjustments to the prevailing status quo (Furstenberg, 1971). These social changes include diversifying racial heterogeneity, a declining economic base, increasing unemployment and high population turnover (Clark, 2003; Furstenberg, 1971). This could accompany shifts in the environment that may disrupt the identification of people and places that are perceived to be risky, which generates more anxiety (Skogan and Maxfield, 1981). Fear of crime therefore becomes a metaphor for resentment and anxiety following social change (Clark, 2003; Pantazis, 2000).23 Possibly due to the longitudinal nature of this hypothesis, few studies have tested the social change model. While Hunter et al. (2002) have lent some support for the model,24 Clark (2003) disputes that such research has only maintained the concept of fear of crime as an anxiety response to rapid change. Instead drawing upon Lotz’s (1979) study, Clark (2003) proposes that it is concern about crime, rather than fear, that correlates with rapid change.25

Review: Social Studies Emphasize the Inherent Complexity of ‘Fear’ of ‘Crime’ The social theories draw attention to how the social fabric of the environment can lead to fear of crime. According to the social theories, fear of crime can reflect • feelings of insecurity or uncertainty; • concerns about the destruction of community social organization; • fear of the unknown and the different; • concerns about poor social integration;

23 Taylor further proposes that fear of crime is provoked by ‘different types of modern risk’, a conclusion very similar to those made by risk society theorists (Hollway and Jefferson, 1997). This supports the concept that fear is more akin to a general sense of anxiety (Clark, 2003). 24 Hunter et al. (2002) found that fear of crime increased during immigrant boom periods. Smith et al. (2001) found that during a period of population growth residents are more likely to view the social context as ‘unpredictable and potentially risky in regard to their perceptions about personal safety from criminal victimisation’ (cited in Hunter et al., 2002). 25 Similarly, Lemert (1951) and others have suggested that changes in conditions, rather than the current level of neighbourhood problems, are the most significant bellwether of fear (Skogan and Maxfield, 1981).

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• concerns about communities being in a state of decline; • concerns arising from rapid social changes. By suggesting that ‘fear of crime’ is not always a ‘fear’ of ‘crime’, the social theories emphasize the complexity underlying fear of crime and the importance of specifically targeting ‘fear’ of ‘crime’ in survey questions.

Environmental Theories Explaining Fear of Crime Environmental theories focus on cues in the external environment that trigger fear of crime. This set of theories is particularly relevant to strategies targeting fear of crime, as they seek to identify factors in the environment that can be altered to potentially reduce fear. The first of the environmental theories is the disorder/incivilities hypothesis, which pioneered such research. The subsequent theories include the threatening and safe environments theories and the signal crimes perspective.

The Disorder/Incivilities Hypothesis The disorder or incivilities hypothesis advises that there is a positive relationship between fear of crime and people’s perceptions of the social and physical characteristics of an environment (Crank et al., 2003; Millie and Herrington, 2005; Nasar et al., 1993; Tulloch, 2000). In particular it is signs of disorder or visible cues in an environment that signify a lack of order and trigger fear of crime (Ross and Mirowsky, 1999). According to Wilson (1968), disorder and incivilities are violations of ‘standards of right and seemly conduct’. Originally, fear of crime studies were primarily concerned with criminal acts and actual infractions of law (Phillips and Smith, 2003). However, the disorder/incivilities hypothesis draws attention to activities and objects that violate norms without violating the law (Ross and Mirowsky, 1999). Numerous studies reveal that the signs of disorder at the forefront of the public’s mind are those that are not legally criminal acts (Phillips and Smith, 2003; Stephens, 1999). More often they include lower-level breaches of community standards or ‘soft’ crimes that are frequently encountered in everyday life (Carvalho and Lewis, 2003; Millie and Herrington, 2005; Phillips and Smith, 2003; Skogan, 1990). Incivilities/disorder theorists (e.g. Nasar et al., 1993) argue that incivilities generate fear of crime in areas where there is no real criminal activity. Incivilities generate fear because they are perceived to be warning signs of underlying crime and criminal threat (Mirrlees-Black and Allen, 1998; Tulloch, 2000). They indicate a breakdown in social norms of behaviour and community relinquishment of both formal and informal social controls and support systems (Perkins and Taylor, 1996; Nasar and Jones, 1997; Rountree and Land, 1996; Tulloch, 2000).

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Disorder highlights the inability of community members to mobilize resources and deal with problems such as crime (Skogan, 1990; Taylor, 1999). This also includes the inability, or neglect, among officers of the state and external agencies to cope with crime (Perkins and Taylor, 1996). An impression of neighbourhood level vulnerability to crime is generated, which translates into fear (Painter, 1996; Nasar and Jones, 1997; Rountree and Land, 1996; Taylor and Hale, 1986). Further, incivilities act as warning signals of impending danger because they are associated with things people fear (Skogan and Maxfield, 1981). Thus, the presence of disorder creates increased perceptions of criminogenic risk (Crank et al., 2003).26 The disorder/incivilities hypothesis assumes that these incivilities are interpreted similarly regardless of the particular situation or local context (Taylor and Gottfredson, 1986). An encounter with disorder can either be ‘direct’ or ‘less targeted’ (Phillips and Smith, 2003). A ‘direct’ encounter refers to those situations whereby an individual is the direct target of an intentional act of deviance. A ‘less targeted’ encounter occurs when an individual observes or hears about an intentional action directed at another person or group of people (Phillips and Smith, 2003). Signs of disorder can also be encountered after the act. This is more often the case with signs of physical disorder. Hunter (1978) and Lewis and Maxfield (1980) identified disorder as being both ‘social’ and ‘physical’ in nature (Robinson et al., 2003). ‘Incivilities’ is an allencompassing label, which characterizes these disorders (Mirrlees-Black and Allen, 1998; Ross and Mirowsky, 1999). Social incivilities encompass disorder that involves people and their behaviours (Ross and Mirowsky, 1999; Skogan, 1999). Social disorder denotes people violating social norms or official laws, or acting in an unpredictable and threatening manner (Painter, 1996; Perkins and Taylor, 1996; Ross and Mirowsky, 1999; Skogan, 1999), including drug users, sex workers, beggars, gangs and people perceived to be behaving violently (Ferraro, 1995; Painter, 1996; Perkins and Taylor, 1996; Ross and Mirowsky, 1999; Skogan and Maxfield, 1981; Tulloch, 2000). Physical disorder refers to a neighbourhood’s overall physical appearance and signs of negligence or unchecked decay (Ross and Mirowsky, 1999; Skogan, 1999). They can also be the by-products of social disorder that has not been managed or taken care of by the community over time. Physical disorder includes abandoned buildings, graffiti, damaged property and broken streetlights (Doeksen, 1997; Painter, 1996; Ross and Mirowsky, 1999; Skogan, 1999). While not legally defined crimes, both social and physical signs of disorder trigger fear of crime. Likewise, so do the threatening environments.

26 However, some researchers state it is not merely the presence of incivilities that triggers fear of crime, but rather a change in the presence of incivilities, which is accompanied by changing community satisfaction and changing perceptions of relative risk, that triggers fear of crime (Robinson et al., 2003; Taylor and Gottfredson, 1986).

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Threatening and Safe Environments Theories Although similar to the disorder/incivilities hypothesis and signal crimes hypothesis, which link fear of crime with certain cues in the environment, the threatening environments hypothesis does not reflect a breakdown in social control. Threatening environments instead provide an all-encompassing label for those objects and acts that generate fear of crime, but these cues are not limited solely to disorder. However, like disorder, threatening environments can be either physical or social in nature. Threatening physical environments are a manifestation of urban planning or lack thereof. While signs of disorder are not necessarily present in threatening environments, they may generate fear because they are perceived as attractive sites for criminal victimization. Threatening physical environments commonly have characteristics that prohibit natural surveillance. Some researchers refer to this as ‘a lack of prospect’, ‘blocked prospect’ or ‘concealment’ (e.g. Fisher and Nasar, 1995; Nasar et al., 1993). For example they may have poor street lighting and barriers that prevent visibility to others, thereby creating hiding spots for offenders (DTUPA, 2002; Painter, 1996). These barriers include the presence of alcoves, too many bushes and overgrown vegetation (Cozens, 2002; Kuo and Sullivan, 2001; Newman, 1972; Fisher and Nasar, 1995). Similarly, threatening physical environments may have entrapment spots, which block the escape avenues of victims (DTUPA, 2002; Fisher and Nasar, 1995). There is a second characteristic, independent of urban planning, that can affect whether an environment is considered threatening. The literature indicates that fear of crime is influenced by time of day (Nasar and Jones, 1997). Researchers agree that people have increased fear after dark (Brantingham and Brantingham, 1993; Cubbage and Smith, 2009; Doran and Lees, 2005; Fisher and Nasar, 1995; Painter, 1996; Samuels and Judd, 2002). The reduction in visibility and recognition abilities and the creation of blind spots, shadows and potential places of entrapment play a role in the physical environment (Painter, 1996). The change in the social character of environments during the night is also likely to be an influencing factor (Koskela, 1999). Threatening social environments may also generate fear while not representing disorder. For example, an absence of pedestrian activity and the notion of a lack of natural surveillance or ‘eyes on the street’ induce fear (Jacobs, 1961; Samuels and Judd, 2002). This is partly based on Jacobs’s (1961) premise that criminals do not want to be observed, as it increases their risk of being reported and apprehended. Social surveillance increases the perceived risk of detection for offenders, prompting them to participate in criminal activity in less populated areas (Jacobs, 1961). In line with this, there is the perception that unaccompanied individuals are more attractive targets for victimization (e.g. Painter, 1996). A lack of social surveillance could also increase a potential victim’s fear of crime for two more reasons. First, there is a lack of potential witnesses who could seek help from police or other authorities (Jacobs, 1961; Samuels and Judd, 2002) and second, there is a lack of capable guardians who could help resist an attack (Painter, 1996). Conversely,

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social surveillance arguably reduces fear of crime, as can the other environmental factors discussed below (Doeksen, 1997; Loukaitou-Sideris, 1999). Safe environments, the opposite of threatening environments, could potentially help mitigate fear of crime. Safe environments theoretically lack those environmental cues that trigger the public’s fear of crime, for example areas to hide and signs of social or physical disorder. They would also contain other environmental cues that reinforce perceptions of safety. Very little information in the fear of crime literature has been gathered on such ‘safe cues’ or ‘control signals’. Nasar (1998) discusses cues he labels as ‘likeable features’, which could trigger people to feel safe. These include signs of ‘naturalness’ (for example vegetation and mountains), ‘upkeep/civilities’ (well-maintained areas), ‘openness’ (open spaces and scenery), ‘historical significance’ (features with a historical feel) and ‘order’ (organization and compatibility of features) (Nasar, 1998). Cozens (2002) additionally suggested that ‘upkeep/civilities’ and ‘order’ can decrease fear of crime. Vegetation, despite potentially being a source of fear when causing concealment and areas to hide, has also been found to reduce fear of crime in some studies (Kuo and Sullivan, 2001). Appleton (1975) proposes that the public is more inclined to feel safe in environments that have adequate prospect to create opportunities for surveillance (Yokohari et al., 2006). In similar vein the signal crimes perspective, discussed below, emphasizes the presence of ‘control signals’ in an environment (Innes et al., 2002; Millie and Herrington, 2005). Control signals are defined as ‘acts of social control that communicate a message to the public’ (Innes, 2004a). Police and town planners generally put such signals in place in an attempt to reassure the community and they have a positive effect by reducing perceptions of criminogenic risk (Innes et al., 2002; Millie and Herrington, 2005). While logical, there is the potential for control signals to inadvertently have a negative impact upon public perceptions of security (Innes, 2004a). For example, the presence of closed-circuit television (CCTV) cameras, which may in part be erected to reduce fear of crime, could simultaneously denote the presence of an unsafe element to some sectors of society.

Signal Crimes Perspective The signal crimes perspective, put forward by Innes et al. (2002), refines some of the generalizations inherent in the disorder/incivilities hypothesis. It draws on social semiotics and symbolic interactionist sociology to illustrate how the wider social character of the environment shapes the way crime and disorder are interpreted and rendered meaningful. The signal crimes perspective argues that different crimes and disorders have a disproportionate impact on how people interpret them, and the extent to which they connote criminogenic risk. It also recognizes that although community members may share common values, different individuals and groups vary in the way they interpret crime and disorder (Innes, 2004a; Innes et al., 2002).

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A brief theoretical background in semiotics and signs is necessary for the understanding of signal crimes. Semiotics theory advises that signs are objects27 or acts that mean something to someone in a context (Innes, 2004b). Social semiotics examines signs in light of how their meaning in different cultural and situational contexts will vary. Signs are composed of two components, the first being the ‘expression’ and denotative description (Innes, 2004a). The second component is the ‘content’ and connotative description. According to Eco (1976), signals are defined as signs that have an effect. The effect of a signal can be ‘affective’ (changing how the onlooker feels), ‘cognitive’ (changing how the onlooker thinks), ‘behavioural’ (changing how the onlooker acts) or a mixture of each (Innes, 2004a). All signals therefore have an expression, content and effect, which in combination, act to establish meaning and differentiate signals from other signs (Innes, 2004a). The signal crimes perspective differentiates ‘signal crimes’ and ‘signal disorders’. With regard to expression, ‘signal crimes’ encompass those signals that denote criminal incidents, for example a mugging. The content is that they indicate the presence of criminogenic risk. In this example it is the risk of mugging (Innes, 2004a). ‘Signal disorders’ follow on from the disorder/incivilities hypothesis. In semiotics terms, while not directly denoting a legally criminal incident, signal disorders28 also connote criminogenic risk (Innes, 2004a). Instead of supposing that all crimes and disorders generically lead to fear of crime, as with some disorder/incivilities theorists and the positivist view of crime, the signal crimes perspective focuses on how and why different signal crimes have a different effect, despite having the same content (Innes et al., 2002). Innes et al. (2002) refer to Slovic’s (1992) hypothesis that proposed different risks have different ‘signal values’. The signal value refers to the extent, strong or weak, a signal crime shapes one’s perception of risk. Strong signal crimes are those acts or objects that are serious enough to generate a ‘significant degree of public awareness’ (Innes et al., 2002). Weak signal crimes do not generate such perceptions of criminogenic risk, when encountered in isolation. However, an accumulative impact occurs when numerous weak signals are encountered in succession or combination (either temporally or spatially). They are then interpreted as a strong signal (Innes, 2004a; Innes et al., 2002). Another addition to the disorder/incivilities hypothesis is the situational relevance of signal crimes. The signal crimes perspective contends that identical objects and acts may be signal crimes in one environment and not another (Innes, 2004a). The content and effect of a signal crime is highly contextualized and situational (Innes et al., 2002). Therefore one’s interpretation of a signal crime is sensitive to characteristics of the social and physical environment in which it is located

27 An object is anything that can be indicated, everything that is pointed to or referred to (Blumer, 1969). 28 As discussed in the previous section, disorders can either be social or physical in their denotative expression.

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(Innes et al., 2002). Innes et al. (2002) use the example that graffiti in a neighbourhood with good social control might act as a signal crime because of its high ‘dissonance’ value, whereas it might go unnoticed in a neighbourhood with the presence of more serious crime and disorder. The signal crimes perspective essentially acknowledges that the disorder/incivilities hypothesis has merit in that that certain signal crimes and signal disorders are thought to be common throughout a community. Innes (2004a) draws on symbolic interactionist sociology to highlight the role of social reactions in defining deviant acts (Innes, 2004a).29 Slovic (1992) reasons that people do not define risk purely on the basis of the signal crime itself, but according to its nature and one’s personal context (Innes et al., 2002). Risk is dependent upon surrounding belief systems, such as those governing acceptable social norms (Innes et al., 2002). If community members share common social norms, then signal crimes may be commonly interpreted. However, the signal crimes perspective recognizes that there is not necessarily a consensus between community members on which acts or objects are considered signal crimes (Innes et al., 2002). Nor is it assumed that common signal crimes are interpreted in the same manner, to the same extent or have the same effect (Innes et al., 2002; Innes, 2004a).30 Signal crimes are interpreted in light of an individual’s past experiences with similar objects, personal values and concerns (Innes, 2004a). An assessment of the situation and prediction about the likelihood of future risks then takes place (Innes, 2004a). Consequently, a particular personal reaction to the signal crime occurs (Innes et al., 2002). Thus, the signal crimes perspective recognizes that individuals vary in the way they interpret and render meaningful signs of disorder. Similarly, different signal crimes vary in their effect on people. As mentioned above, there are a variety of cognitive, affective and behavioural reactions people can exercise after encountering a signal crime. By their definition, signal crimes always induce a cognitive and affective reaction, adversely altering criminogenic risk perceptions and causing feelings of heightened fear and anxiety (Innes et al., 2002). Subsequently the affected people may also adopt a behavioural change in order to protect themselves from victimization (Innes et al., 2002).

29 Symbolic interactionism is a label for an ‘approach to the study of human group life and human conduct’ (Blumer, 1969). Symbolic interactionism contends the meaning of objects and things is derived from the social interaction one has with one’s fellows (Blumer, 1969). 30 This is relevant to different individuals and socio-demographic groups. For example factors such as age, gender and experience of previous victimization may shape how certain signal crimes are interpreted and rendered meaningful (Innes, 2004a).

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Review: Intuitive Environmental Studies into Cues Triggering Fear of Crime Environmental theories propose that signs of disorder (also known as incivilities or signal crimes/disorders) and other stimuli in threatening environments can trigger fear of crime. While environmental theories are well established, different components of the theories have not been fully examined. New research could specifically determine what environmental cues trigger fear of crime in different environments. These studies could, for example, pay attention to potential differences in the content, effect or signal value of different environmental cues in different situational contexts.

Chapter Review: An Opening for Pertinent Environmental Studies Criminal opportunity and risk of victimization theories argue that crime is the primary cause of fear of crime. Drawing on the literature, it is evident that while crime certainly does lead to fear of crime, there is also evidence that fear of crime can occur in areas characterized by low crime rates. Therefore, research into the other factors associated with fear of crime is justified. An extensive body of research has tested demographic theories by examining the potential associations between fear of crime and victimization, indirect victimization and personal feelings of vulnerability. The findings from such research are frequently contested and it is unlikely further studies into these associations will provide new information or substantially progress the fear of crime research field. Similarly, numerous studies have examined the various social theories that propose fear of crime is caused by, and actually represents, risk society feelings or concern about social disorganization. While there may be a set of relationships that can be explored, general feelings of uncertainty or concern cannot substitute fear of crime. Consequently fear of crime studies should use survey questions that minimize the likelihood of producing results that could represent fear of crime as something other than ‘fear’ of ‘crime’. There is clear evidence that environmental cues, for example signs of disorder and other stimuli in threatening environments, can trigger fear of crime. Despite the fact that several studies have investigated the link between fear of crime and environmental cues, it appears there is room for more research into environmental theories and the associated behavioural responses that individuals adopt in relation to perceptions of risk.

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McCoy, V. H., J. D. Wooldredge, et al. (1996). “Lifestyles of the old and not so fearful: life situation and older persons fear of crime”. Journal of criminal justice 24(3): 191–205. Mesch, G. S. (2000). “Perceptions of risk, lifestyle activities, and fear of crime”. Deviant Behavior 21(1): 47–62. Miceli, R., M. Roccato, et al. (2004). “Fear of crime in Italy – spread and determinants”. Environment and Behavior 36(6): 776–789. Miethe, T. D. (1990). “Opportunity, choice, and criminal victimization – a test of a theoreticalmodel.” Journal of Research in Crime and Delinquency 27(3): 243–266. Millie, A. and V. Herrington (2005). “Bridging the gap: understanding reassurance policing”. The Howard Journal of Criminal Justice 44(1): 41. Mirrlees-Black, C. and J. Allen (1998). Concern about crime: Findings from the 1998 British Crime Survey. Research Findings No 83. London, Home Office Research, Development and Statistics Directorate. Nasar, J. L. (1998). The evaluative image of the city. California, Sage. Nasar, J. L., B. Fisher, et al. (1993). “Proximate physical cues to fear of crime”. Landscape and Urban Planning 26: 161–178. Nasar, J. L. and K. M. Jones. (1997). “Landscapes of fear and stress.” Environment and Behavior 29(3): 291–323. Nelson, A., R. Bromley, et al. (2001). “Identifying micro-spatial and temporal patterns of violent crime and disorder in a British city centre”. Applied Geography 21: 249–274. Newman, O. (1972). Defensible space: crime prevention through urban design. New York, NY, Macmillan. Ortega, S. T. (1987). “Race and gender effects on fear of crime: an interactive model with age”. Criminology 25(1): 133. Pain, R. (2000). “Place, social relations and the fear of crime: a review.” Progress in Human Geography 24(3): 365–387. Painter, K. (1996). “The influence of street lighting improvements on crime, fear and pedestrian street use, after dark”. Landscape and Urban Planning 35(2–3): 193–201. Pantazis, C. (2000). “‘Fear of Crime’, vulnerability and poverty”. The British Journal of Criminology 40(3): 414–436. Perkins, D. D. and R. B. Taylor (1996). “Ecological assessments of community disorder: their relationship to fear of crime and theoretical implications”. American Journal of Community Psychology 24(1): 63–107. Perloff, L. S. (1983). “Perceptions of vulnerability to victimization”. Journal of Social Issues 39(2): 41–61. Phillips, T. and P. Smith (2003). “Everyday incivility: towards a benchmark”. Sociological Review 51(1): 85–108. Rader, N. E. (2004). “The threat of victimization: a theoretical reconceptualization of fear of crime”. Sociological Spectrum 24(6): 689–704. Robinson, J. B., B. A. Lawton, et al. (2003). “Multilevel longitudinal impacts of incivilities: fear of crime, expected safety, and block satisfaction”. Journal of Quantitative Criminology 19(3): 237–274. Romer, D., K. H. Jamieson, et al. (2003). “Television news and the cultivation of fear of crime”. Journal of Communication 53(1): 88–104. Rose, N. (2000). Government and control. Criminology and social theory. D. Garland and R. Sparks (Eds.). Oxford University Press, Oxford. Ross, C. E. and J. Mirowsky (1999). “Disorder and decay: the concept and measurement of perceived neighborhood disorder”. Urban Affairs Review 34(3): 412–433. Rountree, P. W. and K. C. Land (1996). “Perceived risk versus fear of crime: empirical evidence of conceptually distinct reactions in survey data”. Social Forces 74(4): 1353–1377. Sacco, V. F. and W. Glackman (1987). Vulnerability, locus of control, and worry about crime. The fear of crime. J. Ditton and S. Farrall (Eds.). Ashgate, Aldershot: 415–428.

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Sampson, R. J. (1986). Crime in cities: the effects of formal and informal social control. Communities and crime. A. J. Reiss and M. Tonry (Eds.). University of Chicago press, Chicago: 271–312. Sampson, R. and B. Groves (1989). “Community structure and crime: testing social disorganisation theory”. American Journal of sociology 94: 774–802. Samuels, R. and B. Judd (2002). Public housing estate renewal: Interventions and the epidemiology of victimisation. Housing, Crime and Stronger Communities Conference, Melbourne, Australian Institute of Criminology & Australian Housing and Urban Research Institute. Skogan, W. G. (1990). Disorder and decline: crime and the spiral decay in American neighbourhoods. Los Angeles, CA, University of California Press. Skogan, W. G. (1999). Measuring what matters: Crime, disorder and fear. Measuring what matters: proceedings from the police research institute meetings. R. H. Langworthy (Ed.). National Institute of Justice, Washington, DC. Skogan, W. G. and M. G. Maxfield (1981). Coping with crime : individual and neighborhood reactions. Beverly Hills, CA, Sage Publications. Smith, S. J. (1987). “Fear of crime: beyond a geography of deviance”. Progress in Human Geography 11: 1–23. Smith, L. N. and G. D. Hill (1991).“Victimisation and fear of crime.” Criminal Justice and Behaviour 18(2): 217–239. Sparks, R. (1992). Television and the drama of crime: moral tales and the place of crime in public life. Buckinghamshire, Open University Press. Stanko, E. A. (2000). Victims R us: the life history of fear of crime and the politicisation of violence. Crime, risk and insecurity. T. Hope and R. Sparks (Eds.). Routledge, London. Stephens, D. W. (1999). Measuring what matters. Measuring what matters: Proceedings from the Police Research Institute meetings. R. H. Langworthy, National Institute of Justice; Office of Community Oriented & Policing Services. Stinchcombe, A. L. (1978). Theoretical models in social history. New York, NY, Academic Press. Sun, I. Y., R. Triplett, et al. (2004). “Neighbourhood characteristics and crime: a test of Sampson and Groves’ model of social disorganisation”. Western Criminology Review 5(1). Taylor, R. B. (1999). The incivilities thesis: Theory, measurement, and policy. Measuring what matters: Proceedings from the Police Research Institute meetings. R. H. Langworthy, National Institute of Justice; Office of Community Oriented & Policing Services. Taylor, R. B. and J. Covington (1993). “Community structural change and fear of crime”. Social Problems 40(3): 374–395. Taylor, R. B. and S. D. Gottfredson (1986). Environmental design, crime and prevention: an examination of community dynamics. Communities and crime. A. J. Reiss and M. Tonry (Eds.). University of Chicago Press, Chicago, IL: 387–416. Taylor, R. B. and M. Hale (1986). “Testing alternative models of fear of crime”. The Journal of Criminal Law and Criminology 77(1): 151–189. Thompson, E. E. and N. Krause (1998). “Living alone and neighborhood characteristics as predictors of social support in late life”. Journals of Gerontology Series B-Psychological Sciences and Social Sciences 53(6): S354–S364. Thompson, C. Y., W. B. Bankston, et al. (1992). “Parity and disparity among three measures of fear of crime: a research note.” Deviant Behavior 13: 373–389. Totaro, P. (1988). Sydney plays it safe by staying at home. The Sydney Morning Herald. Sydney. Tulloch, J. (1998). Quantitative review. Fear of crime. J. Tulloch, D. Lupton, W. Blood et al. (Eds.). National Campaign Against Violence and Crime (NCAVAC), Canberra. Tulloch, M. (2000). “The meaning of age differences in the fear of crime”. The British Journal of Criminology 40(3): 451–467. Vaughn, D. (2002). Signals and interpretive work: the role of culture in a theory of practical action. Culture in mind: toward a sociology of culture and cognition. K. Cerulo (Ed.). Routledge, New York, NY.

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Vold, G. B., T. J. Bernard, et al. (2002). Theoretical criminology. New York, NY, Oxford University Press. Walklate, S. (2000). Trust and the problem of community in the inner-city. Crime, risk and insecurity. T. Hope and R. Sparks (Eds.). Routledge, London. Walklate, S. (2003). Understanding criminology: current theoretical debates, second edition. Buckingham, Open University press. Warr, M. (2000). “Fear of crime in the United States: avenues for research and policy.” Criminal Justice 4: 452–489. Warr, M. and C. G. Ellison (2000). “Rethinking social reactions to crime: personal and altruistic fear in family households”. American Journal of Sociology 106(3): 551–578. Weitzer, R. and C. E. Kubrin (2004). “Breaking news: how local TV news and real-world conditions affect fear of crime”. Justice Quarterly 21(3): 497–520. Williams, P. and J. Dickinson. (1993). “Fear of crime: read all about it. The realtionship between newspaper crime reporting and fear of crime.” The British Journal of Criminology 33(1): 33– 56. Wilson, J. (1968). “The urban unease: community versus the city”. The Public Interest 12: 25–39. Wilson, J. Q. and G. L. Kelling (1982, March). “The police and neighbourhood safety: broken windows”. The Atlantic Monthly: 29–38. Wurff, A. V. D., P. Stringer, and F. Timmer. (1988). Feelings of unsafety in residential surroundings. Environmental social psychology. D. Canter, C. Jesuino, L. Soczka, and G. Stephenson (Eds.). Kluwer, The Hague: 135–148. Yokohari, M., M. Amemiya, et al. (2006). “The history and future directions of greenways in Japanese new towns”. Landscape and Urban Planning 76(1–2): 210–222.

Chapter 4

Managing Fear of Crime

Policing Fear of Crime Fear of crime and other non-criminal community problems are not typically considered in conventional policing models. Instead, policing is traditionally reactive and oriented towards crime incidents, requiring an offence before police act (Xu et al., 2005). Even so, the police often deal with disorder and fear of crime more than actual crime (Glensor and Peak, 1996). Hence, many policing models are increasingly focusing on a more in-depth understanding of non-criminal problems, including fear of crime (Ashby and Longley, 2005). Addressing fear of crime therefore features in many ‘problem-oriented’, ‘zero-tolerance’ and ‘community-oriented’ policing models. Problem-oriented policing was initially developed by Goldstein (1979) and employed in the early 1980s by policing practitioners, such as Wilson and Kelling (1982), the initiators of the broken windows hypothesis. Under this model, the police aim to proactively prevent crime, rather than react to incidents. They deal with non-criminal problems that concern or cause harm to the community, for example disorder and fear of crime (CPOP, 2003; Sims et al., 2002), and identify public concerns in order to carry out thoroughly planned responses to those concerns (CPOP, 2003; Lawton et al., 2005). This process is based on the SARA model (Scanning, Analysis, Response and Assessment) and often involves other public agencies and the private sector, with the community being identified as a potentially important policing partner in dealing with problems like fear of crime (Sims et al., 2002; CPOP, 2003). Problem-oriented policing incorporates a framework for situational crime prevention when acting on identified problems which, in turn, draws on the criminal opportunity and risk of victimization theories by aiming to increase the risks to potential offenders and reduce the rewards or benefits from criminal activity (CPOP, 2003). Therefore unlike standard policing models, problem-oriented policing is geographically focused and allows localized intervention (Lawton et al., 2005). Zero-tolerance policing, also known as order-maintenance policing or disorder policing, is widely discussed in the fear of crime literature (Harcourt, 1998). While grounded in problem-oriented policing, zero-tolerance policing does not focus on

B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_4,  C Springer Science+Business Media, LLC 2012

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police-community interaction. Zero-tolerance policing strategies draw on Wilson and Kelling’s (1982) broken windows hypothesis and Skogan’s (1990) disorder and decline hypothesis and attempts to combat disorder before it can lead to fear of crime and crime (Katz et al., 2003). It is based on the principle that police intervention in reducing disorder can also reverse those processes of neighbourhood decay and criminal activity (Crank et al., 2003; Katz et al., 2003; Novak et al., 1999). The New York Police Department’s (NYPD) zero-tolerance program of the 1990s is frequently cited as a successful example of this, however some critiques suggest the decline in New York’s crime levels were the result of other factors (e.g. Greene, 1999; Harcourt, 1998; Katz et al., 2003; Kelling and Coles, 1997). A case study on the well-known zero-tolerance policing strategy adopted by the NYPD is provided below to examine some of the issues surrounding the role of police–community partnerships in reducing the fear of crime. Stemming from problem-oriented policing, community-oriented policing or neighbourhood policing specifically promotes ‘community police partnerships, proactive problem-solving, and community engagement to address the causes of crime, fear of crime, and other community issues’ (Dietz, 1997). A police understanding of, and response to, public perceptions of crime and disorder is fostered (Baker and Wolfer, 2003; Dietz, 1997; Sims et al., 2002).1 Police empower and work with city agencies, businesses, service providers and the community at large to identify, prioritize and resolve citizen concerns (Adams et al., 2005; Glensor and Peak, 1996; Sims et al., 2002; Walklate, 2000). Surveillance activities like neighbourhood watch programmes are most common, whereby residents report any suspicious activity to the police. Such programmes are identified as helping reduce public fear of crime (Baker and Wolfer, 2003; Skogan and Maxfield, 1981; Tulloch, 1998). More intensive programmes include mobile citizen patrols, whereby community groups patrol the neighbourhood with the aim of interrupting criminal activities, apprehending offenders and making citizens arrests on behalf of the police (Baker and Wolfer, 2003; Kenney, 1987; Skogan and Maxfield, 1981). The dissemination of crime prevention information through newsletters and public meetings, which often involve the police, are also conventional (Kenney, 1987). Garofalo (1979) proposed that information about crime decreases fear of crime. According to this model, increased knowledge of local crime leads to an alteration of risk assessment, which then changes fear of crime levels. Skogan and Maxfield (1981) state that community-based initiatives which aim to reduce and prevent crime play a large role in independently helping to reduce fear of crime. For example, involvement in crime reduction initiatives potentially decreases fear of crime by reversing feelings of vulnerability, community concern and perceptions of social disorganization. Skogan (1986, 1990) further suggests

1 However despite this benefit, community-oriented policing is criticized as being a spatially generalist model that does not reflect local conditions (Bennett, 1991; Spelman, 2004).

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that participation also generates feelings of helpfulness, responsibility, territoriality and optimism, which may also reduce fear. Given this, the presence of disorder and crime may actually increase the well-being of a neighbourhood by encouraging preventative action and collective efficacy (Innes et al., 2002). Another aim of community-oriented policing is that if police and community appear to work cohesively then potential criminals could be deterred from behaving in an antisocial manner (Baker and Wolfer, 2003).2 Public cooperation with police and increased police visibility reduce public fear of crime (Adams et al., 2005; Dietz, 1997; Salmi et al., 2004).3 It is argued that such inter-agency approaches to reducing crime and fear of crime are most successful (e.g. Brown and Polk, 1996; Smith, 1987). Dixon (1995) goes so far as to argue that a safer community cannot be created by criminal justice agencies alone. Thus despite their limitations, proactive crime prevention components of these popular policing models do help to fight crime and fear of crime and have been adopted in many countries (Xu et al., 2005). Indeed, this is frequently reflected in the mission statements of police departments that have fear reduction as one of their primary objectives (e.g. Cordner, 1986; also see Table 4.1 below). In a review of the fear of crime in Australia, Grabosky (1995) argues that the general public view police services as the main government agency with the responsibility of managing the fear of crime. This statement seems to be generally applicable as others have expressed similar views regarding police services and appropriate public policy in relation to the fear of crime (Cordner, 1986; Bennett, 1991; Borooah and Carcach, 1997; Grabosky, 1995).The former NSW Police Commissioner, Ken Moroney, has even formally stated that fear of crime ‘is as debilitating as the crime itself’ (Cameron, 2002). As indicated above, fear of crime features in the primary mission statement of the New South Wales Police, being to provide ‘a safe NSW with a respected police force working with the community to reduce violence, crime and fear’ (NSW Police Force, 2011).

2 Reassurance policing emphasizes this notion even further by focusing on police visibility, familiarity and accessibility in an effort to thwart declining public confidence in the police (Povey, 2001 in Millie and Herrington, 2005). Reassurance policing places a strong emphasis on the reduction of disorder and fear of crime by focusing scarce police resources on the root causes of these issues (Millie and Herrington, 2005). 3 For example in terms of avoidance, Skogan and Hartnett (1997) found that residents in jurisdictions governed by community-oriented policing avoided fewer areas due to worrying about victimization than residents in non-COP neighbourhoods (Sims et al., 2002). Then again, Weisburd and Eck (2004) found that community-oriented policing only reduced fear of crime when implemented with models of problem-oriented policing.

Australia

Australia

New South Wales Police Force, Wollongong local area command Police Tasmania, Launceston

San Diego Police Department

United Kingdom United States

‘To serve the people of Tasmania by protecting life and property, enhancing community safety and reducing the incidence and fear of crime’ ‘Working with our communities to reduce crime, disorder and fear as the leading, caring and professional police service’ ‘We are committed to working together, within the department, in a problem solving partnership with communities, government agencies, private groups and individuals to fight crime and improve the quality of life for the people of San Diego’

United States

Los Angeles Police Department

Thames valley Police

‘The mission of the New York city police department is to enhance the quality of life in our city by working in partnership with the community and in accordance with constitutional rights to enforce the laws, preserve the peace, reduce fear, and provide for a safe environment’ ‘It is the mission of the Los Angeles police department to safeguard the lives and property of the people we serve, to reduce the incidence and fear of crime, and to enhance public safety while working with the diverse communities to improve their quality of life. Our mandate is to do so with honor and integrity, while at all times conducting ourselves with the highest ethical standards to maintain public confidence’ ‘To have police and the community working together to establish a safer environment by reducing violence, crime and fear’

United States

New York City Police Department

Mission statement

Country

Police department

City of San Diego (2011)

Milton Keynes Police (2011)

Glenorchy City Council (2011)

NSW Police Force (2011)

LAPD (2011)

NYPD (2011)

Source

Table 4.1 Examples of police services in western democracies having the reduction of the fear of crime as a primary objective

54 4 Managing Fear of Crime

Policing Fear of Crime

Case Study: The New York Police Department’s (NYPD) Policing Model Bratton (1995, 1996), who was NYPD Police Commissioner from 1994 to 1996, and Kelling and Coles (1997) describe the sequence of events that led to the NYPD’s adoption of a zero-tolerance policing model. During the 1970s, before the implementation of zero-tolerance policing, New York City mirrored the spiral of decay described in Wilson and Kelling’s (1982) broken windows thesis. Unchecked disorder was seen to be leading to more significant crime, disorder and widespread fear. The public began attempting to restore order themselves, which placed direct pressure on the NYPD and other official organizations to address order-related quality-of-life issues. NYPD responded with concerted efforts targeting fear of crime, graffiti, panhandling and homeless people in areas such as Bryant Park and Times Square and on the New York City subway. Order restoration continued to remain a priority throughout the 1980s and early 1990s. These zero-tolerance policing activities have been widely hailed for reducing the city’s crime levels (e.g. Bowling, 1999; Bratton, 1996; Greene, 1999). For example, the New York City Mayor’s Management Report (1998) lists reductions in felony crimes, increases in narcotics arrests and the continued policing of minor disorder as improvements in the quality of life for citizens and neighbourhoods. However, despite the emphasis on fear of crime prior to, and during, the implementation of zero-tolerance policing, debate over the success of the strategy has focused almost exclusively on crime (e.g. Bowling, 1999; Bratton, 1996; Greene, 1999). Bratton (1996) discusses the success of zero-tolerance policing in terms of the number of people arrested for qualityof-life crimes and states that fear was reduced following the aggressive control of disorder. Yet at no point does Bratton (1996) attempt to verify this claim with comparable data used for the section of his argument pertaining to crime. No research is cited which attempts to ascertain levels of fear and whether they have changed in relation to zero-tolerance policing. This suggests that fear of crime was used more for political leverage and not specifically as an issue to be dealt with, monitored and analyzed in the same focused manner as crime. It also means that an assessment of the effects of zero-tolerance policing on fear reduction has to be based on a wider discussion of police–community relations. Nevertheless, Bratton argued that police activities and police departments should expect to have an impact on crime, disorder and fear and that this should result in proactive tactics that focus on the problems that generate crime. This key assumption formed the basis of the four principles that continue to guide the patrol and investigative work of the NYPD: timely accurate

55

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intelligence, rapid deployment; effective tactics; and relentless follow-up and assessment. One of the arguments that Bratton (1996) uses to justify the zerotolerance approach is that it is based on community-oriented policing (COP). Bratton lists three elements he considers critical to the success of COP: partnership, problem solving and prevention. Partnership refers to the greater effectiveness of the police when they work with the community, not apart from it. Problem solving centres on the focus of police and the community to deal with crime and the signs of crime, while prevention is the logical outcome of the first two elements. Bratton claims that the dramatic drop in index crime in New York City in the 1990s is the result of COP that focuses on partnership, problem solving and prevention. However, others have criticized the style of policing adopted at the community and neighbourhood level. For example, Greene (1999) highlights that civil rights claims against the police for abusive conduct increased by 75% in the four years prior to 1999. Amnesty International (1996a, b) also raised concerns over the use of excessive force within the NYPD. They state that allegations of police brutality continued to rise between 1994 and 1996 while deaths in custody also rose substantially between 1993 and 1994. Harcourt (1998) criticizes New York style policing on the basis that it aims to watch, control, relocate and, ideally, exclude members of the community categorized as disorderly. In addition, those arrested for quality-of-life offences are burdened with a criminal record that may haunt them on future job and school applications. Such outcomes are at odds with the principles of partnership, problem solving and prevention that Bratton (1996) argues are central to COP. Hence, by using traditional policing methods and excessive force at the neighbourhood level, it seems likely that the NYPD will undermine the potential for the police to work effectively with some of the communities they aim to serve. However, Amnesty International (1996b) suggests that the police brutality within the NYPD is the result of police, in many cases, ignoring the NYPD’s own guidelines and point towards a significant gap between police policy and practice. It is thus unclear whether the NYPD example means that the disorder-removal and quality-of-life approach cannot significantly reduce fear, or whether well-founded policing policies simply broke down at the community–police level.

Environmental Design and Fear of Crime Strategies of Crime Prevention Through Environmental Design (CPTED) are ‘based on the theory that proper design and effective use of the built environment can reduce the incidence and fear of crime and make an improvement in the quality

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of life’ (Crowe, 1991). The primary goal of CPTED is to modify the physical environment so that it deters criminal activity, thereby making it safer for pedestrian activity, thus reducing fear of crime (JHSA, 1999).4 Equally important is the aim of encouraging people to use previously avoided public spaces (Oc and Tiesdell, 1997). These aims reflect the fundamental assumption in CPTED that environmental characteristics can be manipulated to effect human social behaviour, which subsequently reduces both the incidence of and the fear of crime (Crowe, 1991; Oc and Tiesdell, 1997; Steventon, 1996). While Jacobs (1961)5 is acknowledged as a forerunner in CPTED, Jeffery (1971) is seen to have initiated CPTED in his book. The author argues that urban design can play a role in crime prevention when security is considered in street and building plans (Jeffery, 1971). Despite these seminal works, modern CPTED strategies are based predominantly on Newman’s (1972) concept of ‘defensible space’ (Cozens et al., 2001). Newman (1972) drew on Jacobs’s insights to devise his theory of defensible space and proposed that altering the physical environment reduces opportunities for crime in urban areas (Newman, 1972).6 Defensible spaces primarily communicate residential control, have high prospects for natural surveillance and are difficult to escape from (Oc and Tiesdell, 1997; Schweitzer et al., 1999). Newman’s CPTED model therefore involves residents promoting surveillance opportunities, defining territorial boundaries, limiting access, eliminating conflicting uses, providing amenities and improving area aesthetics (Oc and Tiesdell, 1997; Pollack, 1980). Brantingham and Brantingham (1993) built on this logic by commenting that city planners can shape nodes, edges and paths in environments to affect broad patterns of crime through CPTED techniques. They drew on theories of situational crime prevention and the notion that criminal events require a convergence of victims, offenders and opportunity in space and time. Since these major CPTED theories, planners and policy makers have readily adopted the suggested principles. For example, the Department for Transport Urban Planning and the Arts in Australia encourages access controls that are designed to

4 The arrangement of urban form and activity, later dubbed CPTED, was identified by Pollack (1980) as one of three environmental-modification approaches to crime control. The other two approaches are the management of the environment (for example through police activity) and the use of protective devices (for example locks). 5 Jacobs proposed that feelings of safety in inner city areas are dependent on those areas being in continuous public use. Jacobs identified three main qualities of a safe city: territoriality, surveillance and social controls. To promote these there must be a clear demonstration between public and private space, buildings must be oriented to promote surveillance, and a diversity of street activities present to promote use and vitality (Jacobs, 1961; Oc and Tiesdell, 1997; Taylor and Gottfredson, 1986). 6 After studying crime in public housing, Newman observed that crime was discouraged from ‘zones of territorial influence’ that residents maintained surveillance over and defended (Newman, 1972; Pollack, 1980). Newman termed these areas defensible spaces, which he defined as a ‘range of mechanisms – real and symbolic barriers, strongly defined areas of influence, and improved opportunities for surveillance – that combine to bring an environment under the control of its residents’ (Newman, 1972).

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keep unauthorized persons out of particular spaces. Such controls include doors, shrubs, fences and even lit porch lights (DTUPA, 2002; Wagner, 1997). Similarly, physical barriers are also used to create clear boundaries between public and private areas. These generally signify ownership and include fences, hedges, pavement treatments, art, signs, good maintenance and landscaping (DTUPA, 2002; Schweitzer et al., 1999). Signs of increased surveillance are also popular in CPTED projects. These include the presence of neighbourhood watch signs and even porches and mailboxes, which increase opportunities for surveillance (Oc and Tiesdell, 1997; Schweitzer et al., 1999). Similarly, the British Crime and Disorder Act 1998 requires all local authorities to take crime and disorder into account in all aspects of decision making (Cozens et al., 2001). The British Department of Environment’s Secured by Design scheme further provides an accolade for housing schemes that meet specific CPTED design criteria (Kitchen, 2002). The criteria incorporate key principles such as aiming to create defensible space, territoriality and natural surveillance while minimizing escape routes, crime generators and fear generators (Kitchen, 2002). Another British approach, New Urbanism, draws on Jacobs’s (1961) works. New urbanism recognizes the importance of promoting human activity in the environment in order to achieve safety. A major feature is the encouragement of natural surveillance (Kitchen, 2002). A number of researchers have suggested that the fear of crime can be successfully reduced through such environmental or order-related improvements. Painter (1996) essentially identifies darkness and disorder as pivotal environmental cues that heighten fear in pedestrians and argues that good-quality street lighting can make a substantial contribution as a fear-reducing strategy. The author tested her assumptions in an experiment that looked at the impact of lighting improvements on crime, disorder and fear in three urban streets. The results showed a marked reduction in fear of physical attack and over 90% of pedestrians interviewed in all locations thought fear of crime in the surrounding area had gone down. In a similar study, Herbert and Davidson (1994) found that improved lighting in two British cities significantly reduced the fear of crime. Table 4.2 shows how the public perceived a number of problems central to the fear of crime such as fear of going out after dark and a range of physical and social incivilities to have decreased following the lighting improvements. However, the success of reducing fear of crime through environmental design has been questioned. Herbert and Davidson (1994) suggest that the astonishing influence of improved street lighting on the fear of crime in their study may be due to a halo effect. They describe this as the process where a single change appeared to stimulate other changes in aspects of local life, some of which had no obvious links to the actual environmental modification. Painter (1996) suggests that the effectiveness of the lighting strategy in her study was due to it altering the behaviour of the public, including potential offenders. The author suggests that improved sight lines, increased perceived risks of offending, increased pedestrian density and traffic flow and the associated enhanced natural surveillance all contribute to the potential for improved street lighting to reduce the fear of crime. However, the author

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Table 4.2 Problems in Herbert and Davidson’s (1994) study thought to be more common before and after relighting Hull

Cardiff

Problem

Before %

After %

Before %

After %

Afraid to go out after dark Burglary Rubbish/litter Theft of/from cars Noise nuisance Youths hanging around Vandalism Drunks Robbery Street lighting Being pestered

39 34 44 26 18 17 17 16 13 10 5

13 18 41 15 10 8 11 10 6 4 3

61 47 35 52 25 49 44 16 29 20 12

28 26 35 34 14 20 23 7 13 4 9

tempers her optimistic suggestions by emphasizing that if lighting is to be an effective strategy, planners need to be clear about the mechanisms they are expecting to induce in a specific environmental and social setting. In contrast to the successful projects described by Herbert and Davidson (1994) and Painter (1996), Nair et al. (1993) found that a wide range of environmental improvements, including improved street lighting, failed to reduce the fear of crime in an area of Glasgow, Scotland. Survey respondents later told the authors that the improvements were mostly made in areas where there was little public use and that they would have preferred the changes to have been made in more heavily used public spaces. Attempts to reduce fear through environmental improvements have been strongly criticized by others (e.g. Stanko, 1995; Koskela and Pain, 2000). Koskela and Pain (2000) list a number of projects, such as the work of Nair et al. (1993), which have failed to produce long-term benefits in terms of fear reduction. They argue that ‘designing out fear strategies’ only deal with one immediate and visible source of fear and leave few alternatives in the face of failure. Similarly, Stanko (1995) argues that environmental improvements, if not coupled with increased safety within private houses and relationships, will not significantly reduce women’s fear of crime. Koskela and Pain (2000) suggest that physical and social cues to fear are inextricably linked and that social connotations often explain why some places are regarded as particularly frightening. For example, the authors argue that women’s routine avoidance of certain areas is largely underpinned not by fear of concrete structures but by fear of unknown men. Some researchers have even gone so far as to say that CPTED actually exacerbate fear of crime. For example, Doeksen (1997) suggests that the growing concern for personal security in New Zealand and Australia has resulted in residents, private developers and engineers designing physical environments that emphasize separation over interaction. The author uses the example of how the important role of the colonial veranda as a means of promoting social surveillance within the streetscape has been reduced in many neighbourhoods because residents are literally fencing

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themselves off. Similarly, in the United States and the United Kingdom, protective responses to the fear of crime such as the rapid proliferation of gated communities and the implementation of closed-circuit television surveillance systems have been criticized for contributing to the atomization of communities and the breakdown of public life (e.g. Blakely and Snyder, 1997; Helsley and Strange, 1999; Lymes, 1997; Graham et al., 1998 in Ditton, 2000; Jackson and Gray, 2010). Where individuals become isolated, mistrusting and fearful in their homes, they are unlikely to form social ties with neighbours (Ross and Mirowsky, 1999). WilsonDoenges (2000) identified such patterns in gated communities in Orange County, California. Despite the fact that developers of gated communities aim to create a strong sense of community by providing access control and security walls, and that such measures increase levels of perceived safety (Lymes, 1997), residents from a high-income gated community reported significantly lower sense of community scores compared to those from a non-gated community (Wilson-Doenges, 2000). The negative effects of protective measures on the sense of community within residential areas are also evident within retail centres. Tiesdell and Oc (1998) describe the concept of the ‘fortress city’ which is based upon the separation of those who belong and ‘the other’. This entails the physical segregation, territorialization and defence of space with access controls. The authors argue that by isolating and defending particular territories, fortress cities protect only certain individuals or groups while undermining the public realm’s ideal qualities of social inclusivity, collectivity and universal accessibility. The cost of creating apparently safe, small environments through the use of target-hardening procedures may come at the cost of increasing the fear of wider public spaces (Brown and Polk, 1996) and may also displace the occurrence of crime onto areas or sectors of society that are unable to protect themselves to the same degree (Skogan and Maxfield, 1981; Herbert and Davidson, 1994; Davis, 1990; Tiesdell and Oc, 1998). For example, Davis (1998) outlines how the financial core of Los Angeles was protected by a barrage of security measures during the 1992 riots, while extensive damage was taking place in the old business district nearby. The implication of these criticisms is that any environmental improvements also need to impact upon social factors influencing the fear of crime (Koskela and Pain, 2000; Stanko, 1995). Logically, it should be possible, and may be more appropriate in some cases, to reduce fear through inducing social changes within the local community. Wikström (1995) describes a situation in Sweden where a particular street corner was identified as being a focus for disorder. The venue comprises of a number of restaurants and a bar that were frequented by upper-level secondary students and working class ‘rockers’. The author explains how the area was peaceful during the day and mostly at night. Only during the late evenings and especially at weekends did it become a ‘hotspot’ for stranger to stranger assault. Thus, the area provided a focus for crime and was also likely to inspire fear. Brown and Polk (1996) suggest a number of social measures to address the time-specific nature of disorder in Wikstrom’s (1995) example. The measures outlined by Brown and Polk (1996) were increasing police supervision of premises and public spaces at closing times, training bar staff in management techniques to lower confrontations

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with patrons at closing times and encouraging management practices that would result in keeping more orderly premises. Oc and Tiesdell (1997) and Thomas and Bromley (2000) discuss similar examples to Wikstrom’s (1995) in British city centres but suggest fear-reduction strategies that are a combination of both physical and social measures. Thomas and Bromley (2000) advocate social initiatives such as encouraging a wider range of activities and repopulating city centres to increase natural surveillance in combination target-hardening measures to alleviate motorist anxieties.

Chapter Review: Police, Community and Government Cooperation Fear of crime can be managed through a diverse range of approaches adopted by police, communities and governments. While traditional policing models have failed to acknowledge fear of crime, many models now see fear of crime as fundamental to proactive policing and crime prevention. Nevertheless, with regard to fear of crime, these models are limited by poor knowledge, their generalized responses or their lack of community involvement. Community involvement in fear-reduction strategies can help reduce the fear of crime experienced by public participants. In addition, governments can potentially reduce fear of crime through policies and plans that improve social infrastructure and the design of the environment. The choice of primarily policing, social or environmental strategies to reduce the fear of crime is likely to be dependent upon the nature of the problem in different communities and settings. In some cases the causes of fear may be predominantly social, such as in Wikstrom’s (1995) example. In other situations fear of crime may result from a combination of physical and social cues (e.g. Oc and Tiesdell, 1997; Thomas and Bromley, 2000). Implicit in this argument and the call for strategies to be relevant to the specific environmental and social settings of local areas (e.g. Painter, 1996) is the need for flexibility on behalf of the communities and police involved and an understanding of where and when fear of crime is a problem. Strategies to reduce fear of crime may be based on either social or physical measures and need to be grounded in a solid understanding of the specific environmental and social settings of local areas. This requires flexibility on behalf of the police and local communities as well as an understanding of where and when fear of crime is a problem.

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Newman, O. (1972). Defensible space: crime prevention through urban design. New York, NY, Macmillan. Novak, K. J., J. L. Hartman, et al. (1999). “The effects of aggressive policing of disorder on serious crime”. Policing 22(2): 171–190. NSW Police Force. (2011). “Profile Of Wollongong Local Area Command”. Retrieved April 4th, 2011, from http://www.policensw.com/region/southern/wollongong/lac/rw7.html Oc, T. and S. Tiesdell (1997). Safer city centres: reviving the public realm. London, Chapman. Painter, K. (1996). “The influence of street lighting improvements on crime, fear and pedestrian street use, after dark”. Landscape and Urban Planning 35(2–3): 193–201. Pollack, L. M. (1980). “Territoriality and fear of crime in elderly and nonelderly homeowners”. Journal of Social Psychology 111(1): 119. Ross, C. E. and J. Mirowsky (1999). “Disorder and decay: The concept and measurement of perceived neighborhood disorder”. Urban Affairs Review 34(3): 412–433. Salmi, S., M. Gronroos, et al. (2004). “The role of police visibility in fear of crime in Finland”. Policing-an International Journal of Police Strategies & Management 27(4): 573–591. Schweitzer, J. H., J. W. Kim, et al. (1999). “The impact of the built environment on crime and fear of crime in urban neighborhoods”. Journal of Urban Technology 6(3): 59–73. Sims, B., M. Hooper, et al. (2002). “Determinants of citizens’ attitudes toward police – Results of the Harrisburg Citizen Survey – 1999”. Policing-an International Journal of Police Strategies & Management 25(3): 457–471. Skogan, W. G. (1986). Fear of crime and neighbourhood change. Communities and crime. A. J. Reiss and M. Tonry (Eds.). University of Chicago Press, Chicago, IL: 203–230. Skogan, W. G. (1990). Disorder and decline: crime and the spiral decay in American neighbourhoods. Los Angeles, CA, University of California Press. Skogan, W. G. and S. M. Hartnett (1997). Community policing, Chicago style. New York and London, Oxford University Press. Skogan, W. G. and M. G. Maxfield (1981). Coping with crime: individual and neighborhood reactions. Beverly Hills, CA, Sage Publications. Smith, S. J. (1987). “Fear of crime: beyond a geography of deviance”. Progress in Human Geography 11: 1–23. Spelman, W. (2004). “Optimal targeting of incivility-reduction strategies.” Journal of Quantitative Criminology 20(1): 63–88. Stanko, E. A. (1995). “Women, crime and fear”. Annals of the American Academy of Political & Social Science 539: 46–58. Stanko, E. A. (2000). Victims R Us: the life history of fear of crime and the politicisation of violence. Crime, risk and insecurity. T. Hope and R. Sparks (Eds.). Routledge, London. Steventon, G. (1996). “Defensible space: a critical review of the theory and practice of a crime prevention strategy”. Urban Design 1(3): 235–245. Taylor, R. B. and S. D. Gottfredson (1986). Environmental design, crime and prevention: an examination of community dynamics. Communities and crime. A. J. Reiss and M. Tonry (Eds.). University of Chicago Press, Chicago: 387–416. Thomas, C. and R. Bromley (2000). “City-centre revitalisation: problems of fragmentation and fear in the evening and night-time city”. Urban Studies 37(8): 1403–1429. Tiesdell, S. and T. Oc (1998). “Beyond ‘fortress’ and ‘panoptic’ cities – towards a safer urban public realm”. Environment and Planning B: Planning and Design 25: 639–655. Tulloch, J. (1998). Quantitative Review. Fear of crime. J. Tulloch, D. Lupton, W. Blood, et al. (Eds.). National Campaign Against Violence and Crime (NCAVAC), Canberra. Wagner, A. E. (1997). “A study of traffic pattern modifications in an urban crime prevention program”. Journal of Criminal Justice 25(1): 19–30. Walklate, S. (2000). Trust and the problem of community in the inner-city. Crime, risk and insecurity. T. Hope and R. Sparks (Eds.). Routledge, London. Weisburd, D. and J. E. Eck (2004). “What can police do to reduce crime, disorder, and fear?” Annals of the American Academy of Political and Social Science 593: 42–65.

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Chapter 5

Investigating Fear of Crime

Defining Fear of Crime While fear of crime is easily interpreted during everyday discourse, it needs to be defined for research purposes (Skogan, 1999). The conceptual definition of fear of crime has clear consequences for its operationalization which in turn impacts how research results and findings are interpreted (Skogan, 1999). The definition of fear of crime and the research methods must therefore be clarified before investigations can be conducted and compared to other studies with any validity (Ferraro and LaGrange, 2000). Prior to 1980, researchers rarely explicitly defined fear of crime (Yin, 1980). In his comprehensive review of literature, Yin (1980) found that only Sundeen and Mathieu (1976) explicitly defined of fear of crime as ‘anxiety and concern that persons have of becoming a victim’. Five years later, Garofalo (1981) defined fear of crime as an emotional reaction characterized by a sense of danger and anxiety, produced by the threat of harm. Warr (1984) stated fear of crime had ‘acquired so many diverging meanings in the literature that it is in danger of losing any specificity whatsoever’. This problem was such that the concept of fear of crime and its research utility was considered ‘negligible’ (Ferraro and LaGrange, 1987). Comments such as this have continued well into the 1990s (e.g. Ewald, 2000; Stanko, 2000). Despite an abundance of studies on the topic, the literature still exhibits considerable confusion and ambiguity in relation to defining fear of crime (Pantazis, 2000; Warr, 2000). Fear of crime is equated with a diverging array of emotions, insecurities, concerns, perceptions or judgements, and attitudes or values (see Ditton et al., 2000; Ferraro and LaGrange, 2000; Furstenberg, 1971; Mawby et al., 2000; Warr, 2000). In order to define fear of crime, a strategy is needed to systematically unpack the concept (Ditton et al., 2000). It is useful to examine the individual terms ‘fear’ and ‘crime’ when defining the concept of ‘fear of crime’ as a whole. To do this, we draw upon and refine one of the most commonly used definitions, developed by Ferraro and LaGrange in 1987, which has proven influential in subsequent research (e.g. Ferraro, 1995; Rountree and Land, 1996; Tulloch, 1998). Ferraro and LaGrange (1987) define fear of crime as ‘the negative emotional reactions generated by crime or symbols associated with crime’.

B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_5,  C Springer Science+Business Media, LLC 2012

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Fear Is an Emotion, Not Cognition Much of the debate and confusion surrounding the concept of fear of crime arises from a failure to distinguish between emotion (i.e. what we feel) and cognition (i.e. what we think) (Ferraro, 1995; Warr, 2000). Defining fear as an emotion is therefore important because, although related, emotive and cognitive responses to crime are conceptually different. Thus studies confusing the two states could have markedly dissonant results that cannot validly be compared (Ferraro and LaGrange, 2000; Rountree and Land, 1996). According to Ferraro and LaGrange’s (1987) definition, fear of crime consists of ‘the negative emotional reactions’ [emphasis added]. Emotion is a distinctive mental state, a feeling state, which includes physical responses that prompt or restrain motivated behaviour (Carlson and Hatfield, 1992). In contrast, some researchers view fear of crime as a cognitive assessment. Cognitive assessments encompass people’s judgements about crime – their evaluation of personal risk (i.e. perceived risk) and their general concern about crime (Skogan, 1999). This differentiation was first described by Ferraro and LaGrange (1987) when arguing that fear of crime is strictly an emotional response. They designed the taxonomy shown in Table 5.1 to differentiate risk from fear, with perceptions of crime forming a continuum ranging from cognitive to affective. The cognitive perceptions relate to judgements of risk and the affective perceptions relate to fear reactions. The authors define the concept of fear of crime as being limited to the emotional reaction arising from crime, or the symbols that a person associates with crime (i.e. cells C and F of Table 5.1 below). Ferraro (1995) defines perceived risk as an acknowledgement of potential danger, real or imagined. This danger involves exposure to the chance of injury or loss (Ferraro, 1995). Assessments of risk or safety are people’s perceptions of the probability of someone being victimized (Ferraro and LaGrange, 2000; Skogan, 1999). The distinction between perceptions of risk and threat of victimization is cumbersome. According to Skogan (1999) they are distinct yet related. The author implies that perceptions of risk refer to actual rates of victimization and that threat refers to how at danger one personally is of being victimized, taking into account any strategies that have been adopted to reduce one’s vulnerability. However, Mesch (2000) Table 5.1 Taxonomy of crime perceptions developed by Ferraro and LaGrange (1987) Type of perception: cognitive and affective Level of reference

Judgements

Values

Emotions

General

A. Risks to others: crime or safety assessments D. Risk to self: safety of self

B. Concern about crime to others

C. Fear for others’ victimization

E. Concern about crime to self: personal intolerance

F. Fear for selfvictimization

Personal

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draws the distinction between perceived risk and fear of crime, finding that they are related to different predictors. Researchers also define fear of crime as a concern or worry about crime, which can be referred to as a value (Ferraro and LaGrange, 2000). However, concern is not linked to fear, but to a state of agitation regarding the level of crime in one’s environment and a belief that crime is a serious social problem (Furstenberg, 1971; Oc and Tiesdell, 1997). This is also regarded as being distinct from the perceptions of risk or threat. Skogan (1999) elaborates, stating that concern is ‘a judgment about the frequency or seriousness of events and conditions’ and is distinct from threat because people believe they are capable of dealing with such crime. Garofalo (1981) provides the example of people being more concerned than fearful when it comes to property crime because the threat of physical harm is low compared to personal crime (Garofalo, 1981). Similarly, worry about crime may be reduced by behavioural changes without impacting on fear (Tulloch et al., 1998). With these contrasting meanings, distinguishing between fear (an emotion) and either risk, concern or worry can help when attempting to validate or draw comparisons between different fear-of-crime studies (Lewis and Salem, 1986).

Fear in Relation to Other Emotional Reactions and Stimuli that Trigger Fear Ferraro and LaGrange’s (1987) definition of fear as an emotion fails to distinguish fear from other emotional reactions, like sadness, anger or despair (Warr, 2000). Some researchers argue that many surveys aimed at examining fear of crime are actually tapping into other emotions (e.g. Innes et al., 2002; Innes, 2004). Farrall and Ditton (1999) suggest that respondents are more likely to feel anger, outrage or annoyance rather than fear when thinking about crime. Thus distinguishing fear from these other emotions is important when comparing the potentially discordant results from fear-of-crime studies. This also reinforces the need to succinctly define and target fear when undertaking research in the area. Fear is one of the six primary human emotions essential for survival (Neill, 2001) and is considered to prompt one to protect oneself against loss when confronted by a risk (Clark, 2003). Essentially, fear is a negative emotion that describes feelings of alarm, dread or apprehension about tangible or perceived threats (Clark, 2003; Innes, 2004). Thus, fear is an emotion characterized by an expectation of danger that is produced by the threat of harm (Williams and Dickinson, 1993; Sluckin, 1979). Fear forewarns danger, promoting vigilance and a fight or flight response (Carlson and Hatfield, 1992; Oatley and Jenkins, 1996). In general, fear is determined by an object or stimulus that is expected to cause harm and is not qualitatively different from other forms of fear (Warr, 2000). However, it is important to clarify what makes fear of crime distinct from other forms of fear. Fear of crime is specifically the fear of being harmed during criminal victimization and it is generated by crime or symbols associated with crime (Warr, 2000). These symbols can be thought of as environmental cues that relate to some aspect of crime (Williams and Dickinson,

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1993). Numerous problems, discussed later in this chapter, arise from studies tapping into people’s diffuse or ‘formless’1 feelings of fear, rather than specific or ‘concrete’2 fear of crime. Fear may be aroused by immediate danger, for example an armed attacker, but is often experienced as anticipating a potential threat (Carlson and Hatfield, 1992; Kaplan, 1973). This occurs when people react to environmental cues that imply danger because they are associated with crime (Garofalo, 1981; Warr, 2000). Psychologists identify the emotional reaction to potential threats as anxiety (Clark, 2003). Warr (1984) reasons that anxiety is much more common than fear associated with real encounters of crime (Warr, 2000). Garofalo (1981) also states that behavioural changes can result from such anticipatory fear. This perhaps prompted Ferraro’s (1995) amended definition of fear of crime as an ‘emotional response of dread or anxiety to crime or symbols that a person associated with crime’ [emphasis added]. This is the conceptual definition of fear of crime that is consequently used in this chapter. A further difficulty relating to defining the term arises from the fact that the ‘crime’ in ‘fear of crime’ is also subject to contention.

Crime Involves a Violation of Criminal Law The term ‘crime’ has escaped definition in much of the criminological literature, with many studies presuming crime is self-explanatory (Ewald, 2000). However, how people conceive crime influences their response to fear-of-crime survey questions. Defining crime is therefore a necessary component when defining ‘fear of crime’. Nevertheless, even when crime is defined, opposing theoretical approaches leads to contention (Sparks et al., 2001). The two mainstream legal and social definitions of crime are discussed here.3 Traditional jurisprudential definitions of crime describe it as an act in violation of criminal law. For example, Reiss (1986) defines crime as ‘an event or sequence of events in time and space that violates the criminal statute’. Criminal law, or statute, represents those norms of conduct within a society that are intended to influence, regulate and guide the behaviour of the public (Potas, 1996). However, these social norms are formalized and enforced by a political authority through legislature and the courts (Potas, 1996; Sutherland and Cressey, 1970).4 Therefore, as Stephen 1

Formless fear is a non-specific anxiety (Friedberg and R.a.F. Inc, 1983). Concrete fear is the fear of becoming the victim of a specific crime (Friedberg and R.a.F. Inc, 1983). 3 However, there are many more approaches to defining crime (see Vold et al., 2002; Walsh and Poole, 1983; White and Haines, 2004). 4 (Oc and Tiesdell, 1997) emphasize that the definition of crime reflects the social and political processes whereby certain actions are subjected to criminalization. As crime is dependent on those with the power to label, it can be used to censure certain groups of people. The legal definition of acceptable behaviour can be modified should public concern be acknowledged – for example the introduction of bylaws outlawing the consumption of alcohol in public spaces. 2

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(1983) states, crime also becomes an ‘act or omission in respect of which legal punishment may be inflicted’ (cited in Walsh and Poole, 1983). In contrast, social perspectives of crime propose that crimes are violations of any social code, whether defined by criminal law or not (Jeffery, 1971). These social codes or ‘laws of morality’ also guide public behaviour but are not traceable to a single universally recognized rule-making institution that can enforce them through sanctions for disobedience (Potas, 1996). As social norms of conduct characterize crime, this definition includes many acts not usually regarded as legally criminal, such as drug addiction and prostitution (Jeffery, 1971). The social concept of crime links most closely with the general public’s viewpoint. It is often acts of disorder, rather than legally defined crimes, that cause fear of crime (Oc and Tiesdell, 1997). Clarifying Ferraro’s (1995) fear of crime definition, ‘crime’ in the research presented in Chapters 6 and 7, is seen as a violation of criminal law, yet it is acknowledged that the threat of crime can be triggered by acts of disorder that infringe only social norms. Some researchers contend that fear of crime examined in numerous studies is not actually a true fear of ‘crime’. These social theorists conclude that fear of crime is actually an underlying formless fear caused by different societal problems (Lane and Meeker, 2003). Bearing this in mind, researchers must be vigilant to target fear of actual, legally defined ‘crime’ when devising survey questions. The relationship between fear of crime and a number of different variables proposed by social theories are discussed in the following section on ‘factors associated with fear of crime’. Before doing so, it is necessary to note that crime, in its everyday sense, can be delineated by type of crime, subject of victimization and fear.

Types of Fear of Crime: Personal and Altruistic Points of View Rountree and Land (1996) state that researchers have ‘generally ignored the potentially important distinctions between types or dimensions of fear of crime’. Two dimensions of fear of crime are identified, one concerning the type of victimization (personal or property) and the second concerning the subject of victimization (personal or altruistic). Fear of personal crime was distinguished from fear of property crime by Ferraro and LaGrange (1992). Levels of fear and reactions to fear will vary according to whether the threat of physical harm from victimization is targeted on the person or one’s property (Garofalo, 1981). Therefore it is essential that the type of victimization (personal or property) be specified in fear-of-crime studies. Warr (2000) has been a strong advocate for the study of altruistic fear of crime, which he argues is predominant in society. The author contends that individuals may not only fear for their own personal safety when in a dangerous environment, but also for the safety of other individuals whom they value. Altruistic fear is defined as ‘an emotional reaction to the perceived danger that a household member would be a crime victim’ by Beck et al. (2004). However, it is also likely that altruistic fear extends to those outside of the household to close family and friends, or even the public at large. Nevertheless, it is necessary that researchers distinguish personal

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fear (fear for oneself) from altruistic fear (fear for others) in their investigations (Warr, 2000). Refining the various types of victimization in this way has led to some improvement and clarity in results (Lewis and Salem, 1986).

Review: Key Issues to Consider When Defining Fear of Crime Historically, researchers have failed to succinctly define ‘fear’ and ‘crime’. Conceptual confusion has arisen when researchers have not defined fear of crime and assume it is commonly comprehended. This section has outlined the problems of defining ‘fear’ and ‘crime’. Drawing from the literature, it is recommended that the following points should be taken into account when undertaking research into fear of crime: • • • • •

defining fear as an emotion, not a cognition; recognizing fear is distinct from other emotions; distinguishing fear triggered by the threat of crime from formless fear; focusing on fear of crime that involves a violation of criminal law; acknowledging fear of crime can be triggered by violations of social norms, known as acts of disorder; and • being mindful of the different types of fear of crime. In doing this, researchers can better-define and operationalize fear of crime, tailoring their research design appropriately to maximize the potential for useful results.

Measuring Fear of Crime In order to scientifically investigate fear of crime, the variables in question must be accurately measured (Ferraro and LaGrange, 2000). Researchers consistently dispute the method by which fear of crime should be measured. Thus there are significant contradictions in research findings, even when examining a single dataset (Rountree and Land, 1996; Stafford and Gall, 1984; Mesch, 2000). The extent of these measurement inconsistencies seriously impedes the ability of researchers to make useful generalizations which could be used in initiatives aimed at combating fear of crime (Ferraro and LaGrange, 2000). There are three major approaches used in fear-of-crime research – namely, cognitive, affective and behavioural measures.

Problems with Cognitive Approaches to Measuring Fear of Crime The research utility of traditional cognitive approaches to measuring fear of crime is highly criticized (Rountree and Land, 1996). Despite this, they are continually used in Australian and international crime and safety surveys. Cognitive approaches include global and value- or concern-based measures.

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Global Measures The most widespread approach to measuring fear of crime is based on perceptions of risk. Survey respondents are typically asked to assess how safe their neighbourhood is or how likely they are to be victimized (Rountree and Land, 1996). The most popular question is ‘How safe do you feel, or would you feel, out alone in your neighbourhood at night’ or something similar (e.g. Ditton and Farrall, 2000; Borooah and Carcach, 1997; Killias and Clerici, 2000; Mawby et al., 2000; Mirrlees-Black and Allen, 1998; Pantazis, 2000; Walker, 1994). Respondents answer by choosing from a list of options such as I feel ‘very safe’, ‘reasonably safe’, or ‘somewhat safe’ (ABS, 2006; Liska et al., 1988; Pantazis, 2000). As these questions do not refer to a particular crime, they are often referred to as global measures (Pantazis, 2000). There are a number of problems associated with global measures. First, they are a cognitive approach, targeting what respondents think (Ferraro and LaGrange, 1988). By asking respondents ‘How safe do you feel. . .’, global measures confuse fear of crime with perceived risk, invoking a general assessment of safety in one’s neighbourhood (LaGrange and Ferraro, 1987). Ferraro and LaGrange state that while perceived risk may be an important predictor of actual fear, (Rountree and Land, 1996), peoples’ perceptions of risk of victimization are ‘vastly different’ from their feelings of fear of victimization (Ferraro and LaGrange, 2000). Thus, perceived risk is distinct from, and cannot be used to measure, people’s fear of crime (Pantazis, 2000; Rountree and Land, 1996). Furthermore, it is uncertain whether respondents’ answers to global measurement questions actually reflect their perceptions of risk in the area, knowledge of real risks of victimization or genuine emotional fear (Garofalo and Laub, 1978; Pantazis, 2000; Rountree and Land, 1996; Wilson and Kelling, 1982). Due to this ambiguity inherent in the respondents’ answers, such global measures are criticized as being vague and problematic (Rountree and Land, 1996). A similar global question asks ‘Is there any area right around here – that is, within a mile – where you would be afraid to walk alone at night?’ (LaGrange and Ferraro, 1987). This question is more likely to tap into the emotional aspect of fear because the word ‘afraid’ is used, however it is still ambiguous and seems excessively foreboding (LaGrange and Ferraro, 1987). The word ‘crime’, or a specific act or acts that constitute crime, is not mentioned in global measurement questions (LaGrange and Ferraro, 1987). Respondents may not be sure what they are meant to feel safe or unsafe from, and therefore could confuse their fear of crime with fear in general (Garofalo, 1979; LaGrange and Ferraro, 1987). This creates a conceptual issue for people with specific phobias that cause them to feel unsafe in certain areas. It also opens the door to the various social theories that argue, for example, that fear of crime actually reflects perceptions of subcultural diversity (Covington and Taylor, 1991; Hanson et al., 2000; Katz et al., 2003; Merry, 1981; Taylor and Hale, 1986). With global questions it is important not to assume that people stay home at night because they are afraid of crime, but rather for a diverging array of other reasons (LaGrange and Ferraro, 1987). There are ambiguities even when ‘crime’ is mentioned (Ferraro and LaGrange, 1987). Fear varies with the type of crime under consideration, for example it

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depends on whether the crime involves a threat to one’s personal well-being or damage to one’s property (Skogan and Maxfield, 1981). In terms of personal crime, experiences of fear differ if, for instance, rape or robbery is considered. Global measurement questions conceal any differences in the level of fear of these different crimes (Ferraro and LaGrange, 2000). A lack of crime specificity in survey questions forces respondents to select their own conceptual references. This choice differs between people and, therefore, responses may not be comparable (Ferraro and LaGrange, 2000). Ferraro and LaGrange (1987) argue that the lack of crime specificity in global measurement questions overrides any of their usefulness as fear-of-crime measures. Another criticism of global measures also relating to a lack of specificity is the often-vague geographic reference to the area in which people live. The spatial frame of reference of global measurement questions, the ‘neighbourhood’, is not sufficiently defined and can be envisaged differently by different people (Ferraro and LaGrange, 2000; LaGrange and Ferraro, 1987). For instance, those respondents completing the same fear-of-crime survey may reside in completely different neighbourhoods and thus be referring to a separate environment in their response. For those respondents actually even living in the same neighbourhood their ideas of the boundaries of that neighbourhood may be quite discordant. This inhibits comparison of respondents’ answers. Furthermore, assuming that each respondent reflects on the same neighbourhood when answering the global measurement question, it is still unclear whether they are fearful in the entire neighbourhood or only certain parts of it. This is particularly relevant considering that crime levels and rates fluctuate dramatically within urban neighbourhoods (LaGrange and Ferraro, 1987). Fisher and Nasar (1995) argue strongly that much extant research is limited because studies using global measures cannot reveal the location of specific fear spots or what types of cues stimulate the fear-generating process in individuals or across groups. Finally, survey items asking respondents ‘do you feel, or ‘would you feel’ merge reality with the hypothetical, thereby creating a double-barrelled question (LaGrange and Ferraro, 1987). Tulloch (1998) suggests that global measures are hypothetical for many women and older people because they rarely, if ever, walk alone at night. LaGrange and Ferraro (1989) argue that it is methodologically inappropriate to use hypothetical scenarios since it is difficult for respondents to evaluate how they would feel (Ferraro and LaGrange, 2000). In addition, the use of hypothetical scenarios may exaggerate fear-of-crime levels because it could seem excessively threatening (LaGrange and Ferraro, 1989). Tulloch (1998) further argues that such measures potentially fail to assess how people engage with fear of crime in their daily routines, often producing poor model results. An example of this can be found in the research conducted by Nair et al. (1993) into the effect of environmental improvements on fear of crime. The authors found that significant lighting changes made in a park in Glasgow, Scotland, did not result in the expected substantial fall in fear of crime. The survey respondents later indicated that the improvements to the park were not relevant to their daily routines and that the net result was to turn a poorly lit bad area into a well-lit bad area. Such limitations have, in part, led to the call for researchers to contextualize their studies such that

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the fear-of-crime issues investigated are relevant to the daily routines of the survey respondents (e.g. Nair et al., 1993; Smith and Tortensson, 1997; Tulloch, 1998). Value- or Concern-Based Measures Closely aligned with global questions are value- or concern-based measures. The terms ‘worry’ and ‘concern’ are often interchanged with fear in social surveys (Skogan and Maxfield, 1981 in LaGrange and Ferraro, 1987). However, instead of targeting emotional levels of fear, these questions evaluate people’s opinions of the seriousness of the level of crime in their neighbourhood (Furstenberg, 1971). Furstenberg (1971) provides the example of asking respondents to, ‘choose the single most serious domestic problem (from a list of 10) that you would like to see government do something about’. Another simpler version involves asking respondents if they are personally concerned about becoming a victim of crime (Jaehnig et al., 1981 in Ferraro and LaGrange, 1988). As discussed in Chapter 3, people’s concern or worry about crime is distinctly different from their fear of crime. People who are troubled by the problem of crime are not necessarily afraid of being personally victimized (Furstenberg, 1971).

Improvements Through Affective Approaches to Measuring Fear of Crime While cognitive approaches to measuring fear of crime involve people making judgements about how safe they feel, affective approaches aim to elicit more of an emotional response and aim to measure ‘fear of crime’ in a more literal sense. ‘Emotion-based measures’ is the most common term given to these approaches in the literature. Emotion-Based Measures In contrast to global measures and other types of cognitive approaches to measuring fear of crime, emotion-based measures make explicit reference to a specific crime (Ferraro and LaGrange, 2000). In doing this, they target ‘concrete’ fear by eliciting a personal, emotional reaction from the respondent (Ferraro and LaGrange, 1987; Rountree and Land, 1996; Scott, 2003). While this reaction may also depend on perceived risk, it is distinct from judgements or concerns about crime (Ferraro and LaGrange, 2000). Emotion-based questions include ‘how afraid are you of becoming a victim of . . .’ (Mawby et al., 2000; Rountree and Land, 1996). Respondents answer by choosing from a list of options such as I feel ‘very afraid’, ‘fairly afraid’ or ‘a bit afraid’ (Skogan, 1999). These questions allow respondents to visualize themselves as victims of the crime (Reid et al., 1998). The extent of the fear elicited by the specific crime mentioned in the survey question will depend on a number of factors. Fear of crime is initially based on the nature

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and perceived seriousness of the offence in question which will vary according to community context, social group and the individual (Clark, 2003). Fear is also influenced by an individual’s risk sensitivity to the crime in question (Clark, 2003; Warr, 1984; Rountree and Land, 1996). Each of these factors is subconsciously assessed when a person thinks about a crime and affects the extent of the fear response, pointing out the importance of crime specificity (Clark, 2003). By referring to a specific crime and eliciting these personal considerations, emotion-based measures effectively overcome many problems with global measurement questions. However, they also result in highly subjective responses. People have differing perceptions about concepts like ‘a bit afraid’. Two respondents who state they feel ‘somewhat afraid’ may react completely differently, and therefore comparative analysis of cognitive and affective comments can be problematic. This problem and the hypothetical nature of the questions used restrict the utility of emotion-based measures to certain contexts. Few studies have gathered crime-specific data on fear and those that have generally rank crimes according to the level of fear that they produce (Warr, 2000).

Behavioural Approaches to Measuring Fear of Crime Ditton et al. (2000) criticize fear-of-crime research as being ‘trapped within an overly restrictive methodological and theoretical framework’. In a similar vein, Warr (2000) argues that ‘the study of fear seems to have stalled at a rudimentary phase of development, a situation that is in danger of turning into outright stagnation’. A major factor in this lack of progress is due to continual use of these problematic cognitive and affective questions in surveys (Ditton et al., 2000; Warr, 2000). One potential means of avoiding some of the limitations of global measures of fear of crime is to use behavioural measures. At a general level, behavioural measures would seem to be appropriate, given the common finding that people respond to fear by modifying their behaviour (Samuels and Judd, 2002; Tulloch, 2000; Warr, 2000). As Skogan (1999) indicates, fear is validated when it manifests through behaviour. Behavioural approaches eliminate much of the subjectivity associated with responses from cognitive or affective questions. By focusing on fear of crime through behavioural responses, researchers can measure and compare fear more reliably than other techniques. In fact, Hale (1996) argues that behaviour is a more accurate guide to fear levels than reported statements about fear level. This notion prompted Warr (2000) to state that behaviour may be the best indicator of fear. Behavioural approaches examine the protective actions and avoidance strategies adopted by people attempting to reduce fear and hold more potential to relate to the routines of the survey respondents (Gabriel and Greve, 2003; Samuels and Judd, 2002; Smith and Tortensson, 1997; Tulloch, 1998, 2000). Protection-Based Measures People who are afraid of crime, either in their home environment, or out in their neighbourhood, are likely to use self-protection (Ferraro, 1995; Tewksbury and

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Mustaine, 2003). To determine the types of self-protection employed by survey respondents, they are usually asked questions such as ‘in general have you limited or changed your activities in the past year because of crime (yes or no)’ (Liska et al., 1988). A list of protective actions from which respondents can then choose is often provided (see DeFronzo, 1979; Gray and O’Connor, 1990; Sundeen and Mathieu, 1976). Protective actions are employed to either limit one’s exposure to risk or reduce one’s chances of being victimized when exposed to risk (Skogan and Maxfield, 1981). Many of these actions also therefore make people feel less afraid of crime (Vacha and McLaughlin, 2004). Protective actions generally include individual coping strategies or collective actions. Individual coping strategies are diverse and extensive. In terms of protection against property crime, people adopt ‘targethardening efforts’ (Skogan and Maxfield, 1981) through creating physical barriers for offenders to overcome by locking their doors when leaving home (Warr, 2000), installing extra security locks, bars and systems (Carvalho and Lewis, 2003) and keeping trained watch dogs (Williams et al., 1994). Psychological barriers to deter offenders are also employed such as installing car and home alarms (Reid et al., 1998) and leaving lights or timed appliances like radios and television sets on at home when they are out (Krahn and Kennedy, 1985; Warr and Ellison, 2000). Other coping strategies that protect against, or minimize, the negative consequences of property loss and damage include the engraving of valuables and the purchase of theft and vandalism insurance (Williams et al., 1994) and the use of police property identification systems (Toseland, 1982). The individual coping strategies that people employ to protect themselves against personal crime are also wide ranging. These commonly include the carrying of a weapon such as a handgun or mace to use when warding off or defending against an attacker (DeFronzo, 1979; Kenney, 1987; Reid et al., 1998). Personal alarms and whistles are also carried to drive away attackers and alert passers-by of the problem. For those who choose not to arm themselves in any way, they often simply increase their level of alertness (Reid et al., 1998) and walk faster during those moments of fear. They may also choose to drive a car or use other ‘safe’ methods of travel through feared areas rather than walk (Warr and Ellison, 2000). When at home, people may also refuse to open the door to a stranger (Warr, 1985). Collective actions that are used to protect against crime, and consequently fear of crime, often transcend the boundaries between personal and property crime. A widespread response is people walking in pairs or groups when in feared areas (Carvalho and Lewis, 2003; Nasar et al., 1993). Other collective actions include the organization of ‘neighbourhood watches’ (Reid et al., 1998). Williams et al. (1994) find that these collective actions are more common than personal coping strategies. While these protection-based measures overcome problems of subjectivity, they have generally been subject to the limitations of vague geographic references associated with the global measurement framework. In comparison to the multitude of cognitive and affective studies, relatively little information has been collected on the protective measures adopted by people in response to fear of crime, especially in response to fear of different specific crimes (Reid et al., 1998). Additionally, little is known about the different socio-demographic groups who employ such protective

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measures and if the use of self-protection is related to an individual’s proximity to potential offenders (Tewksbury and Mustaine, 2003). Avoidance-Based Measures As discussed earlier, avoidance is documented as one of the most frequent behavioural responses to fear of crime (Garofalo, 1981). Avoidance refers to ‘those actions taken to decrease the chance exposure to crime by removing or distancing oneself from situations in which the risk of victimisation is perceived to be high’ (DuBow et al., 1979). Often people restrict their movements to safe places at safe times or refuse to leave their homes at all, particularly during the night (Pantazis, 2000; Samuels and Judd, 2002). Some residents even choose to avoid the neighbourhood altogether by moving (Carvalho and Lewis, 2003; Reid et al., 1998). Because collective avoidance is central to many of the negative consequences on affected communities, avoidance-based measures are particularly relevant to the study of fear of crime and any associated fear-reduction strategies. As mentioned, research into avoidance generally involves asking respondents if they avoid any areas because they feel unsafe (Ditton and Farrall, 2000) or something similar to ‘do you avoid certain places and areas of the city because of the possibility of crimes of violence’ (Gomme, 1986). The response to these avoidance-based items in fear-of-crime surveys predominantly features only a yes or no possibility. As such, these studies have only been useful for broad-level macro analyses of fear of crime and avoidance behaviour. Behavioural approaches that measure the actual behaviour of respondents (e.g. Fisher and Nasar, 1992; Nasar and Jones, 1997; Nasar et al., 1993) have the potential to overcome the limitations of global measures relating to the hypothetical nature of survey questions and vague geographic references. For example, avoidance-based questions more recently include a spatial element, with a request that those avoided areas be illustrated on a map (Doran and Lees, 2005; Nasar and Jones, 1997; Nasar et al., 1993). When assessing collective behavioural responses, it is appropriate that mapping restricts the scope of the question to a geographic reference that is defined and common to all respondents which enables a more accurate comparison across an area or ‘neighbourhood’. Despite these benefits, the utility of behavioural measures has often been limited because of a lack of crime specificity. In comparison to the multitude of cognitive and affective studies, relatively little information has been collected on the behavioural reactions adopted by people in response to fear of crime, especially in response to fear of different specific crimes (Reid et al., 1998). Additionally, little is known about the different socio-demographic groups who employ such measures and if the use of self-protection or avoidance is related to an individual’s proximity to potential offenders (Tewksbury and Mustaine, 2003). However not everyone will be able to adopt avoidance as a precautionary behaviour. Hindelang et al. (1978) discuss routine daily activity theory and call attention to a number of constraints that could affect whether people are able to adopt avoidance strategies. Skogan and Maxfield (1981) argue that social norms

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and expectations of ‘role’ according to peoples’ socio-economic positions may preclude them from being able to avoid different areas because they are subject to ‘a host of formal and informal mechanisms which channel [their] activity in expected ways’. Other limitations may ‘derive from the operation of institutions’, namely where people are required to live and work. Skogan and Maxfield (1981) also provide some other examples of constraints on avoidance behaviour: people may lack the resources necessary to avoid feared areas, for instance they must use public transport or walk through feared areas if they do not have a car or a driving license. The authors argue that behaviourally based approaches to measuring fear of crime must consider such constraints (Skogan and Maxfield, 1981).

Review: A Preference for Avoidance-Based Measures in Fear-of-Crime Studies The range of different techniques used to measure fear of crime may be in part due to the complex and multifaceted nature of the issue. Researchers have generally measured fear of crime using three main approaches. The cognitive approach to measuring fear of crime is relatively easy to carry out. However, both the global and value- or concern-based measures are limited in their utility because they do not target actual ‘fear’ of crime. The wording of survey questions also results in responses that are difficult to interpret. While the affective approach to measuring fear of crime does target people’s emotional fear of crime, it too results in ambiguous and subjective findings due to lack of geographic specificity. Despite these restrictions, cognitive and affective approaches to measuring fear of crime have been useful for broadlevel analyses. In contrast, behavioural approaches to measuring fear of crime hold the potential to overcome much of the subjectivity and ambiguity inherent in cognitive and affective survey responses. As Smith (1987) noted in an earlier review, the observed effects of fear of crime on lifestyle are too marked to ignore. This still holds true some 20-plus years later, and it is in this area that future research needs to be conducted. Behavioural approaches, particularly avoidance-based measures, can also produce site-specific, or spatially explicit, results. This means they can be used to perform analyses using Geographic Information Systems (GIS). The potential advantages of spatial analyses of fear of crime are discussed in the concluding sections of this chapter.

Analysing Fear-of-Crime Data Fear of crime is typically analysed using traditional statistical techniques. Bivariate analyses dominate the field, with researchers using Pearson’s correlation co-efficient (r), Spearman’s rank (r) and Chi-Square analyses (e.g. Mirrlees-Black and Allen,

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1998; Reid et al., 1998; Wilson-Doenges, 2000), often as a basis for more complex analyses (e.g. Karakus et al., 2010). Many studies also acknowledge the multidimensional nature of fear of crime and concentrate on the interactions between a multitude of dependent variables and fear of crime (Box et al., 1988; Carcach and Mukherjee, 1999; Ferraro and LaGrange, 1987). Given the contrasting and numerous approaches used to measure fear of crime, it is not surprising that different analytical techniques can give rise to conflicting or dissonant results, even when examining the same dataset (see LaGrange and Ferraro, 1989). One overarching disadvantage of many statistics-based studies is that they are spatially implicit in nature, in part due to the use of cognitive or affective measurement approaches and associated vague geographic references that are ingrained in survey questions. When findings are presented as the percentage of fearful people or fearful subgroups within a study area or region (e.g. Joseph, 1997; Mayhew and White, 1997; Mirrlees-Black and Allen, 1998; Thomas and Bromley, 2000), they are subject to the ecological fallacy and modifiable areal unit problem (MAUP). These issues are well known to urban geographers and arise when a researcher makes inferences about an individual based on area-level aggregations or when data are represented according to different administrative boundaries (e.g. Cromley and McLafferty, 2002; Longley et al., 2001; O’Sullivan and Unwin, 2010). Figures 5.1 and 5.2 below illustrate the MAUP and ecological fallacy with two hypothetical examples. In Fig. 5.1, it can be seen that different counts are derived for the same point dataset when different sets of boundaries are used. In Fig. 5.2, a hypothetical administrative boundary (e.g. a suburb or census district) envelopes a pocket of relative socio-economic disadvantage. In this instance, if the area were labelled as having 40% high-income houses, this would not be an accurate aggregation and could mask the subarea of disadvantage. From the perspective of the institutions responsible for addressing fear of crime, such as police and council services, the outputs from traditional statistical analyses make limited contribution to the ‘where’ and ‘when’ aspects of fear of crime, which are emphasized as fundamental components of fear management strategies (e.g. NCAVAC, 1998). While issues such as the MAUP and ecological fallacy are difficult to avoid entirely (Cromley and McLafferty, 2002; Monmonier, 1996), the use of geocoded data provides a more sensitive means of handling fine-scaled relationships (e.g. Doran et al., 2007). As such, the collection and analysis of spatially explicit fear-of-crime data can potentially contribute much to a ‘stagnant’ field (Warr, 2000) through the provision of information that is not constrained to area-level aggregation alone as a means of summarizing the geography of fear. As Goodchild (2004) notes, Only a fraction of 1 percent of the literature published in the social sciences takes a spatial perspective, so the potential for growth is still enormous.

It would seem that the use of GIS in fear of crime would be one such area where there remain many useful avenues open to investigation.

Analysing Fear-of-Crime Data Fig. 5.1 The effect of the MAUP

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B) 6 2 6 7

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Fig. 5.2 The effect of the ecological fallacy

POCKET OF RELATIVE DISADVANTAGE

Advantages of Spatial Analyses of Fear of Crime Many researchers acknowledge a distinct spatiotemporal element to crime and fear of crime, which researchers should be sensitive to (Gold and Revill, 2003; Lemanski, 2004; Moran et al., 2003; Warr, 2000). Lupton and Tulloch (1999) call for research that explores the ‘dynamic situated and micro-contextual contexts in which fear of crime is generated and experienced’ (Lupton and Tulloch, 1999). By doing this through spatial analysis, fear-of-crime findings can be integrated with an understanding of the social and physical environment (Pain, 2000). As Samuels and Judd (2002) elaborate in the following statement, Mapping provides a spatially focused base for the interpretation of social indicators in their epidemiological context. Maps are setting specific, temporary sensitive, visual-diagnostic tools . . . allowing situational experience to be interpreted in light of the theory and practice of environmental design and community empowerment criminology.

Ashby and Longley (2005) state that these ‘geodemographic’ analyses lead to significantly improved police intelligence. For example, a spatial knowledge of fear of crime and avoidance patterns could allow for the targeting of limited resources to specific hotspots. Such locally tailored responses are also more likely to be effective than generalized strategies (Kitchen, 2002; Nelson et al., 2001; Skogan, 2004). In light of this, Fisher and Nasar (1995) believes that fear-of-crime studies missing a spatial element are vague and less informative than those that do.

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Despite these benefits, few researchers have ventured into the world of spatial analyses of fear of crime. Doran and Lees (2005) used GIS to investigate the spatiotemporal links between crime, fear of crime and disorder – a study that is described in detail in Chapter 6. The study was conducted in a central business district (CBD) area and focused on sampling the working population. The fear mapping outputs helped councils, local agencies and the police to determine priority areas for fear reduction and appropriate measures in different locations earmarked for change in town planning initiatives. The spatial outputs also enabled a comparison of collective avoidance hotspots and concentrations of crime and disorder. It was evident that there existed many avenues for future research. In particular, the underlying motivations for avoidance behaviour in relation to specific fear of crime hotspots were not examined in detail. The techniques developed were suited to the comparison of different geographic areas and held the potential to objectively determine which socio-demographic groups are more afraid. Toseland (1982) states that such outputs could assist special efforts targeting these vulnerable groups. This focus on underlying motivations for avoidance behaviour and different responses to specific cues in fear-of-crime hotspots were investigated in the Kings Cross study – described in Chapter 7. At a macro scale, the spatial visualization of fear hotspots also allows for an investigation into the proposed idea that fear of crime is predominantly an urban, rather than rural, problem (Cates et al., 2003; Miceli et al., 2004; Yarwood, 2001). Therefore, fear mapping has the potential to provide an additional layer of understanding, as well as more localized and geographically relevant information than traditional statistical approaches. The foundation of fear mapping has its roots in behavioural geography and the associated use of cognitive mapping techniques in a GIS-based framework, which are discussed next.

Spatial Cognition and Cognitive Mapping Cognitive mapping, a technique that has been used extensively to gather geographic information in the broader area of behavioural geography (Kitchin, 1996), is likely to be an appropriate means of gathering spatially explicit information on fear of crime and avoidance behaviour. Golledge and Stimson (1997) argue that the surge of interest in behavioural research in human geography in the 1960s and 1970s stemmed from a desire to increase the geographer’s level of understanding of particular types of problems. This aligns well with understanding of the spatiotemporal nature of fear of crime. An understanding of how people develop cognitive maps, and how spatial cognition influences spatial choices and behaviour, is highly relevant to environmental criminology (Brantingham and Brantingham, 1993). In essence, cognitive mapping assists people in making spatial choices, such as determining which areas in which to commit crime or to avoid due to fear of crime (Brantingham and Brantingham, 1993; Downs and Stea, 1973). A cognitive map is a mental copy of one’s environment, featuring information about the relative spatial location, arrangements and properties of ‘phenomenon’ (Block, 1998; Downs and Stea, 1973; Sholl, 1996). Such phenomena include behaviourally relevant ‘landmarks’ that are visible reference points, like buildings, parks or street junctions

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(Nasar, 1998). Spatial cognition involves the attribution of denotative meaning, or object recognition, to these geographic phenomena (Nasar, 1998). By definition, cognitive mapping refers to the process by which people comprehend and respond to the world around them (Downs, 1977). The end product of the cognitive mapping process is a cognitive map (Golledge and Stimson, 1997) which is a cross-section representing the world at one instant in time and reflects the world as a person believes it to be (Downs, 1977). Space is not considered only in terms of the physical environment (Koskela and Pain, 2000). Activities, specific events and processes become associated with the environmental context in which they take place (Koskela and Pain, 2000; Valentine, 1989). For example, a laneway may be associated with drug dealers. Therefore, when shaping and recalling information stored in one’s cognitive map, a person is aware of the environment as having distinct social and physical attributes (Burnett, 1976; Downs and Stea, 1973). Thus, character plays a vital role in social cognition and functions as an effective cue in retrieving spatial information (Tversky and Taylor, 1998). Space and events in space are intimately connected with the perception of time (Block, 1998). Therefore, landmarks and objects often have associated temporal properties and relationships (Block, 1998). As part of spatial cognition, or spatiotemporal reasoning, the ‘appearance, change, and disappearance of things in space and over time’ is also considered (Couclelis, 1998). Taking this into account, the presence of darkness in a particular environment (represented by darkness rather than a measurement of time) can trigger new attributes to be associated with that environment. Using the above example, the drug dealers in the laneway during the day may move to another location at night. Cognitive mapping is not only shaped by the physical, social and temporal properties of space, but also by one’s mental state (Orleans, 1973). The mind is the home of a person’s emotions, attitudes, needs and desires. The process of evaluating an environment is a function of these factors (Burnett, 1976; Orleans, 1973). This evaluation involves judgement and the assigning of a connotative meaning to the different phenomena and social activities within that environment (Husserl, 1973; Nasar, 1998). Continuing the previous example, onlookers could perceive the drug dealers as threatening, thereby connoting risk, or as harmless. They would take appropriate behavioural action, such as adopting avoidance or protective measures, depending on their judgement. While assessing the possible courses of action and making a spatial choice,5 cognitive information will also be influenced by one’s past experiences, present beliefs and especially the future expectations concerning the outcome of such a decision (Burnett, 1976; Downs and Stea, 1973; Kitchin, 1996; Kaplan, 1973; Jeffery, 1971; Mennis, 2003). In circumstances where onlookers perceive the drug dealers to be threatening, the concept of risk becomes attached to that specific laneway and the person may consequently avoid it (Nelson et al., 2001). The laneway then signals the need for avoidance and becomes an anchor point, which

5 Spatial choice is a function of knowledge of one’s location, what is likely to occur, whether it will be good or bad and possible courses of action (Nasar and Jones, 1997).

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is similar to a landmark, only more personal and salient, in one’s cognitive map (Block, 1998; Couclelis et al., 1987). This avoidance behaviour therefore continues even in the absence of the original drug dealers. Thus, avoidance behaviour is part of an individual’s cognitive mapping process because it involves a response based on his/her perceived threat of crime. In conclusion, spatial behaviour is the result of the complex processes of spatial choice. Spatial behaviour6 and spatial choice are dependent on one’s cognitive map of the spatial environment (Burnett, 1976; Downs and Stea, 1973; Freundschuh, 1998) and is therefore a response to both the real and subjective worlds (Kitchin, 1996). However, despite the rational calculation involved in behaviour, inferences and spatial choices can be made without conscious thought (Nasar, 1998). Kitchin (1996) reviews the variety of techniques that can be used to gain information on the cognitive mapping process. These include asking respondents to draw a sketch map of an area (e.g. Walsh et al., 1981), estimate the distance and direction to locations (e.g. Day, 1976; Kirasic et al., 1984) or verbally describe a route or area (e.g. Vanetti and Allen, 1988).

The Beginning of Fear Mapping As mentioned above, cognitive mapping techniques have been successfully adapted to investigate fear of crime and develop fear mapping methodologies. The study of cognitive mapping originally involved evaluating environmental cognition by asking individuals to illustrate their mental maps of geographic regions, with landmarks, on paper. In line with this, Steinitz (1968) mapped ‘denotative meanings’, or people’s knowledge of a city (cited in Nasar, 1998). Later, environmental assessment became of interest where connotative meanings were mapped, or people’s feelings regarding places and activities in different areas of a city (Nasar, 1998). Newman (1972) created one of the first fear maps showing a site plan of designs that residents designated as dangerous. A year later, Gould produced a crude fear map of Philadelphia (cited in Nasar, 1998).7 In their various papers written approximately 15–20 years later, Fisher and Nasar made considerable contributions to the growth of fear mapping, the linking of certain environmental cues to fear of crime and the use of activity diaries (Fisher and Nasar, 1992, 1995; Nasar and Jones, 1997; Nasar et al., 1993). They conducted a number of micro-level behavioural studies investigating emotional levels of fear in university campus settings (e.g. Fisher and Nasar, 1992, 1995; Nasar and Jones, 1997). These studies assessed emotional levels of fear in area and

6 Spatial behaviour is ‘any form of human behaviour that involves or exhibits an interaction between the individual and one or more points in space’ (Louviere, 1976). 7 In 1976, Milgram and Jodelet also mapped perceived areas of danger in Paris (Nasar, 1998). Also, in 1977 Duncan (1997) mapped New York’s feared neighbourhoods (Oc and Tiesdell, 1997).

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time-specific situations (e.g. Fisher and Nasar, 1992, 1995) while walking a particular route at night (Nasar and Jones, 1997). They provide the best examples of behavioural approaches that have sought to understand the actual protective and avoidance behaviours that survey respondents adopted in relation to fear of crime. In their 1990 ‘observations of behaviour’ study, Fisher and Nasar (1992) observed pedestrian activity to determine if people avoid walking in or near areas they judged as unsafe. By examining the most heavily avoided sites, they concluded that people avoid low-prospect/high-refuge areas. In 1991, Nasar et al. (1993) extended this research. Respondents were asked to circle areas that they avoided on a map. These individual maps were then aggregated to produce a coarse hierarchical map of fear. This was then used in more site-specific analyses of the links between feelings of safety and concealment, prospect and escape. Fisher and Nasar (1995) slightly amended this method in their later study, wherein they asked respondents to rate their perceived level of safety in eight predesignated areas on the provided map. The results similarly showed definitively that hotspots of fear occurred at the micro level. Nasar and Jones (1997) again explored fear mapping by asking 26 female respondents to walk a specified route between 8:15 pm and 10:00 pm and to carry a hand-held tape recorder. Respondents were asked to record feelings of safety or unsafeness and any emotional reactions or feelings generated as a result of particular elements of the surrounding environment or situation in general. Sites where respondents felt unsafe were documented on a map and aggregated to show the spatial distribution of fear comments by percentage. One of the main advantages of these studies was the ability to assess levels of fear in relation to actual activities and overcome some of the limitations of broader, global measures of fear. One prospect of extending this approach lies in the use of activity diaries provided as a means to assess fear of crime on a larger scale and in relation to the actual activities of respondents.

Activity Diaries and Daily Routines Activity diaries are another behavioural geography technique and are one of four main methods used to collect time-budget data. Golledge and Stimson (1997) outline these methods. The first is a recall method where activities of some specified period in the past are recalled with as much precision as possible, regarding the location and time of activities. The second is a recall method where activities for some normal period are recalled. The third is the diary method where subjects are asked to keep a diary for a specified period of time. The fourth is a game-based method basically used to investigate changes in the contingencies of the decision-making environment and often used in the ‘post diary’ or post-interview situations. In the context of fear of crime, the diary method of data collection is likely to be the most appropriate, as it records the actual activities of people. The other methods, by incorporating hypothetical procedures increase the likelihood that fear of crime will be measured in situations not relevant to the actual behaviour of survey respondents.

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Farrall et al. (1997) describe a survey respondent who was listed as very worried about crime in a quantitative study but, in a qualitative study, he claimed only to be worried when out on the street and he came across a group of people (i.e. according to the authors’ measures he was a 1 at home, 4 or 5 when out on the streets). An activity-diary investigation of fear of crime can potentially overcome such limitations, as respondents can be asked about their emotional level of fear in relation to specific activities at specific times. A further potential advantage of using activity diaries to investigate fear of crime is that it will assess fear in situations that are part of the respondents’ daily routines. One could argue that the study that Nasar and Jones (1997) conducted, while overcoming some of the hypothetical issues associated with global measures of fear, still involved placing respondents in a situation that may not have been part of their daily routines. Time-space budgets can be used for a wide variety of purposes, following the processing of the data. The first step in processing time-space budgets generally involves the application of a classification scheme (Golledge and Stimson, 1997). Once classified, the data can be analysed to investigate specific issues in relation to the routines of respondents (e.g. Kwan, 1999, 2000a). Chadee and Ditton (2003) emphasize that the interaction between factors known to influence fear of crime is an important consideration of investigations of fear of crime. The authors use the example of the elderly only appearing to be more fearful if they live in large cities, are unmarried, live alone or are low-income earners and black. Activity-diary analyses have been shown to be sensitive to such interactions (e.g. Golledge and Stimson, 1997).

Geographic Information Systems and Fear of Crime Nasar (1998) proposed the use of GIS in fear mapping in order to increase accuracy. Given that recognition of the spatiotemporal nature of fear of crime is generally regarded as fundamental to any analysis of the phenomena (e.g. Nasar and Jones, 1997; Pain, 1997; NCAVAC, 1998; Thomas and Bromley, 2000), it is somewhat surprising that few studies have sought to use GIS in this area. By definition, a GIS contains a powerful set of tools which allows the collection, storage, retrieval, transformation and display of spatial data (Burrough and McDonnell, 1998). This data, or geographic information, is referenced to locations on the earth’s surface and not only includes the location of spatial objects, but also their attributes (Ding and Fotheringham, 1992; Martin, 1991). Mapping through GIS is therefore particularly useful when studying large and complex data with multiple attributes, where conventional inferential statistics and pattern recognition algorithms may fail (Kwan, 2000a). The use of GIS to model dynamic spatiotemporal phenomena is also well recognized (e.g. Egenhofer and Golledge, 1998; Maury and Gascuel, 1999, Kwan, 2000a, 2000b; Olsen and Doran, 2002). GIS is already widely used by police services to investigate patterns of criminal activity (e.g. Ashby and Longley, 2005; Baker and Wolfer, 2003; Bowers

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et al., 2004; Harries, 1999; Murray et al., 2001; Nelson et al., 2001; Ratcliffe and McCullagh, 2001; Russo, 2001; Weisburd et al., 2004; Yarwood, 2001). Murray et al. (2001) suggest that GIS and crime mapping software have been the most influential computer-based tools for developing techniques to explain the occurrence of criminal activity. However, the authors also note that there exist many potential applications of GIS in this area. Similarly, Harries (1999) predicts that GIS adoption by police departments will increase rapidly in future because the technology is simultaneously becoming cheaper and more powerful. One of the major advantages of GIS is that it has the potential to increase the efficiency with which police resources are allocated. Ratcliffe and McCullagh (2001) outline how crime mapping is often used to identify the extent of a crime problem and to target resources to deal with the problem. This approach, first developed through NYPD’s CompStat procedure (Bratton, 1995, 1996), has now become popular in other law enforcement agencies (Ratcliffe and McCullagh, 2001). Using GIS in a similar manner to identify concentrations of fear may enable police services and local communities to address fear of crime in the same focused manner. An area that has received little attention in the literature is the potential spatiotemporal relationship between fear of crime and actual victimization. Considering that many studies have established or suggested links between fear of crime, social disorder and serious crime (e.g. Kelling and Coles, 1997; Perkins and Taylor, 1996; Skogan, 1990; Taylor and Covington, 1993; Wilson and Kelling, 1982), a spatiotemporal analysis of such potential links would be a useful addition to a fear mapping study. Further, the so-called risk-victimization paradox arises from the frequent observation that people with the least fear are at greatest risk and those with the greatest fear are at least risk (e.g. Oc and Tiesdell, 1997; Reid et al., 1998; Warr, 1984). At the level of the individual, it is quite possible that people frequenting areas or times where their fear of crime is low but crime rates are high may be more susceptible to victimization. On a broader level, many of the suggested links between fear of crime and the actual occurrence of crime are largely based on the collective nature of avoidance behaviour. Areas of low natural surveillance resulting from avoidance behaviours adopted by members within a community are said to provide opportunities not only for disorder, but also for crime itself to become established (e.g. Kelling and Coles, 1997; Skogan, 1990). However, to date there are no tools or analytical techniques available for identifying and collating the actual areas that the public avoid due to their fear of crime. If this can be done successfully, it would provide a framework to compare the collective nature of avoidance behaviour to concentrations of crime. As with investigations into the spatiotemporal distribution of crime, GIS-based analyses may prove useful and potentially provide new insights into these areas. The focus of the two studies described in Chapters 6 and 7 is to investigate these issues in Wollongong and Kings Cross – two different settings, one a regional town and another a densely populated inner city area, within Australia.

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Chapter Review: A New Direction with Avoidance Mapping A review of the literature reveals that there are numerous problems with the commonly used cognitive and affective approaches to measuring fear of crime. Some of these limitations can be overcome by using behavioural measures and it is possible to strongly justify research into the area that is behaviour-based. First, if fear of crime is to be addressed on the basis of its most concerning influence on society, protective and avoidance behaviours must be taken into consideration. Second, avoidance-based behavioural measures are particularly applicable because they can be used in spatial investigations into fear of crime. Finally, spatial investigations and the use of techniques from behavioural geography hold the potential to provide new and useful information that cannot be gained through traditional statistical analyses. Such outputs are likely to be particularly useful for policy, planning and localized implementation of fear-reduction strategies. Despite these benefits, few researchers have conducted spatially explicit research into fear of crime. Those that have (e.g. Fisher and Nasar, 1992, 1995; Nasar, 1998) have strongly demonstrated utility of mapping fear at the micro level and have recommended the use of GIS in future applications. As such, there exists a genuine need and opening to ‘put fear on the map’ through the use of GIS and appropriate measurement techniques.

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Chapter 6

The Wollongong Study

The Goals of the Wollongong Study Links between social and physical disorder, crime and fear of crime have been areas of research interest for some time (e.g. Wilson and Kelling, 1982; Skogan, 1990; Kelling and Coles, 1997). One of the most influential studies into this area was the work of Wilson and Kelling (1982) who put forth a theory outlining a causal relationship between disorder, fear and crime. The theory has since been referred to as the ‘broken windows’ thesis or theory (e.g. Harcourt, 1998; Sampson and Raudenbush, 1999; Loukaitou-Sideris, 1999) and has had a significant bearing upon subsequent research and policy developments (e.g. Taylor and Covington, 1993; Tiesdell and Oc, 1998; Skogan, 1990; Bratton, 1995, 1996; Sampson and Raudenbush, 1999). Despite this, there have been few studies that have used a spatially explicit approach to investigate potential spatiotemporal links between crime, disorder and fear. Thus, ‘mapping out fear of crime’ holds the potential to deliver baseline data and a localized means of looking into questions such as 1. Can techniques from behavioural geography be successfully combined with GIS to investigate the avoidance and protective behaviours that people adopt in relation to their fear? 2. When and where are people afraid of crime? 3. Do hotspots of crime, fear and disorder overlap? If so, what implication does this have for reducing fear of crime among the CBD working population? 4. How do localized, behavioural measures of fear compare with global approaches? This chapter first presents some background information on Wollongong, including the evolution and structure of the CBD area, crime rates over the years prior to the study and the survey technique adopted for the project. It then moves onto describe the cognitive mapping technique that was used to look at collective patterns of avoidance behaviour across the CBD at different times of the working day. These patterns are subsequently compared to distributions of social and physical disorder and used as a framework to discuss the spatiotemporal implications of the B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_6,  C Springer Science+Business Media, LLC 2012

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broken windows thesis. The next section presents the findings from the activity diary analysis of protective behaviours and emotional levels of fear in relation to the daily routines of people working in the CBD. The study finished at the end of 2004 which proved to be fortuitous timing, as the Wollongong City Council was in the early stages of implementing a 5-year City Centre Revitalization Strategy (WCC, 2003) and developing a strategic vision, the ‘2020 plan’, which outlined goals that the city and broader community wanted to achieve in the short-medium term. This enabled key research findings from the Wollongong study to be used as a means of assessing the impact of proposed land use planning and design changes (Irwin et al., 2003). The results of the study were also integrated with a crime prevention and community safety plan (WCC, 2007).

Research Setting The study site for the project was the city of Wollongong, Australia, located approximately 80 km south of Sydney on the east coast of Australia (see Fig. 6.1).

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Wollongong is the ninth largest city in Australia and the third largest in New South Wales (WCC, 2003), with a population of 181,000 people and key industry sectors based around manufacturing, mining, technology research and education (IRIS, 2004; ABS, 2003). It is recognized as being the major city in the Illawarra region (WCC, 2003) with the smaller centres of Bulli to the north and Shell Harbour to the south. Wollongong developed in the second half of the nineteenth century as an important industrial city with an economy based on coal, steel, engineering and clothing (Gupy et al., 2000). The past two decades have seen a shift, with activity in these sectors declining and growth taking place in health, education, hospitality, retailing, property, information technology and business services (WCC, 2003). The shift was marked by massive job losses in the early 1980s in steel, coal, engineering and clothing industries which have resulted in Wollongong struggling to establish a new identity and economic development path (Gupy et al., 2000). Future economic growth and diversification is anticipated in areas such as retail and wholesale trade, transport, telecommunications, business services, metal industries, community services, entertainment, accommodation and personal services (WCC, 2003). This is in line with the broad aim of the Wollongong City Council to further develop the city centre as a regional hub for higher-order services and facilities (Olsen, 2003). An important part of Wollongong’s regional identity relates to its rugby league heritage. It has long been a nursery for high-calibre players in the National Rugby League (NRL) competition. In 1998 the Illawarra Steelers merged with the powerful Sydney-based side, the Saint George Dragons, to become the Saint George-Illawarra Dragons (Fagan, 2009). The city draws immense pride from the performance of the merged entity, in particular their recent grand final triumph in 2010. Visually, the city centre of Wollongong is dominated by three elements of the physical setting: the steel works at Port Kembla, the escarpment and the ocean (Irwin et al., 2003, Figs. 6.2 and 6.3). The setting of Wollongong, lying between the coastline and the escarpment is regarded as an important part of the city’s identity (WCC, 2003).

Fig. 6.2 View looking south from the city centre of Wollongong towards the steel works at Port Kembla

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Fig. 6.3 View looking west from the city centre of Wollongong towards the escarpment

Logic Behind Study Site Selection The selection of the CBD as a study site was based on a number of considerations. First, the logic used was similar to Bennett (1991), who chose a study site with relatively high rates of crime in order to make comparisons between crime and fear of crime. One of the primary aims of this study is to investigate potential spatiotemporal links between fear of crime and the actual occurrence of crime. Hence, the selection of a study site that had a significant crime problem was logical. Wollongong has a significant crime problem, which is outlined in more detail below. According to a number of authors (e.g. Sampson and Groves, 1989; Markowitz et al., 2001), communities that experience rapid ecological change are more likely to show increases in crime and fear of crime. Given the economic and social shifts that have occurred in the Wollongong region in recent times, an increase in crimerelated problems is not entirely unexpected. Further, such trends make Wollongong an interesting and appropriate study site for a spatiotemporal investigation of fear of crime, crime itself and social and physical disorder. Second, Wollongong is spatially confined by the Tasman Sea to the east and an escarpment to the west, meaning the population of Wollongong is relatively confined. As such, it is well suited to the development of a method to analyse the spatiotemporal nature of the fear of crime. Larger potential study sites such as the CBD of Sydney were harder to define in terms of spatial extent. Other issues such as the likely commuting distance of respondents were a consideration. In terms of collecting activity diary data, larger study sites are likely to be more complex, with commuters travelling over greater distances (e.g. Kwan, 1999; 2000a, 2000b). Correspondingly, a large activity diary dataset would be needed in order to adequately analyse a study site of large proportions such as the CBD of Sydney. This was beyond the scope of this study. Thus, the smaller CBD of Wollongong was more suited to this project.

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The Central Business District of Wollongong The Central Business District (CBD) of Wollongong is well defined and compact, comprising office blocks, several shopping mall complexes and public facilities such as parks within an area of approximately 1.5 × 0.8 kilometres (see Fig. 6.4 below). Historically, the city centre of Wollongong has always been structured around Crown Street (Lee, 1997). In 1948 it stretched from approximately 100 metres west of the rail line to Corrimal Street. By 1986, the city centre had spread considerably with growth taking place along Auburn Street and east towards Keira Street. There had also been expansion north of Crown Street. By 2003, the broader city centre was still concentrated around Crown Street between the rail line and Corrimal Street. However, it had expanded south to link up with some of the areas of the city centre that were isolated in 1986. Some growth was also evident along the northern and eastern edges of the city centre area (WCC, 2003). Commuters to the CBD show a strong dependency on motor vehicle transport, with a high proportion of people travelling to work by car (67%) with only 5.8% travelling by public transport (ABS,

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2003). A structure plan for the current city centre by the Wollongong City Council (WCC, 2003) describes six general areas of the CBD: – – – – – –

Retail core Commercial offices The area west of the railway The area south of Burelli Street The area east of Corrimal Street Public parks

The current city core or CBD is centred around Crown Street and occupies a smaller area. Figure 6.4 above shows the six main areas of the city centre as well as the extent of the current CBD. The retail core is centred around the Crown Street Mall area which is located along Crown Street between Keira Street and Kembla Street. The Crown Street Mall provides a wide range of shopping facilities with several large department stores and over 250 specialist stores (WCC, 2003). There is no vehicle access along Crown Street between Keira and Kembla Streets or along Church Street between Burelli and Market Streets. These areas are dominated by paved street zones and public seating, and entertainment facilities (see Figs. 6.5 and 6.6). Some parts of the retail core lie outside the Crown Street Mall area. Further west along Crown Street are a range of cafés, large department stores and some shops. An older mall complex, the Piccadilly Shopping area, is situated on the other side of the rail line. The retail core areas along Keira Street and along Crown Street east of Kembla Street are dominated by a wide range of restaurant and entertainment facilities, including a successful café culture (WCC, 2003; Irwin et al., 2003). Woolworths and Aldi supermarkets are located on the corners of Burelli and Kembla and Stewart and Corrimal Streets. The commercial office area comprises primarily the City Council, Commonwealth and NSW Government offices. The majority of office-based activity is centred around the commercial office area but some smaller commercial offices are spread south towards Swan Street and on Regent Street near

Fig. 6.5 Public seating area in the Crown Street Mall at the junction of Crown and Church streets

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Fig. 6.6 Paved walkway and seating area in the Crown Street Mall at the junction of Crown and Keira Streets

the railway. Outside the commercial office area, few office buildings exceed a height of two or three floors (WCC, 2003). The area to the west of the rail line is dominated by hospital- and medical-related uses and medium-density housing. This area is also a major entry point into the CBD through Crown Street (WCC, 2003). The area south of Burelli Street contains a mix of residential, commercial and light industrial uses. The light industry and commercial offices are primarily in medium sized, two-story buildings of mixed quality (WCC, 2003). The area east of Corrimal Street is primarily residential. Exceptions to this are several large car dealerships located around the intersection of Corrimal and Crown Streets and a sports stadium and entertainment area located along Harbour Street. The main public parks in the CBD area are McCabe Park and Pioneer Park. McCabe Park is a large urban park with street edges to the north and east. The street edges have public parking facilities. The park itself contains grassed areas, playgrounds, ornamental gardens and memorial structures. Pioneer Park was formed over a former rest park and is essentially a public garden with a neighbourhood function. Market Square is a park on the site of an old market place. It is regarded as a site of heritage significance (WCC, 2003).

Crime and Fear of Crime in Wollongong Previous research has found crime and fear of crime to be major problems in the Wollongong area. A project looking specifically at fear of crime among women found that up to 75% of women surveyed experienced moderate to great fear (WCC, 1999). Similarly, a more recent community values survey found that crime was one of the issues residents were most concerned about (IRIS, 2002). Crime Trends in the Illawarra Region, 2001 An indication of the type of crime problems experienced in the broader Wollongong region can be gained by examining analyses of recorded crime data in the Illawarra

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Statistical Division (SD). An analysis of recorded crime trends between January 2000 and December 2001 in NSW by the Bureau of Crime Statistics and Research (Allen, 2002) showed the following trends. For the offence categories break and enter – non-dwelling, the Illawarra SD showed a significant upward monthly trend (up by 14%). The rate of break and enter-non-dwelling in the Illawarra SD is above the state average. Similarly, for the offence category motor vehicle theft, the Illawarra SD showed a significant upward trend (up by 27%). Only one SD outside Sydney recorded a rate of motor vehicle theft in 2001 that was higher than the state average in 2001 and this was the Illawarra SD. Further, Allen (2002) highlights that the Illawarra SD along with the Central Western Sydney SD have two of the highest rates of motor vehicle theft in the state. One other offence category, steal from person, showed a significant upward trend in the Illawarra SD. Steal-from-person offences were up by 41% during this period in the Illawarra SD but this was lower than the state rate in 2001. The only offence category showing decrease was for robbery with a firearm. This category was down by 25% during this period in the Illawarra SD but this did not represent a significant downward trend. Crime Hotspots at the LGA Level in NSW, 2002 The NSW Bureau of Crime Statistics and Research (BOSCAR, 2004) provide an analysis at the local government area (LGA) for crime hotspots in 2002. The analysis is based on ranking the top 25 LGAs for nine selected offences (assault, assault – DV related, sexual assault, robbery, break and enter – dwelling, break and enter – non-dwelling, motor vehicle theft, steal from motor vehicle and steal from person). The Wollongong LGA is listed as being a crime hotspot in 2002 for the offence categories of break and enter – dwelling, break and enter – non-dwelling, motor vehicle theft and steal from person. Table 6.1 below shows the four offences for which the Wollongong LGA is listed as being a crime hotspot in 2002. For break and enter – dwelling, Wollongong LGA is ranked 11th in the state with 3041 offences at a rate of 1615 per 100,000 of the population. For break and enter – nondwelling, the Wollongong LGA is ranked 23rd in the state with 1786 offences at a rate of 948.6 per 100,000 of the population. For motor vehicle theft, the Wollongong LGA is ranked 22nd in the state with 1676 offences with a rate of 890.2 per 100,000 Table 6.1 Offences for which the Wollongong LGA was recorded as being in the top 25 LGAs in NSW for 2002 (based on BOSCAR, 2004) Offence

Rank

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of the population. For the offence of steal from person, the Wollongong LGA is ranked 21st in the state with 490 offences at 260 per 100,000 of the population.

Methods There were four key components of the research methods which are listed below and described in detail in the sections following. (a) Fear of crime survey and analysis. This component was based on a survey of 260 people working in the CBD area, conducted in May–June 2002. (b) Disorder assessment. The disorder assessment included physical and social components: – 1 Physical disorder assessment conducted in June 2002. – 8 Social disorder assessments conducted in June–July 2002. (c) Spatial analysis of crime data. This component was based on crime data sourced through the NSW Police Service for Wollongong LGA. (d) Combinatory spatial analysis. A framework was developed to examine potential spatiotemporal links between crime, disorder and fear. Fear-of-Crime Survey and Analysis A five-part survey was designed to investigate the spatiotemporal nature of fear of crime in the CBD area of Wollongong. The survey was based on a voluntary sample. This involved the surveyor approaching numerous businesses in the CBD area and, where consent was given, conducting a face-to-face interview with respondents. Due to ethical requirements of human research, only people older than 18 years were interviewed and respondents were informed that the survey was in relation to fear of crime. Further, it was made clear to respondents that the study would not involve the dissemination of any personal information and that the analytical procedure would result in generalized results. The five sections of the survey were as follows: (1) (2) (3) (4) (5)

General factors known to influence fear of crime Questions on emotional levels of fear in relation to activity diaries Questions on protective behaviour in relation to activity diaries Vignettes assessing emotional levels of fear in hypothetical situations Cognitive mapping of avoidance behaviour

A pilot study was conducted to pretest the survey procedure. The results from the first pilot study indicated that more questions could be incorporated into the survey procedure, as the 15 minutes allocated to the interview process were not being fully utilized.

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General Factors Known to Influence Fear of Crime In general, a number of factors are known to influence fear of crime. These include age, sex, income status, housing type, previous victimization, ethnicity, perceptions of safety, media influence, length of time in neighbourhood, integration with neighbourhood and confidence in the police (e.g. Covington and Taylor, 1991; Box et al., 1988; Borooah and Carcach, 1997; Farrall et al., 2000). Age and sex were recorded as categorical variables. In the case of age, survey regulations dictated that only people over the age of 18 years could be interviewed. Age was recorded in 5-year blocks, starting at 18. Income status was assessed using the measure suggested by Farrall et al. (2000). This was based on the question, ‘How easily would you be able to find $600 suddenly, without resource to a bank loan?’ The responses to this question are based on a five-point Likert scale ranging from 1 equating to very easy to 5 equating to impossible. The measure by Farrall et al. (2000) used a figure of 200 British pounds. At the time of the survey, this figure was equivalent to approximately 600 Australian dollars. The type of housing was recorded after Borooah and Carcach (1997) according to whether respondents were renting from a government housing commission, were owner-occupiers or non-owner-occupiers. Ethnicity was assessed according to whether respondents considered themselves as coming from an English-speaking background. People who identified themselves as coming from a non-Englishspeaking background were assumed to be from an ethnic minority. Perception of media-related issues has been found to contribute to fear of crime (Box et al., 1988). This was recorded using the question, ‘How do you rate media coverage of crimerelated issues in the Wollongong region?’ Responses were based on a five-point Likert scale ranging from very understated to very overstated. Previous victimization was recorded according to Borooah and Carcach (1997). This involved asking respondents if they had been victims of certain crimes in the past 12 months. The crimes recorded were deliberate use of a weapon, attack or assault, threats of force or violence, theft and attempted theft and deliberate damage to property or tampering by vandals or thieves. The length of time that respondents had been living in their current neighbourhood was recorded according to whether they had been there less than 1 year, 1–2 years, 3–5 years and more than 5 years. Community integration was measured after Covington and Taylor (1991) who asked respondents, ‘Suppose some kids were spray painting a building near where you work. Do you think you or any of your neighbours would call the police?’ The response to this question was ‘Yes’ or ‘No’. For the purposes of this study, the phrasing of the question related to the work environment, as opposed to the neighbourhood environment in Covington and Taylor (1991). The purpose of changing the phrasing was related to the focus of this study on the working population of the CBD of Wollongong. Finally, the standard global measure of fear was also recorded (i.e. how safe do you feel when walking alone in the area around your home after dark?). The phrasing of this measure and the responses associated with it were based on Borooah and Carcach (1997).

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Questions on Emotional Levels of Fear and Protective Behaviour in Relation to Activity Diaries Activity diaries were seen as a means of building on Fisher and Nasar’s (1992, 1995) and Nasar and Jones’s (1997) micro-level behavioural investigations of fear in specific spatial and temporal contexts. The activity diary approach is particularly similar to the procedure used by Nasar and Jones (1997) in their investigation of emotional levels of fear among 26 female respondents on the Ohio State University campus. The researchers asked the respondents to walk a specific pedestrian route on the campus which passed through a variety of landscapes. Respondents were given a hand-held tape recorder and asked to record emotional feelings of safety while walking the prescribed route. By using this approach, the authors were able to investigate site- and context-specific levels of emotion-based fear. In this study, the use of activity diaries provided a similar but slightly different framework for investigating emotional levels of fear. First, survey respondents were asked to record their activities using the diary method (Golledge and Stimson, 1997) and subsequently interviewed. During this follow-up interview respondents were asked about their emotional levels of fear in relation to the activities and times recorded in their diaries. The phrasing of the question used to assess emotion-based fear was based on Farrall et al. (2000). Respondents were also asked if they were adopting any protective behaviours in relation to the activities recorded in their diaries. The protective measures of having a dog, carrying something for defence, relying on self-defence training and ‘other’, as described by Krahn and Kennedy (1985) were recorded. Also included were categories relating to respondents who were making sure they were accompanied by a friend, carrying a mobile phone to call someone if they felt in danger and adopting no protective measures. The diary method involves the subject keeping a dairy for a specified period of time (Golledge and Stimson, 1997). In this study, the diary was for a period of one day during the week on which the respondent was working. The time of the diary was from 02:00 to 24:00. Prescribed time intervals of half-hour periods were used (e.g. Kwan, 2000a). Appendix 2 shows the template for the diary and the accompanying instruction sheet given to survey participants. Vignettes Assessing Emotional Levels of Fear in Hypothetical Situations Farrall et al. (2000) used a series of vignettes to assess emotional levels of fear in a range of hypothetical situations. The vignettes, originally based on the study by Van der Wurff et al. (1989), were used by Farrall et al. (2000) to describe their sample in terms of sensitivity to fear when exposed to the same set of hypothetical situations. This approach is used in this study, for the same purposes. Cognitive Mapping of Avoidance Behaviour Respondents were provided with a map of the CBD area and asked if they avoided any areas because they were afraid of being robbed, beaten or attacked during and

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after work hours. The map used to define the area of the CBD was based upon previous surveys conducted by the Wollongong City Council. The phrasing of the question, as with the activity diary analysis of emotion-based fear was designed to specifically tap the fear of personal crime (e.g. Farrall et al., 2000). Where respondents indicated that they avoided areas after work hours, they were asked to clarify the time that they started to avoid particular areas. Respondents that indicated that they avoided any areas because of their fear of crime were also asked to specify how hard they tried to avoid those areas on a scale of 1–5 (1 indicating very hard, 5 indicating not hard at all). Assessing the degree to which people avoided any area they indicated on the maps was designed to provide a weighting measure to be used when collating all of the maps. The same technique was used to assess avoidance behaviour in the neighbourhoods of the respondents. Respondents were asked to mark where they lived on a map of the area. A circle of 1.6 kilometres in diameter was then drawn around their home. This figure was taken from the general phrasing of the global measure, which typically relates to areas within one mile of respondents’ homes. GIS-Based Technique for Collating the Cognitive Maps of Avoidance The maps showing the areas avoided by the individual respondents were digitized and overlaid using ArcView GIS, to create maps of collective avoidance for different times of the day (see Fig. 6.7 below). The digitizing process was based upon tracing the outline of the areas that respondents indicated they avoided due to their fear of crime. Using a similar approach to create a simple index model (e.g. Chang, 2010), the degree of avoidance was used to weight the areas avoided by individuals when combined to create collective maps. Collective avoidance maps were made for the hours between 9 am and 5:30 pm, 5:30 pm and 7 pm and after 7 pm. This

Different degrees of avoidance and different times

Avoidance grids for individual respondents, weighted by how hard people tried to avoid specific areas, were combined for different time groupings to form collective avoidance maps

Fig. 6.7 Combinatory process used to collate individual avoidance grids

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temporal segmentation was designed to investigate collective avoidance in relation to the general daily routines of people working in the CBD of Wollongong. Many of the cognitive maps that survey respondents drew to describe the areas they avoided because they were afraid of being robbed, beaten or attacked outlined areas which showed fine-scale detail. In some cases, areas indicated were street corners or seating areas no more than 10 metres in length or width. In order to minimize the loss of this fine-scale information on avoidance behaviour, an output cell size of 10 metres was selected when converting the input vector files to rasterbased coverages for the combinatory procedure. Disorder Assessment Physical Disorder A physical disorder assessment was conducted using a method similar to that of Sampson and Raudenbush (1999) who assessed disorder on a block-by-block level in Chicago. The disorder assessment in this study, however, was conducted on foot as opposed to by vehicle in the Sampson and Raudenbush study (1999). Table 6.2 below shows the different types of disorder recorded in the assessment. A weighting system was designed to gain a more accurate impression of how disorder was likely to impact upon the public. The weighting system was based upon recording the level of the different types of disorder as well as a weighting factor. At each of the blocks, the level of different types of disorder was assessed on a scale of 1–5 based on how extensive they were. Figures 6.8 and 6.9 show examples of the values given to different levels of graffiti. The weighting factor was based upon how Table 6.2 Types of physical disorder recorded in the physical disorder assessment (after Sampson and Raudenbush, 1999) and the weighting factor given to each type of disorder Disorder number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Type of physical disorder

Weighting factor

Cigarettes or cigars in street gutter Garbage or litter in street or sidewalk Empty beer bottles visible in street Tagging graffiti Gang graffiti Political message graffiti Graffiti painted over Abandoned cars/glass from smashed windscreens Abandoned/boarded-up houses Lack of exterior maintenance Vandalism to buildings Vandalism to public structures Condoms on sidewalk Needles/syringes/methadone capsules on sidewalk Evidence of homeless people

1 (not very visible) 2 (moderately visible) 3 (visible) 4 (quite visible) 4 (quite visible) 4 (quite visible) 2 (moderately visible) 5 (very visible) 5 (very visible) 3 (visible) 5 (very visible) 5 (very visible) 3 (visible) 5 (very visible) 5 (very visible)

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Fig. 6.8 An example of graffiti in Crown Lane, Wollongong, that was given a level value of 3

Fig. 6.9 An example of graffiti in McCabe Park, Wollongong, that was given a level value of 5 (i.e. very extensive)

visible the different types of disorders are. More visible types of disorder such as abandoned cars or buildings were given a higher factor weighting than cigarettes in the street gutter (see Table 6.2 for weighting factors). The latitude and longitude were taken down for each point where disorder data were recorded. Maps showing the distribution of disorder were created, one based upon simply the presence of

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disorder and another based upon the weight multiplied by the level of disorder for each data point. Social Disorder As with the physical disorder assessment, the social disorder assessment was based on techniques outlined by Sampson and Raudenbush (1999). In their study, social disorder was assessed in a vehicle moving at approximately five miles per hour down every street in the study area. Trained observers recorded the presence or absence of adults loitering or congregating, drinking alcohol in public, peer groups with gang indicators present, public intoxication, adults fighting or arguing in a hostile manner, sale of drugs and prostitution on the street. Sampson and Raudenbush (1999) noted that temporal variation in social disorder is a particular problem in attempts to systematically observe it. The authors emphasize that the probability of finding adults loitering or drinking, of finding peer gangs hanging out, or of seeing prostitution or drug deals will depend on the time of day in which a face block is observed. In their study, the authors felt that they were able to account for temporal variation within their sample because face blocks were assessed between the hours of 07:00 and 19:00. However, it could be argued that this approach, by not recoding social disorder after 19:00, does not sufficiently account for potential temporal variation. For example, Thomas and Bromley (2000) describe how the decentralization of retail, office and leisure functions in British cities has been central to segmenting the use of facilities in these areas. The abrupt curtailment of functions related to the business day is followed by the ‘five o’clock flight’, where the working population leaves the city centre. Following this, the city is relatively abandoned for several hours until the onset of the ‘pub and club’ culture, which arrives later. The authors note that the ‘pub and club’ culture is frequently associated with types of social disorder such as heavy drinking, drug use and late-night violent incidents. In an attempt to capture this type of temporal variation, social disorder in this study was assessed for all hours of the day. Social disorder assessments were conducted between 06:00–12:00, 12:00–18:00, 18:00–24:00 and 00:00–06:00. Further, social disorder assessments were conducted on weekdays and the weekends. The purpose of this was to capture potential variation in social disorder related to the ‘pub and club’ culture. In a similar vein to the suggestion made Sampson and Raudenbush (1999), the probability of finding social disorder related to the ‘pub and club’ culture is likely to be greater on weekends, when these facilities receive their greatest use. As the focus of this project was to use GIS-based techniques, the precise location of incidents of social disorder was recorded at the time of observation. The social disorder assessments were conducted in a vehicle driven at approximately five miles per hour, except for the areas of Crown Street that had no vehicle access and the major pathway through the McCabe Park. In these areas, social disorder was assessed on foot. The combination of systematically assessing social disorder in a vehicle and on foot was suggested by Stephens (1999). The types of social disorder assessed in this project are shown below in Table 6.3.

110 Table 6.3 Types of social disorder recorded in the physical disorder assessment (after Sampson and Raudenbush, 1999)

6 Disorder number 1 2 3 4 5 6 7

The Wollongong Study

Type of social disorder Noise Truancy Loitering Public insults Prostitution Panhandling Adults fighting or arguing in a hostile manner Public drinking/public drunkenness Loud parties Street harassment of women Street harassment of elderly Drug dealing Homeless or mentally ill people Public urination

8 9 10 11 12 13 14

A number of authors describe certain venues that act as generators of social disorder, such as bars, clubs and pornographic theatres (e.g. Skogan, 1990; Wikstrom, 1995). The distribution of these types of venues was recorded for the CBD area of Wollongong and a hotspot map was also created using the same procedures as for social and physical disorder. The types of venues associated with social disorder that were recorded are shown below in Table 6.4. Spatial Analysis of Crime Data A map showing the distribution of general crime hotspots (i.e. based upon all types of recorded crime) was produced from geocoded crime data collected by the NSW Police Service for the Wollongong Local Area Command. The geocoding accuracy associated with the crime data was 55%, meaning recorded crime offences could be matched to an address and given a geographic coordinate for 55% of the data. Typically, the geocoding process delivers results where 25–75% of the target database records can be matched and given a geographic coordinate (Drummond, 1995). The database consisted of approximately 65,000 crimes which occurred

Table 6.4 Types of venues recorded that are associated with social disorder

Disorder number

Type of social disorder

1 2 3 4 5 6 7

Bars Bottle shops Night clubs Pornographic theatres Massage parlours Adult shops Methadone dispensaries

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between March 1998 and August 2002. The hotspots of physical disorder and crime were mapped using the kernel density based function within the Crime Analysis Extension of ArcView 3.2 using search radii of 50 and 100 metres respectively. There are few guidelines regarding the selection of search radii for kernel density functions (Levine, 2002). In general, narrower search radii deliver results showing a finer mesh density estimate with well-defined ‘peaks’ and ‘valleys’. A larger bandwidth will lead to a smoother distribution and, therefore, less variability between areas (Levine, 2002). The selection of search radii of 50 metres for the creation of social and physical hotspot maps in this study was based on the likely impact of disorder on fear of crime and general visibility conditions in the CBD area of Wollongong. In most cases, visibility in the CBD of Wollongong is fairly restricted, with sight lines rarely extending beyond 50–80 metres (see Figs. 6.10 and 6.11 below). It was assumed that signs of disorder would be relatively difficult to detect beyond 50 meters in most areas. Hence, search radii of 50 meters were likely to capture a scale of detail relevant to people using public space in the CBD area. The selection of a broader search radius of 100 meters for the creation of the crime hotspot map was based on the desire to obtain a more generalized indication of the

Fig. 6.10 Typical view in the Crown Street Mall area, with sight lines of approximately 50 metres

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Fig. 6.11 Typical view looking west at the junction of Crown and Keira streets, with sight lines of approximately 80 metres

distribution of crime but to retain a scale of detail that was suitable for comparison with the maps outlining collective avoidance behaviour and social and physical disorder. Combinatory Spatial Analysis: Framework for a Spatiotemporal Comparison of Collective Avoidance Concentrations, Social and Physical Disorder and Crime In the context of disorder decline models such as the broken windows theory, the degree to which collective avoidance concentrations overlap crime or disorder hotspots is of interest. According to the theory, there is the potential for crime or disorder to expand into areas of poor natural surveillance over time (Kelling and Coles, 1997). However, there have been few, if any, spatiotemporal examinations of the links suggested between fear of crime, disorder and crime itself. The figures below provide an interpretive framework for the spatiotemporal links suggested by the broken windows theory. Figures 6.12a, b show the presence of a crime or disorder hotspot and the subsequent collective avoidance of the hotspot and the areas surrounding it. According to the broken windows theory, the low level of natural

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a)

b)

c)

d)

Direction of expansion for crime or disorder hotspot

Direction of expansion for collective avoidance concentration Area representing crime or disorder hotspot

Fig. 6.12 Diagrammatic representation of the potential expansion of a crime or disorder hotspot in relation to a collective avoidance concentration

surveillance resulting from the collective avoidance in the areas adjacent to the crime or disorder hotspot creates the opportunity for the crime or disorder hotspot to expand (Fig. 6.12b) and occupy a larger area (Fig. 6.12c). The logical public response to this would be an expansion of the collective avoidance around the now larger crime or disorder hotspot (Fig. 6.12d). Figures 6.13a–d illustrate a situation where a collective concentration envelopes two crime or disorder hotspots. In this case, the direction of expansion of the crime or disorder hotspots into the areas of poor natural surveillance is towards each other (Fig. 6.13b). Over time, the crime or disorder hotspots could potentially join to create one continuous hotspot. As with the situation in Fig. 6.13d, the area of collective avoidance in this situation would expand in relation to a larger crime or disorder hotspot. Figures 6.14a–d illustrate another likely scenario where there is a partial overlap of a collective avoidance concentration and a crime or disorder hotspot. A situation such as this could exist where a social or physical barrier exists on one side of the crime or disorder hotspot. In this case, the expansion into the area of low natural surveillance creates a larger crime or disorder hotspot in one direction (Fig. 6.14b–c). In turn, this could result in a larger collective avoidance concentration in a direction away from the growth of the crime or disorder hotspot. The following process was used in order to examine the degree of overlap between collective avoidance concentrations and hotspots of disorder and crime. First, the maps for collective avoidance concentrations at different times (9 am– 5:30 pm, 5:30–7 pm, after 7 pm) were combined to create a generalized map of collective avoidance. In turn, this was then broken into two discrete classes, one

114

6 a)

b)

c)

d)

The Wollongong Study

Direction of expansion for crime or disorder hotspot

Direction of expansion for collective avoidance concentration Area representing crime or disorder hotspot

Fig. 6.13 Diagrammatic representation of the potential expansion and eventual linking of two crime or disorder hotspots in relation to a collective avoidance concentration

a)

c)

b)

d)

Direction of expansion for crime or disorder hotspot

Direction of expansion for collective avoidance concentration

Area representing crime or disorder hotspot

Fig. 6.14 Diagrammatic representation of the potential expansion of a crime or disorder hotspot in relation to a partially overlapping collective avoidance concentration

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indicating the areas most strongly avoided, the other indicating areas that were not strongly avoided. This generalized output then formed the basis with which to compare generalized collective avoidance behaviour to the other elements of the broken windows theory, namely social and physical disorder and crime. The map showing the collective distribution of the strongly avoided areas was overlaid with separate maps showing the distribution of social and physical disorder and crime. In the case of social disorder, generalized maps were created in a similar manner used to combine the collective avoidance maps for various times. Social disorder maps were combined to create two general maps, one showing the distribution of social disorder on weekdays and another on weekends. This involved combining separate social disorder maps for the various times (6 am–12 noon, 12 noon–6 pm, 6 pm–12 midnight, 12 midnight–6 am) for weekdays and weekends. The display of the maps was designed to highlight the degree to which the generalized collective avoidance concentrations overlapped the separate maps showing the distribution of social and physical disorder and crime. This was achieved by making the map of the generalized collective avoidance concentrations semi-transparent.

Results Sample Characteristics Figure 6.15 shows the age distribution of survey respondents. It can be seen that the age categories for 18–23-year-olds and 30–35-year-olds are the best represented

40

Frequency

30

20

10

0 18−23 24−29 30−35 36−41 42−47 48−53 54−59 60−65 above66

Age Category

Fig. 6.15 Age distribution of respondents

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with 40 and 43 respondents or 17.1% and 18.4% of the sample. For the age categories between 30–35 years to 36–41 years, the number of respondents was 36 and 35, representing 30.3% of the sample. Approximately 23.9% of the sample fell into the age categories of 42–47 years and 48–53 years; 10.3% of the sample fell into the age categories for 54–59 years, 60–65 years and above 66 years. In terms of the sex distribution of survey respondents, the majority of the sample were females (n = 169, 72.2%) and a minority males (n = 65, 27.8% of the sample). This bias towards women is similar to other studies using voluntary samples. For example, Nasar and Jones (1997) conducted a voluntary sample to assess fear on a night-time walk at the Ohio State University campus. In their study, 26 females were surveyed. In a study by Nair et al. (1993) three-quarters of the respondents were women. The majority of the sample (n = 201, 85.9%) were from English-speaking backgrounds. A smaller number (n = 33, 14.1%) were from non-English-speaking backgrounds. Figure 6.16 shows the income distribution of respondents. It can be seen that the majority of the sample (n = 156, 66.6%) indicated that they would find it very easy or easy to access $600 suddenly without access to a bank loan; 17.9% of the sample indicated they may be able to access $600 without access to a bank loan, while 36 respondents (15.4% of the sample) indicated that this was not easy or impossible (Fig. 6.16). With respect to the housing type for the survey respondents, most of the sample (n = 149, 63.7%) were owner-occupiers. A smaller number (n = 79, 33.8%) were non-owner-occupiers while relatively few (n = 6, 2.6%) were from government housing commissions. The majority of the sample (n = 121, 51.7%) had been

100

Frequency

80

60

40

20

0 1 2 3 4 5 1 = Very Easy, 2 = Easy, 3 = Maybe, 4 = Not Easily, 5 = Impossible

Fig. 6.16 Income distribution of respondents

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100

Frequency

80

60

40

20

0 1

2

3

4

5

1 = Very Understated, 2 = Quite Understated, 3 = Accurate, 4 = Quite Overstated, 5 = Very Overstated

Fig. 6.17 Media perception of crime-related issues in Wollongong among respondents

living in their current neighbourhood for over five years. Similar proportions (16.7% n = 41 and 17.5%, n = 39) of the sample had been living in their current neighbourhood for 3–5 years or 1–2 years respectively; 13.6% (n = 32) had been living in their current neighbourhood for under one year. Figure 6.17 shows the media perception of crime-related issues in Wollongong among survey respondents. Approximately 64.5% (n = 151) of the sample felt that crime-related issues in Wollongong were very understated or quite understated; 26.0% (n = 62) of the sample felt that crime-related issues in Wollongong were accurately represented, while 8.9% (n = 21) of the sample felt that crime-related issues were quite overstated. None of the respondents thought that crime-related issues were overstated. Figure 6.18 shows the experience of victimization among respondents in the 12 months prior to the time of interview: 40.2% (n = 94) of the sample had not experienced any type of crime. The proportions of the sample that had experienced the crimes of deliberate use of a weapon, attack or assault and threats of force or violence were 0.4% (n = 1), 1.7% (n = 4) and 4.7% (n = 11) respectively. Compared to the sample of Borooah and Carcach (1997), from which the measures of victimization used in this study were taken, the experience of crimes against the person is lower in this sample (8.8%). The proportion of respondents in the study by Borooah and Carcach (1997) that had been victims of a personal crime was 14%. Higher proportions of the sample had experienced theft or attempted theft 17.1% (n = 40) and vandalism 12.4% (n = 29). Approximately 23.5% (n = 55) of the sample had experienced more than one crime in the 12 months prior to the time of survey.

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100

Frequency

80

60

40

20

0 0 1 2 3 4 5 0 = none, 1 = Deliberate use of a weapon, 2 = attack or assault,

6

3 = Threats of force or violence, 4 = Theft or attempted theft, 5 = Vandalism, 6 = more than one crime

Fig. 6.18 Experience of victimization among respondents

Figure 6.19 shows the responses of the survey respondents to the global fear-ofcrime measure used in the survey (i.e. ‘How safe do you feel when walking along after dark in the area around your home?’). It can be seen that 54.7% (n = 128) of the sample indicated a degree of fear in this situation (not very safe or not safe at all); 45.3% (n = 106) of the sample indicated that they felt fairly safe or very safe. Table 6.5 below shows the responses of the sample to the vignettes of Van der Wurff et al. (1989). In parentheses are the responses to the same vignettes from the studies by Van der Wurff et al. (1989) and Farrall et al. (2000). Farrall et al. (2000) used the vignettes to make a general comparison between their sample and that of Van der Wurff et al. (1989) on the basis that differences in answers would reflect variations in sensitivities. Farrall et al. (2000) concluded that their sample was slightly more fearful, as it showed higher levels of fear to most vignettes was slightly more fearful. Using this logic, the sample in this study is slightly more fearful than the samples of Van der Wurff et al. (1989) and Farrall et al. (2000). In response to the question regarding the reporting of spray-painting offences in their neighbourhood (i.e. ‘Suppose some kids were spray painting a building near where you work. Do you think you or any of your neighbours would call the police?’), most of the sample (83.8%, n = 196) indicated that they would report spray painting in their neighbourhood to the police, while 16.2% (n = 38) said they wouldn’t. Fig. 6.20 shows the responses of the sample to the question regarding how well the police were thought to be performing their jobs. It can be seen that a relatively small proportion of the sample 2.6% (n = 6) felt that the police were performing their jobs very well; 42% (n = 98) of the sample felt that the police were

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Frequency

90

70

50

30

10 1 2 3 4 1 = Very Safe, 2 = Fairly Safe, 4 = Not Very Safe, 5 = Not Safe at all

Fig. 6.19 Answers to global measure of fear amongst respondents

Table 6.5 Degree of safety in relation to the vignettes of Van der Wurff et al. (normal parentheses represent the sample of Farrall et al. (2000), square parentheses, the sample of Van der Wurff et al. (1989)) Vignette

Meana

Standard deviation

Modal answer

Doorbell Car Party Bus stop Telephone

3.00 (3.05) [2.35] 1.95 (2.53) [3.27] 1.38 (1.73) [3.77] 2.06 (2.53) [2.30] 3.06 (3.18) [1.96]

1.24 (1.28) [1.29] 0.95 (1.15) [1.23] 0.66 (0.86) [1.08] 1.00 (1.14) [1.14] 1.18 (1.19) [1.32]

4.00 not very afraid 2.00 quite afraid 1.00 very afraid 2.00 quite afraid 4.00 not very afraid

a Note:

Values are based on the Likert scale where 1 = very afraid to 5 = not afraid at all

performing their jobs quite well, 30.3% (n = 98) indicated that they did not know; 22.6% (n = 53) felt the police were not performing their jobs well and 2.6% (n = 6) the police were not performing their jobs well at all.

The Spatiotemporal Distribution of Collective Avoidance Concentrations Table 6.6 below shows the percentages of the sample that were adopting avoidance behaviour during the day and after dark for situations relating to their neighbourhood as well as the CBD area. It can be seen that the percentage of the sample adopting avoidance behaviour in the CBD area after dark is greater than for the neighbourhoods of respondents after dark (81.2% compared to 64.1%). It can also

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100

Frequency

80

60

40

20

0 1 2 3 4 5 1 = Very Well, 2 = Quite Well, 3 = Don't know, 4 = Not Very Well, 5 = Not Well at all

Fig. 6.20 Responses to the question regarding how well the police are thought to be performing their jobs Table 6.6 Percentages of respondents adopting avoidance behaviour in their neighbourhoods and the CBD area

Area

Percentage of respondents adopting avoidance behaviour during the day

Percentage of respondents adopting avoidance behaviour at night

CBD Neighbourhood

39.31 18.38

81.20 64.10

be seen that in both the CBD area and the neighbourhoods of respondents the percentage of the sample adopting avoidance behaviour increases substantially after dark. GIS-based analysis of avoidance behaviour in the neighbourhoods is not presented, as the sample was spatially too dispersed to adequately assess collective avoidance in the neighbourhood context. Figures 6.21, 6.22 and 6.23 show the collective avoidance hotspots for different times of the day for the sample of working people from the CBD of Wollongong. In general it can be seen that the collective avoidance hotspots are well defined in that the areas avoided are relatively specific. The distribution also changes noticeably over time. Between 09:00 and 17:30 (Fig. 6.21) there are two major hotspots, one centred around the McCabe Park area and another towards the western side of Crown Street. There are two smaller hotspots further down Crown Street, in what is a mall area. Between 17:30 and 19:00 the hotspots expand considerably. The most noticeable changes are that the hotspot in the west Crown Street area extends east to

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250

Keira S

treet

Meters

Crown Street

McCabe Park

Burelli S

treet

Legend Minor Roads Main Roads

Degree of Avoidance Low Intensity

High Intensity

Fig. 6.21 Areas of the CBD avoided between 09:00 and 17:30 in relation to fear of crime

nearly join up with a large hotspot that has developed in the mall area. After 19:00, the hotspots recede to the areas around McCabe Park, west Crown Street and the mall area. The hotspots at this time are generally smaller, except for the hotspot in the mall area. Between 17:30 and 19:00 (Fig. 6.22) the collective avoidance concentration centred around the Piccadilly complex in west Crown Street extends eastwards to occupy most of Crown Street to effectively link up with a collective avoidance concentration that has formed in the Crown Mall area, which extends from the junction of Crown and Keira Streets to the junction of Kembla and Crown Streets. A smaller collective avoidance concentration has also formed around Globe Lane within the Crown Street Mall complex. The collective avoidance concentration centred around the McCabe Park area has grown in area to occupy most of the park and part of Burelli Street to the north. After 19:00 (Fig. 6.23), the collective avoidance concentrations have retreated in extent compared to the concentrations between 17:30 and 19:00. The concentrations centred around McCabe Park and Piccadilly areas occupy smaller areas than at the other times (Figs. 6.21 and 6.22). However, the collective avoidance concentration in the Crown Street Mall area has expanded, particularly around the junction of Market and Crown Streets. The extent of this concentration covers most of the paved areas in the Crown Mall that have no vehicle access. The collective avoidance concentration

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250

Keira S tr

eet

Meters

Crown Street

McCabe Park

Burelli S

treet

Legend Minor Roads Main Roads

Degree of Avoidance Low Intensity

High Intensity

Fig. 6.22 Areas of the CBD avoided between 17:30 and 19:00 in relation to fear of crime

centred around Globe Lane that is apparent between 17:30 and 19:00, is no longer present after 19:00. The Spatiotemporal Distribution of Physical and Social Disorder and Crime Figure 6.24 below shows the distribution of physical disorder hotspots within the CBD of Wollongong using the weighted locational data. It can be seen that the main hotspots are located along the west and middle Crown Street areas, the McCabe Park area and one hotspot on Keira Street. In general, the hotspots of physical disorder are smaller and more spatially confined than the collective avoidance hotspots shown in Figs. 6.21, 6.22 and 6.23. Figure 6.25 shows the ranking of different types of disorder after the weighting system was applied. It can be seen that tagging graffiti, garbage or litter and empty beer bottles in the street were the most highly ranked types of disorder. Graffiti was ranked the highest, showing it to be the most dominant type of physical disorder within the disorder hotspots shown in Fig. 6.24. Figures 6.26, 6.27, 6.28 and 6.29 show the spatial distribution of social disorder for different times on weekdays and weekends. Figure 6.26 shows concentrations of social disorder on weekdays during the day (i.e. between 06:00 and 18:00). It can be seen that there are far fewer hotspots of social disorder than there are for

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Keira S

treet

Meters

Crown Street

McCabe Park

Burelli S

treet

Legend Minor Roads Main Roads

Degree of Avoidance Low Intensity

High Intensity

Fig. 6.23 Areas of the CBD avoided after 19:00 in relation to fear of crime

physical disorder (Fig. 6.24). On weekdays between these times, concentrations of social disorder are mainly around the northern end of McCabe Park and nearby on Burelli Street. An intense concentration is also evident along Denison Street near the junction of Crown Street. Other, less intense hotspots are distributed 100–200 metres to the north and south of Crown Street. Figure 6.27 shows the distribution of social disorder hotspots on weekdays at night (i.e. between 18:00 and 06:00). It can be seen that social disorder hotspots are dispersed widely across the CBD area between these times. Only one intense hotspot is evident along Keira Street, past the junction of Victoria Street. A cluster of less intense hotspots are concentrated along Crown Street between Kembla and Corrimal Streets. Also apparent is the lack of social disorder hotspots around the McCabe Park and Piccadilly areas between these times. Figures 6.28 and 6.29 show the spatial distribution of social disorder hotspots for weekends during the day (i.e. between 06:00 and 18:00) and at night (i.e. between 18:00 and 06:00). It can be seen that during the day that a cluster of social disorder hotspots are concentrated around the western end of the Crown Street Mall area between Market and Keira Streets. The cluster of hotspots extends along Globe Lane and east along Burelli Streets. A hotspot of medium intensity is located around the Piccadilly area between these times. Between these times, on the weekend, there are fewer hotspots in the minor streets surrounding Crown Street than there are

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Keira S treet

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Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Core avoidance hotspot Intensity of Physical Disorder Hotspot Low Intensity

High Intensity

1800 1500 1200 900 600

Abandoned cars/glass from … Evidence of homeless people

Graffiti painted over

0

Garbage or litter in street Empty beer bottles visible in street Cigarettes or cigars in street gutter Abandoned/boarded up houses Lack of exterior maintenance Vandalism to buildings Vandalism to public structures

300 Tagging graffiti

Weighting multiplied by level

Fig. 6.24 Physical disorder hotspots, based upon weighted data

Fig. 6.25 Ranking of different types of disorder recorded in the physical disorder assessment

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Corrima

250

l Street

Keira S treet

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Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.26 Social disorder on weekdays during the day (i.e. between 6 am and 6 pm)

during the day on weekdays (Fig. 6.26). Figure 6.29 shows the distribution of social disorder hotspots on weekends at night (i.e. between 18:00 and 06:00). It can be seen that intense hotspots are located within the Crown Street Mall area and along Keira Street near the junction with Market Street. Smaller clusters of hotspots are centred around the Piccadilly area and the eastern end of Crown Street near the junction of Corrimal Street. A number of low-intensity hotspots are spread around the minor streets to the east and west of Keira Street and along Crown Street. Figures 6.30 and 6.31 show generalized (i.e. grouped) maps of social disorder on weekdays and weekends. These maps were a composite of social disorder for weekdays and weekends for day and night (i.e. all times). In general it is evident that social disorder hotspots on weekdays are more dispersed than on weekends. A noticeable difference is the lack of social disorder in the Crown Street Mall area during weekdays, whereas on weekends this area shows the greatest concentration of social disorder. On weekdays, hotspots of social disorder are evident in the Piccadilly area, the McCabe Park area, along Burelli Street towards Corrimal Street. Another cluster of hotspots stretches northwards along Keira Street. On weekends, social disorder hotspots are concentrated in the Crown Street Mall complex, around the junction of Keira and Market Streets and around the Piccadilly area. Figures 6.32 and 6.33 show the frequency of the different types of social disorder recorded during the assessments for weekdays and weekends respectively. It can be

Keira S treet

250 Meters

Crown Street

McCabe Park

The Wollongong Study

Street

6

Corrima l

126

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.27 Social disorder on weekdays at night (i.e. between 6 pm and 6 am)

Corrima

Keira S

Meters

l Street

treet

250

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.28 Social disorder on weekends during the day (i.e. between 6 am and 6 pm)

Research Setting

127

Corrima l

Meters

Street

Keira S treet

250

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.29 Social disorder on weekends at night (i.e. between 6 pm and 6 am)

Corrima

Keira S

Meters

l Street

treet

250

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.30 General hotspots of social disorder on weekdays (day and night grouped)

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Street

Keira S treet

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Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.31 General hotspots of social disorder on weekends (day and night grouped)

seen that on weekdays the most frequent type of social disorder recorded were loitering and noise (23 and 12 observations respectively). Public drinking, public insults and homeless people were recorded at low frequencies. On weekends (Fig. 6.33) it can be seen that, in general, more types of disorder were recorded and the frequencies were higher. Loitering, public drinking and noise were the most frequently recorded types of disorder (38, 22 and 14 observations respectively). On weekends, low frequencies (between 1 and 8 observations) were recorded for public insults, homelessness, adults fighting or arguing in public, street harassment of women and public urination. For some types of disorder, namely truancy, prostitution, panhandling, loud parties, street harassment of elderly and drug dealing, no observations were recorded on weekdays or weekends. Figure 6.34 shows the hotspots of clubs, bars, adult entertainment stores and other venues frequently associated with generating social disorder. It can be seen that the most evident concentration is along Crown Street between Keira Street and the rail line. Another concentration around the junction of Market and Keira Streets is also evident. A cluster of smaller hotspots is located near the junction of Corrimal and Crown Streets. The frequency of the different types of venues is shown in Fig. 6.35. Figure 6.36 below shows the general crime hotspot map for Wollongong between March 1998 and August 2002. It can be seen that there are several distinct hotspots within the CBD area, the largest of which is located in the eastern end of Crown

Research Setting

129

25

Frequency

20 15 10 5

Public urination

Drug dealing

Street harassment of elderly

Street harassment of women

Loud parties

Adults fighting or arguing in a hostile manner

Panhandling

Prostitution

Truancy

Homeless or mentally ill people

Public insults

Public drinking/ public drunkenness

Noise

Loitering

0

Fig. 6.32 Types of social disorder on weekdays

Street. Others are located on Keira Street between Burelli and Crown Streets, the northern part of Keira Street and in the western area of Crown Street. The Degree of Overlap Between Collective Avoidance Concentrations, Physical and Social Disorder and Crime The following figures show the overlap between general areas of collective avoidance and the primary elements of the broken windows theory, namely physical disorder, social disorder and crime itself. The general levels of avoidance were created by combining the avoidance grids for the various times (i.e. between 09:00 and 17:30, between 17:30 and 19:00 and after 19:00). The areas that were most heavily avoided were then selected and used to create a grid representing general avoidance. Figure 6.37 below shows the degree of overlap between general areas of avoidance and crime hotspots. It can be seen that there is a strong degree of overlap between the crime hotspot centred around the Piccadilly area and the general collective avoidance concentration. There is partial overlapping of collective avoidance concentrations and crime hotspots along the northern fringe of McCabe Park. In the Crown Street Mall area, there is partial overlapping with crime hotspots at the junctions of Crown and Keira streets and Crown and Kembla streets.

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40

Frequency

30

20

Drug dealing

Street harassment of elderly

Loud parties

Panhandling

Prostitution

Truancy

Public urination

Street harassment of women

Homeless or mentally ill people

Adults fighting or arguing in a hostile manner

Public insults

Noise

Public drinking/ public drunkenness

0

Loitering

10

Fig. 6.33 Types of social disorder on weekends

Figure 6.38 shows the degree of overlap between weighted physical disorder hotspots and general areas of collective avoidance. There is a strong degree of overlap between general areas of collective avoidance and the intense physical disorder hotspots around the Piccadilly area and east along Crown Street near the junction with Auburn Street. Also apparent near the Piccadilly area is the partial overlapping of the general collective avoidance area and two intense hotspots located along Gladstone Avenue and along Crown Street, approximately 100 m west of the junction with Gladstone Avenue. In the McCabe Park area there is a strong degree of overlap between the general area of collective avoidance and the cluster of physical disorder hotspots clustered along the western and central parts of the park. There is only slight overlapping of general collective avoidance of the Crown Mall area and physical disorder hotspots. Figure 6.39 shows the degree of overlap between general areas of collective avoidance and social disorder hotspots for weekdays. It can be seen that there is a strong degree of overlap between general areas of collective avoidance and the intense social disorder hotspot located near the Piccadilly area at the junction of Denison Street and Crown Street. There is also a strong degree of overlap between the general collective avoidance concentration and social disorder in the McCabe

Research Setting

131

Meters

Corrima l Street

Keira S treet

250

Crown Street

Burelli S

treet

McCabe Park

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.34 Social disorder hotspots for bars, clubs and adult entertainment stores

10 Number

8 6 4

Night clubs

Bottleshops

Pornographic theatres

Methadone dispensaries

Adult shops

Massage parlors

0

Bars

2

1

2

3

4

5

6

7

Fig. 6.35 The number of clubs, bars and nightclubs in the CBD area

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6

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Corrima l Street

Keira S treet

250

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CrownStreet

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Intensity of Crime Hotspot Low Intensity

High Intensity

Fig. 6.36 General crime hotspots within the CBD of Wollongong

Park area. There is relatively little overlap between the general collective avoidance concentration and social disorder on weekdays in the Crown Mall area. Figure 6.40 shows the degree of overlap between general areas of collective avoidance and hotspots of social disorder on weekends. As with the situation on weekdays, there is a strong degree of overlap between the general collective avoidance concentration and the intense social disorder hotspot in the Piccadilly area. In the Crown Street Mall area, there is a strong degree of overlap between general areas of collective avoidance and the cluster of intense social disorder hotspots. There is only a partial degree of overlap between the general collective avoidance concentration and social disorder around the McCabe Park area. Figures 6.39 and 6.40 show that there is no overlapping of the social disorder hotspots concentrated around the junction of Keira and Market Streets and the general collective avoidance concentration. Table 6.7 below summarizes the degree of overlap between the general collective avoidance concentration and hotspots of crime, social disorder on weekdays, social disorder on weekends and weighted physical disorder.

Research Setting

133

Meters

Corrima l Street

Keira S treet

250

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Core avoidance hotspot Intensity of Crime Hotspot Low Intensity

High Intensity

Fig. 6.37 The degree of overlap between general areas of avoidance and crime hotspots

Discussion of Spatial Outputs Potential Constraints on Social Interaction Resulting from Collective Avoidance Behaviour The use of cognitive mapping to investigate avoidance behaviour, and the subsequent GIS-based analysis, provides new insights into some of the issues that have been central to debates on fear of crime. One issue which has become an increasingly important aspect of such debates is the degree to which fear of crime impedes people’s freedom of movement (Pantazis, 2000). Liska et al. (1988) suggested that, to some extent, fear of crime is a social problem because it is assumed to constrain social interaction. The maps of collective avoidance for the CBD of Wollongong (Figs. 6.21, 6.22 and 6.23) show how fear of crime is likely to be constraining social interaction among the working population in the city. The implications of reduced social interaction in each of the key collective avoidance areas identified in Table 6.7 are discussed below. The Piccadilly Area In the Piccadilly area, the constant avoidance of the shopping complex and surrounding vicinity, at all times of the day, suggests that the working population in

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Meters

Corrima l Street

Keira S treet

250

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Crown Street

McCabe Park

Burelli S

treetx

Legend Coastline Main Roads Minor Roads Rail Line Core avoidance hotspot Intensity of Physical Disorder Hotspot Low Intensity

High Intensity

Fig. 6.38 The degree of overlap between general areas of avoidance and weighted physical disorder hotspots

the CBD is effectively divided between people east and west of the rail line. The barriers to pedestrian movement along the rail line leave only one main pathway between west Crown Street and east Crown Street (Olsen, 2003). This pathway is the passage of Crown Street over the rail line. The strong avoidance of the Piccadilly area indicates that the majority of people in the CBD are reluctant to use this main pathway along Crown Street. This has a range of implications, the most apparent being the loss of potential social interaction between the working populations east and west of the rail line. Further, people west of the rail line, by being reluctant to utilize the main pathway along Crown Street are restricted in their ability to access the wider range of facilities and services available in the Crown Central Mall area. In this sense, the collective avoidance concentration in the Piccadilly area is a social barrier likely to reduce cohesion within the CBD community. This supports the findings of (Markowitz et al., 2001) who found fear to reduce cohesion on a broader scale. Gibson et al. (2002) suggest that social integration and community cohesion are important factors in terms of stabilizing or improving neighbourhood conditions. Thus, if the collective avoidance of the Piccadilly area continues, it is unlikely that conditions will improve and may in fact deteriorate over time. The actual centre of collective avoidance, the Piccadilly shopping complex and surrounding streets, is highly likely to be experiencing a substantial loss of potential customers for the businesses located in the area. This supports, and provides

Research Setting

135

Corrima

Keira S tr

Meters

l Street

eet

250

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Core avoidance hotspot Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.39 The degree of overlap between general areas of avoidance and social disorder hotspots for weekdays

concrete visual evidence for, arguments that the avoidance behaviours prompted by fear of crime must inevitably have an economic cost because people avoiding an area remove themselves as consumers (Oc and Tiesdell, 1997; Warr, 2000). The consistent avoidance of the area may also be creating greater opportunities for crime and disorder. Nodes of transport, such as rail stations, are often centres for certain types of disorder and crime (e.g. Loukaitou-Sideris, 1999). The close proximity of the Piccadilly centre to the rail station, in combination with poor natural surveillance associated with collective avoidance behaviour is likely to be providing favourable conditions for loitering, drug dealing and for prospective break-and-enter offenders to examine the area at their leisure during daylight hours. In terms of a policing response, the Piccadilly area is one where collective avoidance, social and physical disorder and crime itself show a strong degree of overlap. As such, a direct involvement of police in this area to control elements of disorder is appropriate. The McCabe Park Area The collective avoidance concentration in the McCabe Park area is similar to the one centred around the Piccadilly complex, in that it shows a constant avoidance of the area throughout the day by the public. Logically, the park should be used by

136

6

Meters

Corrima l Street

Keira S treet

250

The Wollongong Study

Crown Street

McCabe Park

Burelli S

treet

Legend Coastline Main Roads Minor Roads Rail Line Core avoidance hotspot Intensity of Social Disorder Hotspot Low Intensity

High Intensity

Fig. 6.40 The degree of overlap between general areas of avoidance and social disorder hotspots for weekends Table 6.7 Degree of overlap between different types of disorder, crime and collective avoidance concentrations at the three main centres of collective avoidance Degree of overlap with elements of disorder and crime Location

PDA

SDA1

SDA2

Crime

Piccadilly Mall McCabe Park

Strong Weak Strong

Strong Weak Strong

Strong Strong Weak

Strong Medium Medium

PDA physical disorder assessment, SDA1 social disorder assessment on weekdays, SDA2 social disorder assessment on weekends

people working in and around the Crown Street Mall area during lunch- and workbreak times, as it is the closest park to the mall area. The strong avoidance of the McCabe Park suggests that this is currently not happening. The amenities within the park designed for public use – seating areas, playgrounds and memorial sites – are likely to be underutilized. The social disorder assessments showed a consistent presence of public drinking and loitering in the park during the week. As with the Piccadilly area, the poor natural surveillance in the park may be creating favourable conditions for the types of disorder currently present, as well as serving to reinforce the avoidance of the area by the general public.

Research Setting

137

At the conclusion of the Wollongong study, the McCabe Park area was earmarked for redesign, in light of its current lack of use by the public (WCC, 2003; Irwin et al., 2003). The suggested changes to the park revolved around building on existing features in the park, improving lighting and the creation of seating areas that were intimate in nature. Changes to the park edge focus on strengthening the transition from street to park (Irwin et al., 2003: 74). In light of the collective avoidance of the park by the public, these suggested landscape design initiatives were inappropriate. The creation of seating areas of a more intimate nature would serve to reduce the potential for natural surveillance and as such, would be likely to discourage, rather than encourage, greater public use of the park. Further, the creation of secluded seating areas would be likely to create greater opportunities for the types of social disorder present in the park, namely loitering and public drinking. The suggested improvements to lighting may have results similar to the fear-reduction strategy described by Nair et al. (1993) in Glasgow, Scotland, where the authors concluded that the changes in lighting had simply turned a poorly lit bad area into a well-lit bad area. Some of the changes proposed to streets surrounding McCabe Park also seemed unlikely to prove beneficial in terms of reducing collective avoidance of the park area. For example, Irwin et al. (2003) suggested relocating current parking facilities in the median strip of Church Street where it abuts McCabe Park, to some of the residential streets joining Church Street such as Ellen and Bank Streets. Irwin et al. (2003) proposed that, following the relocation of parking, the median strip on Church Street be fully replanted with vegetation. While this was designed to build on the boulevard nature of Church Street where it abuts McCabe Park, it would most likely enhance the secluded nature of the park area and reduce the potential for natural surveillance from one side of the street to the other and into the park itself. The relocation of parking areas to Ellen and Bank Streets would also have had additional impact in terms of pedestrian activity. At the time of the Wollongong study, Church Street provided one of the main parking areas in the CBD. The daily movement of commuters who parked along Church Street between their work area and where they parked their cars gave rise to pedestrian movement, and hence natural surveillance around the park area before and immediately after work hours. The collective avoidance of the park by the public suggests that if an alternative route to the parking area were provided, it would be more heavily utilized. The relocation of parking facilities to Ellen and Bank Streets could result in commuters moving along Kembla Street when moving between their vehicles and their work areas, instead of along Church Street. This in turn would reduce pedestrian activity along Church Street and the resulting natural surveillance around the park. According to the broken windows theory and the logic presented in Figs. 6.12, 6.13 and 6.14, lower natural surveillance along the eastern edge of McCabe Park could create greater opportunities for social disorder currently persisting in the centre of the park and the eventual expansion of the collective avoidance concentration. The possible negative consequences of inappropriate landscape design initiatives in the McCabe Park area could have longer-term implications as well. The structure plan for the Wollongong city centre (WCC, 2003) advocates strategies that build on the current trend in Australian cities towards residential development on the fringe of core city areas. The structure plan specifically suggests a long-term increase in

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The Wollongong Study

residential densities around and to the east of McCabe Park. If the shorter-term landscape design strategies in and around McCabe Park served to increase social disorder and collective avoidance as suggested above, they could jeopardize the success of the longer-term residential development proposals. Investors are less likely to involve themselves in an area characterized by social and physical disorder (Skogan, 1990). This places added emphasis on the selection of an appropriate short-term land use design strategy for the park. These observations, and others outlined for the Piccadilly and Crown Street Mall area, were presented at a number of workshops and strategic meetings that were part of the Wollongong City Council’s City Centre Revitalisation Plan (WCC, 2003). The concluding section of this chapter discusses how the core findings from the Wollongong study were integrated with a Crime Prevention and Community Safety Plan initiated in 2007 (WCC, 2007). The Crown Street Mall Area The collective avoidance of the Crown Street Mall area is different from that centred around the McCabe Park and Piccadilly areas. The collective avoidance concentration around the Crown Street Mall shows greater spatial and temporal variation. Evident in the collective avoidance concentration in this area between 17:30 and 19:00 is the fact that most people are avoiding the open walkways in the mall area, namely Globe Lane and the paved section of Crown Street between Keira and Kembla Streets. The strong increase in collective avoidance of this area following work hours appears to be a strong spatial representation of the five o’clock flight described by Thomas and Bromley (2000). There are some senses in which the rapid departure of people from the CBD area may act to reinforce the five o’clock flight. If people leaving their work areas are confronted with a near-vacant mall area, or people departing the area quickly, it is unlikely to create conditions under which they will want to stay after work hours in the CBD area. Perhaps adding to the five o’clock flight on weekdays is the presence of social disorder that occurs in the mall area on weekends. The overlaying of generalized collective avoidance areas for weekdays showed a high degree of overlap with social disorder hotspots on weekends in the Crown Street Mall. The cause for this disorder may relate to the mall area providing a gathering point for the ‘pub and club’ culture on weekends. The hotspot map of pubs and nightclubs showed concentrations on either side of the mall along Crown Street. It is possible that the mall, being well lit and located in between the concentrations of clubs, creates a convenient gathering point for people moving between the various clubs. According to the broken windows theory, the presence of disorder prompts avoidance of such areas (Wilson and Kelling, 1982; Kelling and Coles, 1997). It may be that people working in the CBD are aware of the social disorder that is present on weekends and this may contribute to their avoiding of the area during the weekdays after work hours. In this sense, the involvement of police to control or limit the presence of social disorder in the mall area on weekends may be of importance. As with the Piccadilly and McCabe Park areas, a number of landscape design changes were also proposed for the Crown Street Mall (Irwin et al., 2003). These

Research Setting

139

changes largely centred on physical changes to structures within the mall and aimed at improving pedestrian flow. In the main, these suggestions were likely to be potentially useful in terms of increasing natural surveillance. However, it was suggested that these initiatives should be combined with social measures that aim to address the five o’clock flight and collective avoidance of the mall area after work hours. Possible relevant social measures could be for the Wollongong City Council and mall management committee to encourage activities relevant to the working population of the CBD. The types of activities that are likely to be relevant to the CBD community include open-air coffee houses and staggered closing times for shopping venues. A number of such initiatives were established in 2007 as part of the Crime Prevention and Community Safety Plan (WCC, 2007). They are discussed in more detail in the final sections of this chapter regarding police–community partnerships and fear-reduction strategies. In order to gain further context and insights into the influence of fear of crime on the daily routines and behaviour of people working in the CBD of Wollongong, an activity diary analysis was also conducted as part of the study. The next section presents the techniques used and the key results from the activity diary analysis.

Activity Diary Analysis Data Preparation Typically, the first stage involved in the analysis of diary data is the classification of activities (Golledge and Stimson, 1997). The basis of classification stems largely from the focus of the research (e.g. Kwan, 2000b; Keuleers and Wets, 2001). In this study, the primary reason for using the activity diary approach was to examine protective behaviour and emotion-based fear in relation to specific situations in the daily routines of people working in the CBD of Wollongong. The classification of activities, therefore, focused on grouping the diary data according to the time of day and the commuting nature of the sample. As with other studies (e.g. Kwan, 2000b), the classification process involved a number of assumptions. It was assumed that the general activity pattern of respondents involved leaving their home, travelling to the CBD in a vehicle (bus, car or train), walking from the point of departure from the vehicle to their work area, work-based activities and the reverse sequence to return home. In addition to this, it was assumed that respondents also engaged in a number of recreational and mandatory activities. The recreational grouping included activities such as walks, bike rides, social outings and participation in sports. The mandatory grouping included activities such as dropping off or picking up children from school, shopping, appointments with a doctor and attending university or TAFE classes. Three relatively specific groupings were made, one for work breaks (e.g. lunch or scheduled breaks) and another for travelling to the bank and at the bank itself. The groupings for activities at home, work, travelling in a vehicle, travelling on foot and recreational activities were further split in relation to standard business hours (09:00–17:00). For the home grouping, it was assumed that most respondents

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Table 6.8 The 16 categories resulting from the classification procedure applied to the activity diary data Situation

Number of observations

Home before 08:30 Home between 08:30 and 17:30 Home after 17:30

231 106 229

Work before 09:00 Work between 09:00 and 17:00 Work after 17:00

119 232 135

Travelling in vehicle before 17:00 Travelling in vehicle after 17:00

221 199

Travelling by foot before 17:00 Travelling by foot after 17:00

164 102

Travelling to bank At bank Work break

17 30 86

Mandatory activities Recreational activities before 17:30 Recreational activities after 17:30

46 41 53

would have to leave their homes by 08:30 in order to get to work by 09:00 and would not be home before 17:30. For the recreational grouping, it was assumed that in situations where respondents were engaging in recreational activities after work hours, these would generally take place after 17:30. Following the classification procedure, 16 categories were created. The categories are shown below in Table 6.8. In order not to bias the results, only one observation per respondent was used for each of the categories. In cases where there was more than one observation per respondent in a particular category, an average level of emotion-based fear was taken. For example if a respondent was at work from 09:00 to 15:00 and showed an emotional level of fear of 4 at work before 12:00 and 3 after 12:00, for the situation ‘work between 09:00 and 17:00’, the respondent was given an average emotional level of fear of 3.5. For protective behaviours, the dominant type of protective behaviour was assigned to the observation. The process of assigning one observation per respondent in a category reduced the total number of observations from 10,211 to 2012 observations. Table 6.8 shows the number of observations in each of the categories and Table 6.9 shows the different categories of protective behaviour recorded. Protective behaviour was assessed by asking respondents if they were adopting any of the measures shown in Table 6.9 while engaging in different activities listed in their diaries. In a similar vein, emotional levels of fear were assessed by asking respondents how afraid they were of being robbed beaten or attacked while undertaking the different activities they had recorded.

Research Setting

141 Table 6.9 Coding system for protective behaviours

Protective behaviour  1 = Making sure you were accompanied by a friend  2 = Carrying something to defend yourself  3 = Relying on self-defence training  4 = Having a dog with you  5 = Carrying a mobile phone to call someone if you felt in danger  6 = Other, please specify: . . . . . . . . . . . . . . . . . . . . . . . .  7 = Not doing anything in particular to protect yourself  8 = More than one protective behaviour

Emotional Levels of Fear and Protective Behaviour in Relation to Daily Routines – General Results Table 6.10 shows the average level of emotion-based fear, percentage of respondents showing a degree of fear, percentage of respondents adopting a protective behaviour and percentage of respondents adopting more than one protective behaviour for each of the situations resulting from the classification of the activity diary data. In general the average levels of emotion-based fear do not indicate a definite degree of fear (i.e. less than 3). The only situation where the average level of emotion-based fear for all respondents was lower than 3 was while they were travelling to the bank (2.71). In all situations where the activity types were further segmented by time (i.e. home, work, travelling by vehicle, travelling on foot and recreational activities), average levels of emotion-based fear showed lower values (i.e. more fearful) with later times. For example, the average level of emotion-based fear at home between 08:30 was 4.71, between 08:39 and 17:30 it was 4.60 and after 17:00 it was 4.48. The percentage of respondents showing a definite degree of fear in the different situations varies considerably. The situations where the highest percentages were recorded were travelling to the bank (58.82%), travelling on foot after 17:00 (45.10%), at work after 17:00 (23.7%) and at the bank itself (20.0%). The situations where the lowest percentages were recorded were at home before 08:30 (3.90%), at home between 08:30 and 17:30 (4.71%), at home after 17:30 (6.55%), travelling by vehicle before 17:00 (3.17%) and recreational activities after 17:30 (3.78%). As with average levels of emotion-based fear, the percentage of respondents showing a degree of fear increases with time of day for the situations segmented by time. The exception to this pattern is for recreational activities after 17:30, where the percentage of respondents showing a degree of fear was lower than for recreational activities before 17:30 (i.e. 3.78% compared to 12.20%). The percentage of respondents adopting a protective behaviour in the different situations shows less variation than the percentage of respondents showing a degree of fear. In general, 40–50% of respondents adopted protective behaviours in the various situations. The highest percentages were recorded for situations where respondents were travelling to the bank (76.5%), travelling on foot after 17:00 (73.5%) and travelling in a vehicle after 17:00 (60.1%). The lowest percentages

86

46

41

53

Work break

Mandatory activities

Recreational activities before 17:30

Recreational activities after 17:30

164 102

Travelling by foot before 17:00 Travelling by foot after 17:00

30

221 199

Travelling in vehicle before 17:00 Travelling in vehicle after 17:00

At bank

119 232 135

Work before 09:00 Work between 09:00 and 17:00 Work after 17:00

17

231 106 229

Home before 08:30 Home between 08:30 and 17:30 Home after 17:30

Travelling to bank

Number of observations

Situation

4.56

4.30

4.33

3.78

12.20

13.04

16.30

20.0

58.82

14.20 45.10

3.17 9.55

12.61 16.40 23.7

3.90 4.71 6.55

41.5

41.5

34.7

52.9

53.3

76.5

48.2 73.5

49.3 60.1

40.4 44.8 48.1

46.3 50.0 46.7

Percentage of respondents adopting a protective behaviour

12.2

4.8

8.7

8.0

10.0

17.6

7.9 22.5

9.5 16.1

10.1 10.3 11.1

6.9 8.5 11.8

Percentage of respondents adopting more than one protective behaviour

6

3.84

3.47

2.71

4.00 3.10

4.48 4.22

4.00 3.90 3.61

4.71 4.60 4.48

Average level of emotion-based fear

Percentage of respondents showing a degree of fear (i.e. <3)

Table 6.10 Average levels of emotion-based fear and percentages of respondents showing a degree of fear, adopting a protective measure and adopting more than one protective measure for the 16 situations arising from the classification of the activity diary data

142 The Wollongong Study

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were recorded for situations where respondents were engaged in mandatory activities (34.7%), at work before 09:00 (40.4%), recreational activities before 17:30 (41.5%) and recreational activities after 17:30 (41.5%). The percentage of respondents adopting more than one protective behaviour in the different situations was generally between 8 and 12%. The situations where the percentages were highest were while respondents were travelling on foot after 17:00 (22.5%), travelling to the bank (17.6%) and travelling in a vehicle after 17:00 (16.1%). Summary of Results of Activity Diary Analysis The general results from the activity diary analysis reveal that, when investigated in relation to the daily routines, the percentage of respondents showing a degree of emotion-based fear varies considerably. In most situations the percentage of respondents showing a degree of fear was relatively low, between 4 and 15%, but in certain situations this was as high as 59%. The highest percentages of respondents showing a degree of fear were for situations when they were travelling on foot after 17:00, travelling to the bank, at the bank or at work after 17:00. The highest percentages of respondents adopting a protective behaviour (60–77%) or more than one type of protective behaviour (16–23%) were recorded for situations where they were travelling in a vehicle after 17:00, travelling on foot after 17:00 or travelling to the bank. The situations that were segmented by time of day showed increasing percentages of respondents indicating a degree of fear or adopting protective measures with later times of the day. Discussion of Activity Diary Analysis: The Discrepancy Between Emotion-Based Fear in Relation to Daily Routines and Global Measures of Fear In relation to daily routines, the percentages of the sample showing a degree of fear were found to be substantially lower than the percentages recorded using global measures. In this study, for most of the situations resulting from the classification of the activity diary data, 4–16% of survey respondents showed a definite degree of fear. The percentage of respondents in this sample that showed a degree of fear using the global measure ‘how safe do you feel walking alone after dark in the area around your home?’ was 55%. Other studies using this measure typically report 30–45% (e.g. Borooah and Carcach, 1997; Fishman and Mesch, 1996; Mirrlees-Black and Allen, 1998; Michalos and Zumbo, 2000), and as much as 50–70% (e.g. Nair et al., 1993; Joseph, 1997) of the sample showing a degree of fear. On the surface, this discrepancy between emotion-based fear assessed in relation to daily routines and assessed using the standard global measure would seem to suggest that global measures produce inflated percentages of samples showing a degree of fear. Such a conclusion would support some of the broad criticisms made of global measures. In general, global measures would appear to be hypothetical for many of the survey respondents, as suggested by a number of authors (e.g. Ferraro

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and LaGrange, 1987; Walker, 1994; Hollway and Jefferson, 1997). In particular, it would appear to support the criticism that global measures fail to assess how people engage with fear of crime in their daily routines (Tulloch, 1998). Further, it would reinforce arguments such as those proposed by Walklate (1998) that the results from broad-scale victimization surveys using global measures give rise to somewhat sensational and unrealistic statements relating to fear causing many people to become prisoners in their own homes. However, as noted by Ferraro (1995), the standard global measure of fear taps more into cognitive judgements of risk on a personal level. Aligning this with the fact that avoidance behaviour is primarily an attempt by individuals to reduce their risk of being exposed to victimization (Skogan and Maxfield, 1981), it is likely that the standard global measure better assesses avoidance behaviour than emotional levels of fear. The results from this study, in terms of the percentage of respondents who were adopting avoidance behaviour in their neighbourhoods, support this suggestion. In relation to the specific question and cognitive mapping procedure regarding avoidance behaviour in respondents’ neighbourhoods, 64.1% of the sample in this study indicated that they were adopting avoidance behaviour in their neighbourhood at night. This is approximately 10% higher than the percentage of the sample showing a degree of fear in response to the global measure. It appears that a more direct assessment of avoidance behaviour, in relation to the daily routines of respondents, is likely to reveal a higher proportion of people who are restricted by their fear of crime than when using the standard global measure. This is in line with, and lends credence to, the assertion that fear of crime leads many people to become prisoners in their own homes (e.g. Joseph, 1997). Further, it emphasizes the appropriateness of Pantazis’s (2000) linking of the patterns associated with avoidance behaviours to current debates on poverty and social exclusion which focus on people’s ability to participate in activities that others take for granted. Thus, the global measure of fear, from these perspectives may be more relevant to the avoidance behaviour of survey respondents and less hypothetical than a number of authors have suggested (e.g. Hollway and Jefferson, 1997; Tulloch, 1998). Riger et al. (1982) suggested that the structural constraints and role obligations dictated by lifestyles and routine daily activities may circumscribe people’s ability to use precautionary tactics such as avoidance behaviours. The low percentages of respondents showing a degree of emotion-based fear recorded in this study for situations when respondents were at home may be a reflection of their ability to adopt avoidance behaviours. In the home environment, people are likely to have greater flexibility to act on their judgements of risk and, therefore, the potential to reduce their exposure to perceived victimization. The result, it appears, are lower levels of emotion-based fear. This conclusion contrasts with observations made by Liska et al. (1988) who hypothesized that the constraining of behaviour to safe places and times of the day may act to reduce levels of fear but found that constrained behaviour was associated with increased fear. Other situations where people could have more potential to adopt avoidance behaviours may be while engaging in mandatory and recreational activities.

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Mandatory activities can be conceptualized as having a fixed location and time (Golledge and Stimson, 1997). However, the definition of mandatory activities adopted for the classification of activity diaries in this study is similar to that used by Kwan (2000b) and included activities such as school, shopping and appointments. It could be argued that the selection of where to engage in these activities could stem, in part, from judgements of risk. The same logic applies to recreational activities. If people experience a crime or something that may influence their judgement of risk in these situations, they could choose to do them elsewhere. This could involve a change to a different time or a different location, for example another shopping area, school or sports field. These possible avoidance behaviours may explain the low percentages of the sample showing a degree of fear in relation to mandatory and recreational activities. In situations where respondents have less potential to adopt avoidance behaviour, such as travelling on foot after work hours in the CBD, the results show higher percentages of the sample with a degree of fear and higher percentages adopting one or more protective behaviours. These are situations where the demands set by occupational schedules necessitate exposure to risk (Riger et al., 1982). A number of studies have found respondents more likely to adopt protective behaviours in more fearful locations (e.g. Teske and Arnold, 1991; Nasar et al., 1993; Nasar and Jones, 1997). Nasar and Jones (1997) describe such situations as being characterized by a climate of fear. In relation to daily routines, it appears that particular situations also have a climate of fear.

Integrating the Key Spatiotemporal Findings with Police and Community Initiatives in Wollongong: The Degree of Institutional Involvement A number of authors have argued that avoidance behaviours prompted by fear of crime must inevitably have an economic cost because people avoiding an area remove themselves as consumers (Oc and Tiesdell, 1997; Warr, 2000). Warr (2000) notes that it is remarkable how there is no systematic evidence on the financial impact of fear of crime on retail business. The collective avoidance maps of the Wollongong CBD area and analysis of the activity diary data from the survey provide strategic information in this regard. Areas adjacent to, or enveloped by, areas of collective avoidance are highly likely to be at a disadvantage compared to businesses in areas that are not in close proximity to areas of collective avoidance. In the case of the CBD of Wollongong, the times of most relevance regarding the impact of collective avoidance behaviours on retail businesses are the collective avoidance behaviours during core business hours (i.e. between 9 am and 5:30 pm). Thus, in cases where collective avoidance concentrations do not show a strong degree of overlap with other elements of the broken windows thesis, councils and business chambers logically have a greater degree of involvement in terms of managing fear of crime than police services.

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Table 6.11 The degree of spatiotemporal overlap between elements of the broken windows thesis and suggested degree of institutional involvement Degree of overlap with elements of disorder and crime Location

PDA

SDA1

SDA2

Crime

Piccadilly

Strong

Strong

Strong

Strong

Mall

Weak

Weak

Strong

Weak

McCabe Park

Strong

Strong

Medium

Medium

Institutional response, in order of suggested degree of involvement Police/Council/Business Chamber Business Chamber/Council/ Police Council/Police

Researchers such as Warr (2000) suggest that there are sound reasons for treating crime and fear of crime as distinct social problems. However, since the emergence of fear of crime as an area of social research there has been the recognition that crime and fear of crime are linked through the role of protective and avoidance behaviours (e.g. PCLEAJ, 1967; Wilson and Kelling, 1982; Skogan, 1990). Theories such as the broken windows thesis provide the clearest formalization of the links between crime and fear of crime (e.g. Wilson and Kelling, 1982; Kelling and Coles, 1997). Thus, from a management perspective, it is important to recognize crime and fear of crime as distinct but interlinked social problems. The application of cognitive mapping techniques to fear of crime and the subsequent GIS-based analysis in this project have provided a platform to examine the collective nature of avoidance behaviour in the Wollongong CBD area to the spatial distribution of crime. Table 6.11 revisits the degree of spatiotemporal overlap between collective avoidance and the other elements of the broken windows thesis in Wollongong and provides an additional column outlining the suggested degree of institutional involvement. The distribution of the collective avoidance hotspots in relation to the hotspots of crime and disorder provide useful information in terms of strategic intervention early in the broken windows cycle. To date, many strategies designed to reduce crime, disorder and fear have focused on the later stages of the cycle. For example, the well-known zero-tolerance policing strategy in New York was based upon the assumption that the aggressive policing of disorder would reduce crime and fear of crime (Bratton, 1995, 1996). In part, the focus on the latter stages of the broken windows cycle may be due to police services lacking tools to identify where and when people are afraid of crime, recognized as key information for fear-reduction strategies (NCAVAC, 1998). In the case of the Wollongong Study, the core spatiotemporal findings (i.e. the ‘where and when’ information for fear of crime in the CBD) were published in an article in the Professional Geographer (Doran and Lees, 2005) and presented at a number of City Centre Revitalization Strategy (WCC, 2003) workshops. The findings were integrated directly with a Crime Prevention and Community Safety Plan implemented in 2007 (WCC, 2007). Many of the strategic actions in the plan drew on the suggested degree of institutional involvement for key areas of the CBD and reinterpreted the recommendations to define the role of specific agencies as well as funding streams at local, state and federal levels. In

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many instances, the NSW Police Service was listed as a contributing agency but, interestingly, often as a supporting agency rather than the lead institution. The council was able to draw on several sets of information, including the results from the Wollongong study, to better define its role and that of other agencies, as outlined in the quotation below from the crime prevention plan: Clearly there is a place for Wollongong City Council to take more of a lead role in enhancing safety throughout the LGA and specifically in the CBD. Council’s Wollongong City Centre Revitalisation Strategy states a vision for the future in which: ‘Wollongong is an innovative and prosperous regional city with a vibrant, cosmopolitan and safe city centre. . .’ (Wollongong City Centre Revitalisation Strategy Overview 2005). Safety in this context refers to the three basic CPTED principles of: natural surveillance, access control and ownership (CPTED guidelines) and (Wollongong City Centre DCP 2005). Apart from CPTED guidelines, as yet there is not a plan, strategy or integrated set of policies within Council that spell out crime prevention strategies. Yet there have been numerous surveys and consultations done throughout the LGA in which residents have given high priority to community safety and crime prevention (Doran and Lees, 2005), (Social Data Research Project 2004) and (Social Community Plan 2002–2006) (WCC, 2007: 43).

Building upon this recognition, the crime prevention plan put forth a number of intervention strategies that drew upon different sources of data – the fear mapping outputs from the Wollongong study being one of the layers of information. The action tables listed in the plan form part of the safe communities compact 2007– 2010. Some examples of specific actions are described below. ‘Don’t Stall in the Mall’ was an action which aimed to ‘. . . reduce anti social behaviour in Wollongong’s CBD, especially abusive language, harassment and evidence of drug taking and drug dealing. . .[and also] reduce fear of crime by visitors and workers in the Wollongong Mall’ (WCC, 2007: 13). The rationale for this action drew on several sources of information: Wollongong Local Area Command regularly ‘tasks’ the CBD making arrests for drug dealing and abusive behaviour. Bruce Doran’s report “Investigating the Spatiotemporal Links between Disorder, Crime and Fear of Crime” 2005, indicates Wollongong’s Mall is a ‘hot spot’ for avoidance behaviour as a result of the community’s fear of crime. A recent safety survey in December by Wollongong Police also indicated fear of crime is a direct result of anti social behaviours witnessed, including abusive language, harassment and urinating in public (WCC, 2007: 13).

Figure 6.41 has been extracted from the crime prevention plan. It illustrates how the council has reinterpreted the generic recommendations outlined in Table 6.11 to identify specific lead and partner agencies as well as funding sources in addressing drug-related disorder which was more prevalent during weekdays. The ‘City Centre Street Camera CCTV Program’ was an initiative designed for the whole CBD area and had the stated objective ‘To reduce crime in Wollongong’s CBD, namely assault, drug trafficking, theft and vandalism. . .’ (WCC, 2007: 26). The rationale for this programme drew upon the results from the Wollongong study that were published in Doran and Lees (2005), social data research projects conducted by the council in 2004 and 2005 as well the fact that ‘. . . assault, drug trafficking, theft and vandalism in this area is also well known to local police who

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regularly patrol the CBD’ (WCC, 2007: 26). The action table below (Fig. 6.42) outlines the role of key agencies and funding streams, both local and federal, that were identified for the CCTV program.

Fig. 6.41 Don’t stall in the mall – action table (source: WCC, 2007:13)

Fig. 6.42 City centre street camera CCTV program – action table (source: adapted from WCC, 2007: 26)

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Other recommendations based on the findings of the Wollongong study were less formally integrated into planning and design initiatives undertaken as part of the City Centre Revitalization Strategy which was implemented after the conclusion of the research. The fear mapping and activity diary data were presented at workshops and discussed in relation to different design options that had been put forth by a landscape design consulting group who had been recruited by the Wollongong City Council (Irwin et al., 2003). While it is difficult to determine whether these initiatives have resulted in reducing fear of crime and collective avoidance behaviour across the CBD area, it would appear that the situation has improved. Follow-up research would be needed to investigate this issue.

Assessments of Techniques and Approaches Developed in Wollongong Study Cognitive mapping, in combination with GIS proved a successful means to investigating collective avoidance behaviour. Essentially, the principles of cognitive mapping have been well established for some time (e.g. Downs and Stea, 1973; Downs, 1977; Walsh et al., 1981; Olson and Bialystok, 1983). The application of cognitive mapping to perceptions of neighbourhood service use, objective physical and social conditions, perceived neighbourhood and dangerous areas was studied by Walsh et al. (1981). However, at the time of the study, GIS was in its infancy and the researchers could do relatively little in terms of spatial collation and analyses in comparison to the tools available today. The true emergence and recognition of GIS as a distinct science began in the early 1990s (Goodchild, 1992). Since that time GIS has evolved significantly (Longley et al., 2001). However, Walsh et al. (1981) were nonetheless able to determine a strong degree of neighbourhood consensus based on a range of usage variables, despite individuals having uniquely defined patterns. More recently, Rengert (1995) used GIS-based techniques to investigate the perceptions of 24 community service recruits being trained to work in the inner city of Philadelphia. The author used a thematic mapping approach, where the study area was broken into 16 nearly equal areas and the recruits were asked to identify and rank these areas according to how dangerous they thought they were. The results were discussed in relation to hotspots of violent crime and the recruits’ knowledge of the area. A limitation acknowledged by the author was that the approach used to assess fear did not allow for a clear understanding of whether the areas identified as dangerous were also being avoided. A further limitation of Rengert’s (1995) study is that the division of the study site into 16 areas imposes artificial boundaries that may not accurately reflect the actual areas that people avoid in relation to their fear of crime. The cognitive mapping methodology developed in this study overcomes these limitations, first by specifically measuring avoidance behaviour and, second, by allowing respondents to outline the actual areas they avoid, rather than supplying them with predetermined regions. When combined using a grid-based analysis in a GIS, the results provided new insights into collective avoidance at the community

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level. In turn, this provided a framework with which to investigate spatiotemporal aspects of the broken windows theory and strategic information for the institutions involved in addressing fear of crime. The procedure used to assess social disorder and the resulting maps suggest that the temporal aspects of disorder, while relatively more time consuming to record, are important considerations. Previous block assessments of social disorder have been based on data collection between specific time periods. For example, Perkins and Taylor (1996) assessed social disorder between 17:00 and 20:00 on weeknights and 12:00 and 20:00 on weekends. Sampson and Raudenbush (1999) assessed social disorder between 07:00 and 19:00. Both Perkins and Taylor (1996) and Sampson and Raudenbush (1999) acknowledge the potential limitations of their studies in terms of capturing temporal variation in patterns of social disorder. In the case of this study, assessing social disorder on weekdays and weekends for time segments spanning 24 hours revealed a high degree of variation in social disorder. The finer-scale results were instructive when comparing social disorder to patterns of collective avoidance behaviour. For example, the strong degree of overlap between the collective avoidance of the Crown Street Mall on weekdays and social disorder in the mall area on weekends, mostly at night, suggests a link between the behaviour of the ‘pub and club’ culture on weekends and avoidance behaviour on weekdays by people working in the CBD area. This link would not have been evident if social disorder had been assessed between only between 07:00 and 19:00 or 12:00 and 20:00. Thus, the effort taken in this study to investigate the temporal dimensions of social disorder, according to time of day as well as day of week, is justified and underlines the importance of testing and adapting existing techniques used to assess neighbourhood disorder. The mapping of physical disorder proved useful in terms of highlighting the areas where concentrations were evident. The inclusion of a weighting system for different types of physical disorder was also useful, in that it reduced the number of intense hotspots to show the areas where physical disorder was of greatest concern. In general, this emphasizes the ability of GIS-based analyses to provide the institutions responsible for managing crime, disorder and fear with relevant information. For example, analysis of police-recorded incidents of graffiti in the Wollongong LGA shows the area to be the worst area in the state on the basis of all graffiti incidents (Fitzgerald, 2000). In terms of addressing this problem, being able to highlight specific areas where graffiti is concentrated is of particular value for managers. The activity diary analysis was built on the micro-level behavioural studies of Fisher and Nasar (1992, 1995) and Nasar and Jones (1997). The approaches adopted by these researchers enabled investigations of emotional levels of fear and protective behaviour in specific spatial and temporal contexts. The use of activity diaries in this study provided a means for doing this on a larger scale, namely a working population in a medium-sized city. As such, activity diaries were successful in identifying particular times and activities where a ‘climate of fear’ was evident. One potential advantage of activity diaries over the methods used by Fisher and Nasar (1992, 1995) and Nasar and Jones (1997) is that they do not require survey respondents to walk a particular route at a specified time. Activity diaries, by recording the actual

References

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daily routines of survey respondents, may therefore capture more typical situations. Further, the diary approach enabled an investigation of a range of activities, rather than only one (i.e. walking). The analysis showed emotional levels of fear and protective behaviour to be most noticeable not only when respondents were travelling by foot but also in other situations such as when respondents were at work between certain hours. General Summary of the Wollongong Study In general, the findings from the Wollongong study demonstrate that a GIS-based approach in conjunction with techniques from behavioural geography, in this case cognitive mapping and an activity diary analysis, does indeed have the potential to deliver much needed, localized, information on fear of crime. The individual cognitive mapping outputs provided the building blocks for a collective analysis of avoidance behaviour. In turn, this enabled a spatiotemporal investigation of fear of crime in relation to patterns of crime as well as social and physical disorder. The activity diary analysis provided additional context and insights into protective behaviour and emotional levels of fear among people working in the CBD area. It was possible to integrate this body of spatial and temporal information with several strategic initiatives that took place at the conclusion of the Wollongong study such as the City Centre Revitalisation Strategy (WCC, 2003) and associated land use planning changes (Irwin et al., 2003). The core findings from the Wollongong study were published in an article in the Professional Geographer (Doran and Lees, 2005) which was integrated directly with a Crime Prevention and Community Safety Plan implemented in 2007 (WCC, 2007). The Wollongong study also opened a number of avenues for future research. For example, while the cognitive mapping exercise considered the degree of avoidance by individual respondents, it was clear that other questions could be asked in relation to underlying motivations for avoidance – were people responding more strongly to different types of disorder? How could fear mapping contribute towards high-visibility policing strategies in a densely populated inner-city area? Given that Wollongong is a regional city with a number of unique spatial features and a relatively isolated CBD population, would the approach used be transferable to other contexts such as a large inner-city area? These and other questions were explored in the Kings Cross study which is described in the next chapter.

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Rengert, G. F. (1995). Comparing cognitive hotspots to crime hotspots. Crime analysis through computer mapping. C. R. Block, M. Dabdoub and S. Fregly (Eds.). Police Executive Research Forum, Washington, DC: 33–47. Riger, S., M. T. Gordon and R. K. LeBailly (1982). “1982. Coping with urban crime: women’s use of precautionary behaviors”. American Journal of Community Psychology 10(4): 369–386. Sampson, R. J. and W. B. Groves (1989). “Community structure and crime: testing socialdisorganization theory”. American Journal of Sociology 94(4): 774–802. Sampson, R. J. and S. W. Raudenbush (1999). “Systematic social observation of public spaces: a new look at disorder in urban neighborhoods”. American Journal of Sociology 105(3). Skogan, W. G. (1990). Disorder and decline: crime and the spiral decay in American neighbourhoods. Los Angeles, CA, University of California Press. Skogan, W. G. and M. G. Maxfield (1981). Coping with crime : individual and neighborhood reactions. Beverly Hills, CA, Sage Publications. Stephens, D. W. (1999). Measuring what matters. Measuring what matters: proceedings from the police research institute meetings. R. H. Langworthy (Ed.). National Institute of Justice Office of Community Oriented Policing Services, Washington, DC. Taylor, R. B. and J. Covington (1993). “Community structural change and fear of crime.” Social Problems 40(3): 374–395. Teske, R. H. C. J. and H. R. Arnold (1991). A comparative victimization study in the United States and the federal republic of Germany: a description of the principal results. Victims and criminal justice. G. Kaiser, H. Kury and H. J. Albrecht (Eds.). Max Planck Institute, Germany: 3–44. Thomas, C. and R. Bromley (2000). “City-centre revitalisation: problems of fragmentation and fear in the evening and night-time city”. Urban Studies 37(8): 1403–1429. Tiesdell, S. and T. Oc (1998). “Beyond ‘fortress’ and ‘panoptic’ cities – towards a safer urban public realm.” Environment and Planning B: Planning and Design 25: 639–655. Tulloch, J. (1998). Quantitative review. Fear of crime. J. Tulloch, D. Lupton, W. Blood, et al. (Eds.). National Campaign Against Violence and Crime (NCAVAC), Canberra. van der Wurff, A., L. van Staalduinen, et al. (1989). Fear of crime in residential environments: testing a social psychological model. The fear of crime. J. Ditton and S. Farrall (Eds.). Ashgate, Aldershot: 395–414. Walker, M. A. (1994). “Measuring concern about crime”. The British Journal of Criminology 34(3): 366–702. Walklate, S. (1998). “Crime and community: fear or trust?”. The British Journal of Sociology 49(4): 550–569. Walsh, D. A., I. K. Krauss, et al. (1981). Spatial ability, and environmental knowledge, and environmental use: the elderly. Spatial representation and behaviour across the lifespan: theory and application. L. S. Liben, A. H. Patterson and N. Newcombe (Eds.). Academic Press, New York, NY: 321–357. Warr, M. (2000). “Fear of crime in the United States: avenues for research and policy.” Criminal Justice 4: 452–489. Wikström, P. H. (1995). Preventing city center street crimes. Building a safer society: strategic approaches to crime prevention. M. Tonry and D. P. Farrington (Eds.). University of Chicago Press, Chicago, IL: 429–468. Wilson, J. Q. and G. L. Kelling (1982, March). “The police and neighbourhood safety: broken windows.” The Atlantic Monthly: 29–38. Wollongong City Council (WCC). (1999). Wollongong Safe Women Project 1999. Wollongong, Wollongong. Wollongong City Council (WCC). (2003). Wollongong City Centre Structure Plan: A Strategy for the Revitalisation of the Wollongong City Centre Wollongong, Wollgongong City Council. Wollongong City Council (WCC). (2007). Wollongong City Council Crime Prevention and Community Safety Plan. Wollongong, Wollongong City Council.

Chapter 7

The Kings Cross Study

Background to the Kings Cross Study In 2003, then NSW Police Superintendent Dave Darcy of the Kings Cross Local Area Command implemented a Community Safety Mapping Project in order to gain an appreciation of fear of crime in his command area and thereby have the knowledge necessary to reduce it. Previously, such information on public perceptions of crime had been obtained through community meetings and, to a lesser extent, on safety audits. However, the police were cognizant that it was unlikely that the small numbers of attendees accurately represented the views, experiences and perceptions of the general community. Thus, the community safety survey was conducted to gain a wider and more objective understanding of the nature, scope and causes of fear of crime in the local community (Darcy, 2003). Using the latest hand-held computer technology and geopositioning software, residents and visitors to Kings Cross were approached on the street and asked to identify sites in the Local Area Command where they felt unsafe and safe. The survey respondents were also asked which environmental cues triggered them to feel unsafe or safe. A large sample of participants was obtained, which allowed the spatial analysis of fear experienced by different socio-demographic groups (Darcy, 2003). The enthusiasm and innovation shown by the police in instigating this fear mapping project was unprecedented in NSW, however the validity of the maps was unfortunately limited by the techniques used to measure and visualize the spatial fear data. First, this was because a traditional global approach to measuring fear of crime was employed. Chapter 5 has established how such measures are problematic. Second, the fear maps disproportionately represented the areas people felt unsafe in. This resulted from the method used to interpolate the survey point data into the grid data, which was necessary in producing a series of thematic maps showing areas where certain numbers of individuals felt unsafe. Additionally, the coarse cell size of 100 m2 meant a micro-scale analysis of the study site could not occur. The fear mapping study presented in this chapter was carried out in Kings Cross following the Police Community Safety Mapping Project in 2004. This latter study employed the theoretically valid approach to measuring and mapping fear that was

B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_7,  C Springer Science+Business Media, LLC 2012

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explored in the earlier Wollongong study while meshing it with the innovative additions of the Police example in determining the factors associated with fear of crime, as described in the following sections.

Goals of the Kings Cross Study In line with the practical needs for problem-oriented policing and evidence-based crime prevention, the Kings Cross study aimed to answer the following questions in a spatially explicit manner: 1. 2. 3. 4. 5.

Are people afraid of crime? When are people afraid of crime? Why are people afraid of crime? Where are people afraid of crime? Who is afraid of crime?

In terms of methodological improvements for the field of fear-of-crime research, the study further developed unique visual-diagnostic mapping technique used to explore the capacity fear mapping has for providing new and useful information that is not revealed through traditional cognitive statistical approaches.

Research Setting The study surveyed people in Kings Cross, an inner Sydney City district in the state of New South Wales. Kings Cross loosely refers to Sydney’s 24-hour adult entertainment precinct, which encompasses a disproportionate number of strip clubs and associated brothels, licensed bars, clubs, cafes and restaurants and backpacker accommodation. Travel organizations advertise Kings Cross as ‘the premier destination for visitors’, featuring ‘a wild mixture of prostitution and crime, with stylish restaurants and hotels’ (Australian Explorer, 2005), and ‘more than two hundred of the city’s finest restaurants, bars and cafes’ (Tourism NSW, 2005). These services are focused on the infamous Darlinghurst Road, a 200-metrelong strip known colloquially as Sydney’s ‘dirty half mile’ (Butel and Thompson, 1984; Ellis and Stacey, 1971). Darlinghurst Road also accommodates Australia’s first and only Medically Supervised Injecting Centre, established in May 2001 (MSIC).

Geographic Location An area approximately one square kilometre, with Darlinghurst Road as its centre, was chosen as the specific study site. It is surrounded by the suburbs of Darlinghurst,

Research Setting

157

Fig. 7.1 Street map of the Kings Cross study site

Wolloomooloo, Potts Point, Rushcutters Bay and Elizabeth Bay. This area was selected in consultation with the NSW Police to include areas of high and low crime to allow comparison between fear in high- and low-crime areas (See Fig. 7.1 for study site map and Figs. 7.2, 7.3, 7.4, 7.5 and 7.6 for photos of Darlinghurst Road, which were taken in 2004).

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7 The Kings Cross Study

Fig. 7.2 Darlinghurst Road, Kings Cross. Looking north from Bayswater Road junction. Notice the building architecture and road works reflect the history of Kings Cross and the current development interest in the region

Fig. 7.3 Darlinghurst Road, Kings Cross, looking east. Notice the adult entertainment premises and their resident spruikers

Research Setting

159

Fig. 7.4 Darlinghurst Road, Kings Cross. Restaurants and cafes are encouraging alfresco dining

Fig. 7.5 The fountain and Fitzroy Gardens. A popular tourist attraction

Historical Background Kings Cross has historically fluctuated between periods where it has been regarded as Sydney’s premier dining and entertainment district, and periods of economic depression, poverty and slum conditions. Kings Cross has similarly been subject to constant changes in demography with the resident population being defined

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7 The Kings Cross Study

Fig. 7.6 Springfield Avenue, off Darlinghurst Road. One of the more developed laneways in the area

by groups ranging from wealthy upper-class communities, refugees, returning servicemen, European immigrants and bohemian artists. During the first half of the nineteenth century Queens Cross (now Kings Cross) was considered a wealthy area, being occupied by the community’s upper-class residents who lived on large estates and in mansions (Whitaker, 2002). The depression of the 1840s prompted residents to subdivide and sell off their land for the construction of terraces (Butel and Thompson, 1984; Whitaker, 2002). By the 1850s the area was renowned for its cheap housing, slum conditions and violence (Butel and Thompson, 1984). The area had become the home of the infamous gang, the ‘Darlinghurst Push’ (ESNA, 2002). Residences were subject to further subdivision in the 1870s following the imposition of heavy land taxes and by the 1880s terrace housing and ribbon development in the area became fashionable again (Butel and Thompson, 1984). During this decade the area saw a large influx of European migrants (Butel and Thompson, 1984; Whitaker, 2002; Lumby, 2005). In 1905 Queens Cross was renamed Kings Cross and the region became popular for dining and entertainment (Whitaker, 2002). Gangs, mobsters, violence and shootings became common from 1916 (Butel and Thompson, 1984; Ellis and Stacey, 1971). During the 1920s, prostitution was obvious and Darlinghurst Road gained

Research Setting

161

its nametag as ‘the dirty half mile’ (Butel and Thompson, 1984; Ellis and Stacey, 1971). Public outcry in the 1930s saw the gangs eradicated and the arrival of a bohemian presence (Butel and Thompson, 1984). By this time, flats became prominent, as many terraces had deteriorated or were turned into boarding houses (Butel and Thompson, 1984; Lumby, 2005). An influx of refugees in the 1930s was closely followed by US servicemen, throughout World War II (Lumby, 2005). Butel and Thompson (1984) suggest that the ‘the growth of night clubs and strip clubs, black market trading and rampant prostitution’ dated from World War II. Hence, World War II reportedly changed Kings Cross with the local residents being unhappy with corruption in the area and the presence of US servicemen (Ellis and Stacey, 1971). Cheap rents in the late 1940s encouraged an influx of more immigrants and again in the 1950s, Kings Cross entered a phase during which it was regarded as a sophisticated and cosmopolitan gathering place for diners and tourists (Ellis and Stacey, 1971; ESNA, 2002). Kings Cross became famous for its ‘live theatre, good restaurants and cafes and intellectual and artistic activities’ (Lumby, 2005). More people immigrated from Europe and the Mediterranean regions under the government’s policies and settled in the area, adding to the culinary diversity (Lumby, 2005). In the late 1960s demand for terrace houses returned and prices in the area increased (Butel and Thompson, 1984). Despite this, during the 1950s the area became known as a ‘red light’ district, with the growth of ‘home’ brothels (ESNA, 2002). Residents were complaining of harassment, assault and robbery and by 1967 a police station was built in Kings Cross (Ellis and Stacey, 1971). In 1969, police made 11,624 arrests, an average of over 31 per day. These arrests included charges of assault, robbery, drunkenness, murder, prostitution, possession of drugs, vagrancy, obscene exposure, gaming and betting, receiving stolen goods and indecent language (Ellis and Stacey, 1971). Ellis and Stacey (1971) note that a period of crime followed the ‘invasion’ of American servicemen on leave from the Vietnam War from 1967 until 1970. In the early 1970s Kings Cross became the centre of heroin supply and use in Australia. By the late 1970s street prostitution was apparent (ESNA, 2002; Van Beek, 2004). This coincided with a decriminalization of the offences of loitering and soliciting for the purposes of prostitution in 1979 (ESNA, 2002). Public outcry in 1983 forced the amendment of the Prostitution Act to prohibit soliciting for prostitution in residential streets, however this law was not enforced (ESNA, 2002). The area was regarded by Executive Chief Superintendent, Ken Chapman, as being ‘volatile’, with assaults ‘happening all over the place, both day and night’ (Zadel, 1989). Nevertheless, Kings Cross continued to be Sydney’s premier tourist district into the 1970s and 1980s (Whitaker, 2002). Since the late 1990s, tourism in the area has declined and a majority of the hotels have been converted into apartments (Whitaker, 2002). Kings Cross’s diverse history is evident in the heterogeneity of its current physical and demographic profile (Ellis and Stacey, 1971). Streets contain buildings from different eras, and a diverse range of people from differing socio-demographic backgrounds occupy those buildings (CoSC, 2005d). This is illustrated in the next section on the demographic characteristics of the region. Economically, Kings Cross is still defined by its 24-hour adult entertainment services including strip clubs and

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associated brothels, licensed bars, cafes and restaurants, and backpacker accommodation (Jochelson, 1997). Travel organizations advertise Kings Cross as ‘the premier destination for visitors’, featuring ‘a wild mixture of prostitution and crime, with stylish restaurants and hotels’ (Australian Explorer, 2005), and ‘more than two hundred of the city’s finest restaurants, bars and cafes’ (Tourism NSW, 2005).

Demographic Characteristics This diverse history of Kings Cross is evident in the heterogeneity of its current demographic profile. On average the inner-east is a wealthy area, with residents having a mean weekly individual income double that of the average for both NSW and Australia ($700–$799 compared to $300–$399; ABS, 2002a, 2002b). However, constituting this average are some of the highest income earners in Australia and some of the most socially disadvantaged. Kings Cross still has a high proportion of visiting tourists and a very transient population. On the night of the Australian census, 23% of the census sample were visiting Kings Cross from overseas or elsewhere in Australia. In comparison, only 1% of the population in Sydney on the night of the census were visiting from areas outside of Sydney. Of the population over the age of 5 years, 66% occupied a different address five years ago. Reflective of broader Sydney, Kings Cross has a large number of overseas-born residents (ABS, 2002a, 2002b). Like many high crime and high fear-of-crime regions, Kings Cross is a highdensity inner-city area undergoing rapid gentrification (Grennan, 2001; Darcy, 2003; Knox, 2003). In 2001, Kings Cross comprised Australia’s most densely populated area (the inner-east suburbs of Elizabeth Bay, Potts Point, Rushcutters Bay and Woolloomooloo). Here, fear of crime has the potential to affect a large number of people in a relatively small area. There are practical and policy implications of being able to map fear of crime in such areas. For example, already the local community, police and council would be interested in reducing crime and fear of crime. Thus there was increased likelihood that the maps produced in this study, which show where and why people avoid areas, could be used in policy, planning and practice. As an area undergoing gentrification, the redesign of the environment to manage fear and crime and to promote the use of public space is particularly relevant.

Crime Crime has been high in Kings Cross since the 1800s and the region is renowned for this association (Butel and Thompson, 1984; Jochelson, 1997; Whitaker, 2002). The NSW Bureau of Crime Statistics and Research (BOCSAR) has identified Kings Cross as an inner-Sydney hotspot of assault and robbery. Table 7.1 shows the number and rate for each of the following offences: assault, robbery and ‘other offences against the person’ for the Kings Cross LAC between 1999 and 2004 (the five years

2786.5 1141.2 735.1

52.5

353.6

31.5

796 326 210

15

101

9

12

105

9

874 346 232

42.0

367.6

31.5

3059.6 1211.2 812.2

Rate

14

147

17

1063 492 328

No.

2001

Source: NSW Bureau of Crime Statistics and Research, ref: tm05-3505.

Assault Robbery total Robbery without a weapon Robbery with a firearm Robbery with a weapon not firearm Other offences against the person

No.

No.

Rate

2000

1999

49.0

514.6

59.5

3721.2 1722.3 1148.2

Rate

14

83

7

982 296 206

No.

2002

49.0

290.6

24.5

3437.7 1036.2 721.1

Rate

16

88

8

947 289 193

No.

2003

56.0

308.1

28.0

3315.1 1011.7 675.6

Rate

15

50

8

943 211 153

No.

2004

52.5

175.0

28.0

3301.1 738.6 535.6

Rate

Table 7.1 Number and rate per 100,000 population of selected offences recorded by NSW police in the Kings Cross local area command: 1999–2004

Research Setting 163

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7 The Kings Cross Study

prior to interviewing). To provide a comparison, Table 7.2 shows the number and rate of these offences for NSW between 2001 and 2004. For each year during the five-year period prior to interviewing (from 2000 to 2004), the rate of assault and ‘other offences against the person’ in The Kings Cross LAC was three times the rate of those offences NSW-wide (BOCSAR, 2005b). However, it is important to note that the crime trends in Kings Cross for these offences and other selected offences, like ‘stealing’ and ‘break and enter’, are all stable or decreasing for the period from 2000 to 2004 (see Table 7.3). BOCSAR statistics also show that out of the 80 police LACs in NSW, Kings Cross has consistently been ranked in the top 10 for assault, robbery and ‘other offences against the person’ from 2002 through to 2004 (see Table 7.4). In 2003, the year prior to interviewing, Kings Cross was ranked 6th for assault, 4th for robbery and 6th for ‘other offences against the person’. Comparatively, the statistics indicate Kings Cross is a hotspot for these specific crimes. At the time of interviewing, specific problems with crime in Kings Cross were largely alcohol and drug related, such as assault, the use of and dealing of illicit drugs, prostitution and vandalism: or associated with socially disadvantaged youth who are being introduced into criminal enterprises (Darcy, 2005). Prostitution, homelessness, organized crime and antisocial behaviour were also considered the norm (Darcy, 2005).

Fear of Crime In addition to crime, fear of crime is recognized as a problem in the study site. The Australia-wide fear-of-crime study by Tulloch et al. (1998a, 1998b) identified Kings Cross as having a reputation for crime and fear of crime. For example, their Tasmanian survey respondents most commonly mentioned Kings Cross as the most dangerous place in Australia. However, even their Sydney respondents mentioned Kings Cross as a dangerous suburb (Tulloch et al., 1998a, 1998b). Many of their respondents acknowledged that they have never been to Kings Cross and were fearful of crime due to presumed crime levels and police corruption. In line with this reputation, many travel organizations advise visitors to be careful in Kings Cross (Sydney Online, 2005; Travel Online Australia, 2005). Nevertheless, it is also recognized as a place of excitement and pleasure. For example, one organization warns visitors to be careful ‘especially at night, as people do get mugged here. . . spruikers outside nightclubs: they can be intimidating and aggressive’ (Sydney Online, 2005). In contrast others comment that ‘it is well policed and there’s rarely more trouble than a few drunks having a fight’ (Travel Online Australia, 2005). Results from the aforementioned Police Community Safety Mapping Project, implemented in Kings Cross in 2003, also revealed a high level of fear of crime (Darcy, 2003). Of 603 respondents, 62% stated they felt unsafe in the Kings Cross Local Area Command (Darcy, 2005). Due to methodological inconsistency, it is difficult to compare this level of fear with those evident in other regions

– – – – –

–

– – – – –

–

–

– – – – – –

– – – – –

Rate

994

69,165 – 8055 992 5229

No.

2001

Source: NSW Bureau of Crime Statistics and Research, BOCSAR (2005a).

Assault Robbery total Robbery without a weapon Robbery with a firearm Robbery with a weapon not firearm Other offences against the person

No.

No.

Rate

2000

1999

15.1

1051.9 – 122.5 15.1 79.5

Rate

1130

72,279 – 6614 805 3486

No.

2002

17.0

1089.5 – 99.7 12.1 52.5

Rate

1270

72,419 – 6270 793 2971

No.

2003

19.0

1083.8 – 93.8 11.9 44.5

Rate

Table 7.2 Number and rate per 100,000 population of selected offences recorded by NSW police in NSW: 2001–2004

1414

70,122 – 4973 666 2609

No.

2004

21.0

1041.7 – 73.9 9.9 38.8

Rate

Research Setting 165

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7 The Kings Cross Study

Table 7.3 Trends in selected offences for the Kings Cross local area command: 2003–2004 and 2000–2004 Offence category Assault Sexual assault Indecent assault, act of indecency and other sexual offences Robbery without a weapon Robbery with a weapon not a firearm Break and enter – dwelling Break and enter – non-dwelling Motor vehicle theft Steal from motor vehicle Steal from retail store Steal from dwelling Steal from person Malicious damage to property

Annual percentage change 2003–2004

Average annual percentage change 2000–2004

Stable Stable Stable

Stable Stable Stable

Down by 20.7% Stable

Down by 9.9% Down by 16.9%

Down by 31.7% Stable Stable Down by 16.0% Stable Stable Down by 22.0% Stable

Down by 13.6% Down by 13.9% Down by 8.7% Down by 14.0% Up by 10.8% Down by 4.1% Not calculated Stable

Source: NSW Bureau of Crime Statistics and Research, BOCSAR (2005b).

of Australia.1 In 2004, Burgess (2004) conducted a fear-of-crime survey using crime-specific avoidance-based measurement questions and produced a number of preliminary avoidance maps. When comparing areas of fear with sites of robbery and assault, Burgess (2004) found that there were areas of the study site where crime and fear of crime appear to coincide. However, there were regions of discrepancy, where levels of fear appeared lower or higher than the occurrence of crime would justify when using those spatially congruent areas as a point of reference (Burgess, 2004). This mismatch is largely attributed to the presence of environmental cues that trigger fear of crime, which were not examined in this initial study (Burgess, 2004). Darcy’s survey found that junkies/homeless people were the top response for triggering those respondents to feel unsafe (see Table 7.5). Both of these studies will be discussed more throughout this chapter.

1 The ABS’s 2006 General Social Survey found that less than half (48%) of people reported feeling safe or very safe walking alone in their local area at night (ABS, 2006). The ABS’s 2005 Crime and Safety Survey found that 4% (day) and 8% (night) of respondents felt unsafe or very unsafe when at home alone during the day and night respectively (ABS, 2005). The ABS’s 2002 Crime and Safety Survey found that 4% (day) and 10% (night) of respondents felt unsafe or very unsafe when at home alone during the day and night respectively (ABS, 2002a).

Number

668 1445 601 982

2002

Number

614 1445 270 296

2002

Number

9 22 25 14

Local area command

Castlereagh City Central The Rocks Kings Cross

Robbery

Local area command

Redfern City Central Surry Hills Kings Cross

Other offences against the person

Local area command

The Rocks City Central Orana Kings Cross

72.8 76.0 43.7 49.0

Rate

1763.4 4988.6 1172.5 1036.2

Rate

5134.9 4988.6 4861.3 3437.7

Rate

2 1 7 5

Ranking

2 2 4 5

Ranking

1 2 3 6

Ranking

Source: NSW Bureau of Crime Statistics and Research, ref: sew05-3525.

2002

Assault

9 14 36 16

Number

2003

559 1606 279 289

Number

2003

647 1606 598 947

Number

2003

72.8 48.3 62.9 56.0

Rate

1605.4 5544.4 1211.6 1011.7

Rate

4973.5 5544.4 4837.0 3315.1

Rate

2 8 3 6

Ranking

2 1 3 4

Ranking

2 1 3 6

Ranking

15 31 44 15

Number

2004

543 1460 267 211

Number

2004

752 1460 602 943

Number

2004

121.3 107.0 76.9 52.5

Rate

1559.4 5040.4 1159.5 738.6

Rate

5780.6 5040.4 4869.4 3301.1

Rate

Table 7.4 Number, rate and ranking per 100,000 population and ranking of selected criminal incidents recorded

1 2 3 9

Ranking

1 2 3 4

Ranking

1 2 3 6

Ranking

Research Setting 167

168 Table 7.5 Top 10 responses accounting for triggering respondents to feel unsafe

7 The Kings Cross Study Reason for feeling unsafe

Percent (%)

Junkies/Homeless Prostitutes Spruikers/Intoxicated persons Dark laneways Vulnerabilities Intimidation Lighting Lack of cleanliness Laneways Loitering

18 13 8 7 6 6 5 5 4 4

Source: Darcy, 2003.

Methods Interviewing Approach With the intention of sampling residents of, and visitors to, Kings Cross, respondents were recruited in a public street setting. Interviews were primarily conducted along the main streets of Darlinghurst Road, Macleay Street and Victoria Street, in Fitzroy Gardens and in front of Wolloomooloo Police Station. Safety concerns for the interviewers meant that few of the interviews were conducted in the backstreets of Kings Cross. This also prevented door knocking as a recruitment option. Interviewers moved from site to site at various times of day. At each site, the closest individual was approached and asked if he/she would participate. If this person declined, the next closest individual was approached until someone agreed to participate. The survey was conducted in April–May 2004, between the hours of 7 am and 6 pm. These hours allowed the interviews to cover the temporal shifts in the demographic groups occupying public spaces in Kings Cross. For example, it allowed for surveying of workers entering or leaving the study area (present from 7 am to 9 am and 4:30 pm to 6 pm), the elderly (present from 9 am to 11:30 am) and intravenous drug users and dealers (present from 11:30 am onwards). Standardized interviewing methods were used because the presence of an interviewer overcomes many of the problems with postal or self-administered surveys.

Survey Design and Questions The survey was adapted from Doran and Lees (2003) and consisted of a questionnaire and mapping section where respondents mapped areas they avoided. The questionnaire, comprising a series of closed questions, was included to gain relevant

Methods

169

socio-demographic information about the respondents. This included the respondents’ gender, age, housing tenure type, residential status, experience of previous victimization and income. Questions aimed to assess levels of social integration, confidence in the police and fear of crime were also included in this section. For comparative purposes, a global measure of fear of crime and a crime-specific avoidance-based measure of fear of crime were presented. The global measure of fear was based on the frequently used question ‘have you felt fearful or afraid when walking alone in your neighborhood’ (Ditton, 2000; Pantazis, 2000). The geographic reference was changed from ‘neighborhood’ to Kings Cross to allow for a comparative analysis. The final question read as ‘Have you ever felt fearful or afraid when walking alone in Kings Cross? (yes or no)’. The crime-specific avoidance-based question immediately followed in the mapping section of the survey. Respondents were provided with a map of the study site and asked if they avoided any areas because they were afraid of being robbed, beaten or attacked, first during the day and second during the night. As Kings Cross is a definite hotspot for recorded incidents of assault and robbery, these specific crimes were chosen because they are relevant to the research setting, are personal in nature and can be distinctly conceptualized. If the respondents answered positively to this crime-specific avoidance-based measure of fear, they were asked to illustrate those areas they avoided on the map provided. Surveys where the respondents did specify areas that they avoided on the provided map were considered as answering positively to the question. Respondents were then asked how hard they tried to avoid each area and what environmental cues triggered their fear of being robbed, beaten or attacked. When respondents answered how hard they avoided each area, they could choose only one of five potential answers ranging from ‘very hard’, ‘quite hard’, ‘don’t know’, ‘not very hard’ to ‘not hard at all’. The options were displayed on a cue card and corresponded to a numerical value, based on an ordinal Likert Scale. These values are referred to as ‘avoidance hardness’ weights. A second question on environmental cues was then presented to the respondents. Respondents were given another cue card with a list of 16 social and physical environmental cues (see Table 7.6 for cues and explanations). Multiple environmental cues could be selected and different answers could be given for each avoided area that was defined. The environmental cues were chosen due to their relevance to the research setting and extensive reference throughout the fear-of-crime literature. Highlighting this is important because Phillips and Smith (2003) criticize that researchers can intentionally or unintentionally label certain groups as ‘incivil’ from the outset of their studies. These groups are typically those who are disadvantaged and different, whom the public stereotype as being involved in crime and become scapegoats symbolically linked to crime (Blalock, 1967; Kelling and Coles, 1997). Such groups include those used in this survey, including homeless people, sex workers and drug users.

Explanation

Intravenous drug users, users of other illicit drugs and drug dealers Employees, usually of adult entertainment venues, who encourage pedestrians to buy tickets and enter their premises People living in community shelters or on the street People who have consumed alcohol or appear dunk People who engage in sexual acts for money, also known as prostitutes Groups of people, who generally appear menacing or who elicit feelings of concern in pedestrians People who appear to have no purpose for being where they are; ‘up to no good’ Lack of other pedestrians

Explanation

Absence or lack of adequate street lighting Property damage by vandals, for example graffiti or broken windows Litter on streets/thoroughfares that is not in bins, and equipment used in drug injection Vacant buildings or those that are dilapidated or in a state of disrepair Shops that offend or appear dilapidated Places where an attacker could seek refuge, for instance hiding behind bushes

Social factors

Drug usersa Spruikersb Homeless peoplec Intoxicated personsd Sex workerse Gangsf Loitering peopleg Pedestrian absenceh

Physical factors

Poor street lightingi Vandalismj Rubbish/syringesk Rundown/abandoned buildingsl Offensive/degraded shopsm Areas to hiden

Table 7.6 List and explanations of the social and physical environmental cues used in the survey

170 7 The Kings Cross Study

An area where escape would be difficult in the event of an attack, for instance stairways Small thoroughfares, generally pedestrian only although some are big enough for one-way traffic

a Other sources that examine drug users: Covington and Taylor (1991), Darcy (2003), Perkins and Taylor (1996), Skogan (1990), Perkins et al. (1993) cited in Ross and Mirowsky (1999) b Other sources that examine spruikers: Darcy (2003) c Other sources that examine homeless people: Darcy (2003), Perkins et al. (1993) cited in Ross and Mirowsky (1999) d Other sources that examine intoxicated persons: Covington and Taylor (1991), Darcy (2003), Skogan (1990), Ross and Mirowsky (1999) e Other sources that examine sex workers: Darcy (2003), Perkins et al. (1993) cited in Ross and Mirowsky (1999) f Other sources that examine gangs: Perkins and Taylor (1996), Rohe and Burby (1988) and Perkins et al. (1993) cited in Ross and Mirowsky (1999), Skogan (1990) g Other sources that examine loitering people: Darcy (2003), Skogan (1990), Ross and Mirowsky (1999) h Other sources that examine pedestrian absence: Jacobs (1961), Loukaitou-Sideris (1999), Samuels and Judd (2002) i Other sources that examine poor street lighting: Darcy (2003), Fisher and Nasar (1995) j Other sources that examine vandalism: Lewis and Maxfield (1980), Perkins et al. (1993) cited in Ross and Mirowsky (1999), Skogan (1990) k Other sources that examine rubbish/syringes: Covington and Taylor (1991), Darcy (2003), Doeksen (1997), [LaGrange, Ferraro and Supancic (1992); Perkins et al. (1993) cited in Ross and Mirowsky (1999)], Skogan (1990), Taylor and Covington (1993) l Other sources that examine rundown/abandoned buildings: Covington and Taylor (1991), Doeksen (1997), Perkins and Taylor (1996), [Ferraro and Supancic (1992); Gates and Rohe (1987); LaGrange, Lewis and Maxfield (1980); Rohe and Burby (1988) cited in Ross and Mirowsky (1999)], Skogan (1990) m Other sources that examine offensive/degraded shops: Darcy (2003) n Other sources that examine areas to hide, or concealment: Rondeau et al. (2005), Fisher and Nasar (1992, 1995), Nasar and Jones (1997) o Other sources that examine blocked escape: Rondeau et al. (2005), Fisher and Nasar (1992, 1995) p Other sources that examine laneways: Darcy (2003), Fisher and Nasar (1995)

Blocked escapeo Lanewaysp

Table 7.6 (continued)

Methods 171

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7 The Kings Cross Study

Spatial Data Visualization Using ESRI’s ARC suite of GIS programs, this spatial avoidance data was used to produce three different styles of fear maps. These consisted of two-dimensional (2D) ‘collective-avoidance’ maps showing how many respondents avoided each area, 2D ‘avoidance hardness’ maps showing the extent to which the respondents tried to avoid each area and three-dimensional (3D) maps that concurrently displayed the ‘collective-avoidance ’ and ‘avoidance hardness’ data. Each individual survey map was digitized and imported in the GIS as separate layers. Attributes were assigned to the layers so that each area avoided by a respondent could, for example, display the sex, age and income of the respondent and the corresponding environmental cues that triggered fear in the respondent. For the 2D ‘collective-avoidance’ maps, each area avoided by an individual was assigned a value of one (1). The individual maps were then aggregated to a single map that illustrated the number of respondents avoiding each area of the study site. The resulting accumulative values were then classified as a percentage of the total number of respondents in that socio-demographic group. This allows for comparison between the socio-demographic groups. The process is shown in Fig. 7.7.

Fig. 7.7 A visual representation of the process of aggregating the individual maps to produce the 2D ‘collective-avoidance’ and ‘avoidance hardness’ maps. The number of respondents in the example is 10, with two of these people employing avoidance behaviours

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To create the 2D ‘avoidance hardness’ maps, the same process was replicated using the ‘avoidance hardness’ values for each avoided area. However, to show the average degree of ‘avoidance hardness’ for each area, the accumulative values in the output maps were then divided by the total number of people avoiding areas in that socio-demographic group. This returned the values to the original Likert Scale with values from one to five, the Avoidance Hardness Index. This process is also shown in Fig. 7.7. The 2D ‘collective-avoidance’ and ‘avoidance hardness’ maps were then visualized in 3D to better illustrate areas of fear according to the number of people avoiding them and the extent to which those people tried to avoid the areas. The unclassified ‘collective-avoidance’ maps were first displayed as the elevation (z-factor) of the land, thereby representing the number of people avoiding each area. Higher land indicated areas avoided by more people than lower land. In order to allow easy comparison between different maps, particularly those displaying the patterns of avoidance adopted by different socio-demographic groups, population percentile bands were added to the maps (white horizontal lines). The population percentile bands were inspired from contour lines on a topographic map; however instead of showing height above sea level, they indicate the proportion of respondents that was avoiding each area. The population percentile bands were placed at 5% intervals (i.e. at several heights representing increments in the numbers of people equal to 5% of the total sample population in that category). Each band therefore indicates a 5% increase or decrease in the number of avoiding respondents. The colour of the land shows how hard the respondents tried to avoid each area. The 2D ‘avoidance hardness’ layers were then draped over the 3D collectiveavoidance’ maps. Thus, the colour of the maps shows how hard the respondents tried to avoid each area. This adds an extra dimension that can be analyzed in conjunction with the population numbers. Fig. 7.8 visually represents this 3D process using a 2D graph that signifies a cross-section of a 3D map.

Results and Discussion Sample Characteristics Slightly more males (53%) than females (47%) were interviewed. This is consistent with the male to female ratio in Sydney’s inner-east. The majority of respondents were in the middle-age group (46% aged between 30 and 59). Almost 25% of respondents were over the age of 60 and 30% of respondents were between the ages of 18 and 29. While the percentage of respondents in the later age group is consistent with the local demographic (32%2 ), there is a much higher proportion of respondents aged over 60 in the research sample. In line with this, there is a lower

2

Thirty-two percent of the inner-east population over the age of 18 years.

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7 The Kings Cross Study

Fig. 7.8 A visual representation of a 3D map cross-section, showing how the 2D maps were combined for 3D display. The number of respondents in the example is 400. The maximum number of avoiding respondents in the cross-section is approximately 150 (33% of the total)

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18% 16%

14.96% 15.22%

14% Percent

10.76%

11.29%

12% 10%

8.40%

8%

8.14%

11.02% 7.87%

7.09%

6%

4.72%

4% 2%

0.52%

0% 18−23

24−29

30−35

36−41 42−47 48−53 54−59 60−65 Age distribution of respondents

66−71 over 72

No answer

Fig. 7.9 Age distribution of respondents

proportion of respondents in the 30–59 age group in the sample. Only 1% of the respondents did not state their age (see Fig. 7.9). In terms of housing tenure, the majority of the sample were owner-occupiers (45%) and non-owner-occupiers (36%). Less than 10% of the respondents lived in government housing or were staying in backpacker accommodation and 2% of the sample lived in a community shelter. Overall these percentages are consistent with general housing tenure in the inner-east; however more of the sample are owner-occupiers in comparison to the local demographic (36%). Only 1% of the respondents did not answer the question on housing tenure. Slightly more visitors to, than residents of, Kings Cross were interviewed. This is contrary to the ratio of residents to visitors in the inner-east on census night (77% to 23% respectively). Of those visitors to Kings Cross who were interviewed, 76% were Australian residents and 22% were visiting from overseas. Less than 3% percent of the visiting respondents did not answer this part of the residency question. Of those residents of Kings Cross who were interviewed, the majority (50%) had resided in the area for more than five years. Of the remainder of the residents, approximately 20% had lived in the area less than 1 year, 10% for 1–2 years and 15% for 3–5 years; 5% of the respondents who were residents did not answer this part of the question. The majority of respondents had not been a victim of crime in the 12-month period prior to interviewing (66%) (see Fig. 7.10 and Fig. 7.11); 35% of the respondents had been victims of crime. Of these respondents, their experience of victimizations had comprised of attack or assault (27%), threats of force or violence (19%), theft or attempted theft (32%) and damage to property (20%) (see Fig. 7.11). Only 2% had been victims of a crime involving the use of a weapon. The majority of respondents had not ever been victims of crime in Kings Cross (64%); 20% of the respondents who had been victims of crime in Kings Cross reported that crime to the police.

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7 The Kings Cross Study 65.62%

70% 60%

Percent

50% 40% 30% 20% 11.02%

9.19%

10%

6.56%

6.82%

0.79% 0% Use of a weaponAttack or assault

Threats Theft / force/violence attempted theft

Damage to property

Not applicable

Experience of victimization

Fig. 7.10 Respondent distribution according to experience of victimization

35%

32.06%

30%

26.72%

Percent

25% 19.08%

20%

19.85%

15% 10% 5%

2.29%

0% Use of a weaponAttack or assault

Threats Theft / attempted force/violence theft Use of a weapon

Damage to property

Fig. 7.11 Distribution of those respondents who had been victimized, according to experience of victimization

The majority of respondents (85%) indicated they thought that they or their neighbours would call the police if they saw someone being assaulted in Kings Cross. Only 10% answered negatively and 6% did not answer this question; 64% of the respondents indicated they were either ‘very confident’ or ‘quite confident’ in the police. Only 18% of the respondents were ‘not very confident’ or ‘not confident at all’ in the police. A large proportion of respondents fell into the lowest income category (28%). The two middle-income categories comprised of 55% of the respondents, while less

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than 10% of respondents were in the top-income bracket. This result is very different from typically high income levels earned by inner-east residents (for example 4% and 21% in the lowest- and highest-income categories respectively). The result is more consistent with the income levels earned by residents of greater Sydney: 8% of the respondents did not answer the question on income.

People Are Afraid of Crime in Kings Cross A sample of 399 survey respondents was obtained. A total of 18 surveys were excluded from analysis because more than two general survey questions or the mapping section was unanswered. The study results verify that people are afraid of crime in Kings Cross, with 35% of the respondents indicating that they had felt fearful or afraid when walking alone in Kings Cross (see Table 7.7). More specifically, 36% (day) and 66% (night) of the respondents indicated that they avoided areas of the study site because they were afraid of being robbed, beaten or attacked during the day and night respectively. The finding that fear of crime is greater during the night than the day (nearly double that during the day) is consistent with the results from other fear-of-crime studies that attribute this increase in fear to the onset of darkness. These findings are also consistent with the earlier study in Wollongong, where Doran (2004) found that 54.7% of respondents felt either ‘not very safe’ or ‘not safe at all’ when walking alone in the city, and that 39.31% (day) and 81.20% (night) respondents actually avoided parts of the CBD during the day and night respectively. Given the fact that crime levels are higher in Kings Cross than in Wollongong, it is somewhat surprising that avoidance levels, in terms of overall percentage of avoiding respondents, are higher for Wollongong than Kings Cross. This finding reflects the signal crimes perspective, which concludes that fear of crime depends on the situational context of the study region and the presence of environmental cues within it. Regardless, the levels of fear found in Kings Cross are high and it is possible that they are underestimated given the probability that the most fearful members of society could not be interviewed due to the street-based interviewing approach. While this study does not suggest that these research findings will be true for the wider population, they do indicate a large proportion of residents of, and visitors to, Kings Cross could be avoiding parts of the region due to fear of crime. Forty-six percent of the avoiding respondents tried either very hard or quite hard to avoid the areas in which they were afraid of being robbed, beaten or attacked during the day. This figure increased to 57% during the night; 34% of the respondents did not try very hard or did not try hard at all to avoid those areas during the day. This figure decreased to 30% during the night. For both the day and night, only 4% of respondents did not know how hard they tried to avoid those areas; 22 respondents did not answer the question. This accounted for 16% and 9% of the respondents during the day and night respectively (see Table 7.8).

178

7 The Kings Cross Study Table 7.7 Number and percent of respondents indicating fear of crime Yes

No

No answer

Total

No.

%

No.

%

No.

%

No.

%

Global measure: Have you ever felt fearful or afraid when walking alone in Kings Cross?

133

34.91

244

64.04

4

1.05

381

100.00

Crime-specific avoidance-based measure: Do you avoid any areas shown on this map of Kings Cross, because you are afraid of being robbed, beaten or attacked, during the day?

138

36.22

243

63.78

0

0.00

381

100.00

Crime-specific avoidance-based measure: Do you avoid any areas shown on this map of Kings Cross, because you are afraid of being robbed, beaten or attacked, during the night?

252

66.14

129

33.86

0

0.00

381

100.00

Table 7.8 Number and percent of respondents by degree of avoidance hardness Day

Very hard Quite hard Don’t know Not very hard Not hard at all No answer Total responses

Night

No.

%

No.

%

40 24 5 30 17 22 138

28.99 17.39 3.62 21.74 12.32 15.94 100.00

86 57 11 54 22 22 252

34.13 22.62 4.37 21.43 8.73 8.73 100.00

The Dissonance Between Traditional Global Measures and Crime-Specific Avoidance-Based Questions By employing a survey comprising of a global measure of fear and a crime-specific avoidance-based question, it was not only possible to assess fear-of-crime levels, but also possible to compare unspecified cognitive approaches to measuring fear

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of crime with specified behavioural approaches. This section discusses the differences between the results of the two measurement approaches and discusses the implications of these differences for future fear-of-crime research. One would expect respondents declaring that they have not felt fear of crime to not avoid areas because they were afraid of being robbed, beaten or attacked. Likewise, one would expect respondents affirming fear of crime in Kings Cross to avoid areas because they have felt such fear. If this were true, the responses would coincide. However, agreement occurs in only 68% of cases during the day and in 60% of cases during the night. This exposes a degree of dissonance, which in turn provides some useful insights and asserts the appropriateness of crime-specific avoidance-based questions in measuring fear of crime. There are two scenarios where there is dissonance between the results from the crime-specific avoidance-based question and a global measure of fear. The first scenario consists of respondents answering positively to the crime-specific avoidance-based question and negatively to the global measure of fear. The second scenario involves the converse. While these discordant results could be due to respondents answering survey questions dishonestly or misinterpreting questions, possible arguments are discussed for each scenario.

Scenario One: Fearful People Choosing Not to Avoid The first scenario consists of respondents stating that they have felt fearful or afraid when walking in Kings Cross and yet do not avoid any areas because they fear being robbed, beaten or attacked. Approximately 16% of respondents answered in this manner during the day and 3% during the night. Three arguments support this scenario. First, it is possible the fear experienced in Kings Cross was either not due to fear of crime at all or not specifically due to fear of being robbed, beaten or attacked. Having never felt fearful of being robbed, beaten or attacked there would be no motive to avoid areas. This reinforces the notion that global questions about fear can provide misleading results. It is imperative that questions are crime-specific when querying respondents about fear. The second rationale is respondents were fearful of being robbed, beaten or attacked, however not to the extent whereby avoidance was warranted. Benefits of using the areas would outweigh the negative responses felt when evaluating the situation. This argument could account for lower levels of dissonance during the night (13.4% down from 16.3% during the day). The percentages could indicate that fear does not warrant avoidance behaviour during the day, but does at night. This demonstrates that avoidance behaviours produce more informative results than cognitive assessments of fear, as suggested by Warr (2000) and Burnett (1976). Finally, it may be that respondents were previously fearful of being robbed, beaten or attacked. The question put forward was ‘have you ever felt fearful. . .?’. This implies respondents could answer affirmatively having once felt fearful, even if they no longer felt that fear. Theoretically, if people no longer felt fearful there

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7 The Kings Cross Study

would be no reason for avoidance behaviour. This suggests that the area, or respondents’ perceptions of the area, might have improved (Couclelis, 1998). In a dynamic area like Kings Cross this occurrence could be exaggerated. Additionally, it indicates that global questions may not reflect fear of crime at the time of surveying. As in the avoidance-based question, survey items should specifically ask respondents about current feelings of, or responses to, fear. Scenario Two: Fearless People Avoiding Areas This scenario involves respondents stating they have not felt fearful of crime when walking in Kings Cross and yet do avoid areas due to fear of being robbed, beaten or attacked. Approximately 16% and 37% of respondents answered in this manner during the day and night respectively. Four arguments support this scenario. Each assumes respondents do not feel fearful because they avoid the areas in which they feel such fear. This reinforces the benefit of studying behavioural responses to fear of crime, rather than emotional statements. It is important to re-emphasize that respondents state they have never felt fearful when walking in Kings Cross. This elicits questions of why these people originally avoided areas because they felt fearful of being robbed, beaten or attacked if they never felt fear in the first place. The first two arguments infer respondents never visited the areas they avoid and only assume that they would fear being robbed, beaten or attacked if they resided there. This situation requires respondents to associate areas with fear based on secondary information provided by friends, family or media (Romer et al., 2003; Weitzer and Kubrin, 2004; Killias and Clerici, 2000; Koskela and Pain, 2000; Valentine, 1989). This supports theories implying that indirect-victimization is largely responsible for heightened levels of fear (Katz et al., 2003). Consequently, the respondents’ spatial knowledge and patterns of avoidance are based on the reputation of the area, not personal experiences. If this is the case, surveys that consequently investigate the influence of certain environmental cues that act to trigger fear in the area may be flawed, as the respondents will not have actually seen the area themselves. Therefore, it may be beneficial that future investigation ask respondents whether they have actually been into or seen the areas in question. The second line of reasoning entails respondents observing the areas they avoid from a distance. Individuals may view a laneway from another street and perceive it as dangerous in which they would feel fearful of being robbed, beaten or attacked. In this case, the avoided areas should only be as large as the field of vision from various vantage points used to assess the area. If the avoided area is larger, the respondents could be associating the region with environmental cues that are not present. As mentioned above, this could hamper any investigations of environmental cues that act to trigger fear of crime. Hence respondents should be asked if they have seen all of the areas that they avoid. Another case involves respondents having once visited the areas they avoid. That they did not feel fearful within those areas could imply they were not walking alone and felt less vulnerable to attack. For example accompaniment by others or driving a vehicle could provide a sense of security. Further studies could investigate

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181

such circumstances or protective actions enabling people to feel secure in normally avoided areas. Similarly, people may be walking alone and employ other protective actions which stopped them from feeling fear. Finally, the results show that the dissonance between the statistical and spatial results is greater for the night than the day. Respondents may automatically think of daytime hours, or the time of interviewing, when contemplating if they have felt fear. This demonstrates the practicality of asking respondents about fear during the day and night, as did the crime-specific avoidance-based question. As expected, given the literature on global measures of fear of crime, many respondents answered the crime-specific avoidance-based and global fear-of-crime survey questions differently. This dissonance demonstrated the ambiguity inherent in global measures and reinforces appropriateness of using crime-specific avoidance-based questions.

People Avoid Specific Areas of Kings Cross Due to Fear of Crime This study specifically examines where people are afraid of being robbed, beaten or attacked in Kings Cross. By developing a visual-diagnostic technique for mapping collective avoidance, the study found there are common patterns of avoidance throughout the study site, and that fear of crime in Kings Cross occurs in noticeable hotspots. The evidence that specific micro-level hotspots of fear do exist in this highcrime area is consistent with other fear mapping examples, for instance Doran and Lees’s (2003) and Fisher and Nasar’s (1992, 1995) studies. Information regarding the whereabouts of these fear hotspots in Kings Cross has useful implications for policy and planning in the region, especially given that the City of Sydney Council aims to identify safety issues and crime ‘hotspots’ and design strategies to improve crime rates and safety (CoSC, 2006m). This section of the chapter examines overall avoidance patterns in the study site, as well as the implications of the maps for policing and CPTED. Mapping Reveals Three Fear-of-Crime Hotspots The overall avoidance maps reveal a radical change in elevation in the centre of the fear maps, which indicate an enormous divide in collective avoidance either side of William Street (Figs. 7.12, 7.13, 7.14, 7.15 and 7.16). There is a much larger proportion of respondents avoiding the study site north of William Street. The three peaks north of William Street show that collective avoidance in the study site is greatest in three distinct hotspots, referred to as hotspots A, B and C (see Fig. 7.16). Avoidance predominates • over central Woolloomooloo (hotspot A); • in the street block bordered by Victoria Street, Orwell Street and Darlinghurst Road (hotspot B); and • in the street block bordered by Darlinghurst Road, Bayswater Road, Roslyn Street and Ward Avenue (hotspot C).

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7 The Kings Cross Study

Fig. 7.12 Two-dimensional collective-avoidance map depicting areas where all respondents avoided because they were afraid of being robbed, beaten or attacked during the DAY

Results and Discussion

183

Fig. 7.13 Two-dimensional collective-avoidance map depicting areas where ALL RESPONDENTS avoided because they were afraid of being robbed, beaten or attacked during the NIGHT

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7 The Kings Cross Study

Fig. 7.14 Three-dimensional fear map depicting areas of fear according to the number of respondents avoiding them and the extent to which those respondents tried to avoid the areas because they were afraid of being robbed, beaten or attacked during the DAY

Avoidance in the study site was greatest in the Kings Cross neighbourhood, which locates the fear hotspot peaks B and C. These hotspots are discussed before peak A. Kings Cross: Hotspots B and C Victoria Street, Orwell Street and Darlinghurst Road are frequently the boundaries for the second area (hotspot B). Streets within this area include Earl Place, Earl Street, Llankelly Lane, Mall Place and Springfield Avenue. Hotspot B often extends to include Orwell Lane, Hughes Lane, Hughes Place and Hughes Street. Darlinghurst Road, Bayswater Road, Roslyn Street and Ward Avenue border hotspot C, which encompasses Kellett Way, Kellett Street and Mansion Lane. The slopes of the peaks defining hotspots A and B are quite high, suggesting that there is a large change in the number of avoiding respondents over a relatively small area. The fear maps show that up to 15% and 30% of all respondents avoided hotspots A and B during the day and night respectively. With a history of crime and disorder in the Kings Cross neighborhood, the City of Sydney Council has made plans to encourage opportunities for surveillance throughout the area. This is proposed

Results and Discussion

185

Fig. 7.15 Three-dimensional fear map depicting areas of fear according to the number of respondents avoiding them and the extent to which those respondents tried to avoid the areas because they were afraid of being robbed, beaten or attacked during the NIGHT

by maintaining the mix of land uses so there is 24-hour surveillance along major streets; lighting pedestrian pathways with vandal-proof fixtures; having public open spaces that are well lit and have clear sight lines; signage describing pathways and facilities, for example taxi ranks and bus stops; provision for Help Points; active uses and frontages around public open spaces; building entry points that are readily identifiable, clearly visible and well lit; and buildings that are designed to minimize entrapment spots and have openings in all walls that have frontage to a public area (AJC, 2006). Kings Cross Town Centre and Plaza are situated on Springfield Avenue near its intersection with Earl Place. These venues are in the heart of fear hotspot B and, have been identified by Council as key sites for redevelopment with the establishment of active edges and outdoor eating to activate the street life and provide opportunities for natural surveillance (AJC, 2006). Those streets forming hotspot C have not specifically been addressed in terms of redevelopment opportunities. If combating fear of crime is an objective, these streets should also be a priority for gentrification.

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7 The Kings Cross Study

Fig. 7.16 The Kings Cross study site depicting Blocks A, B and C, which make up the three common fear-of-crime hotspots

Woolloomooloo: Hotspot A The avoidance maps show that up to 11% (day) and 29% (night) of all respondents avoided central Woolloomooloo (hotspot A) during the day and night respectively. The coverage and definition of hotspot A varies greatly for each environmental

Results and Discussion

187

cue (discussed later). Generally speaking, the City of Sydney intends to maintain the quiet residential atmosphere that currently defines the Woolloomooloo neighbourhood (AJC, 2006). However, the results from the fear mapping show that this environment should not be maintained as is if fear of crime and avoidance are to be addressed. The City of Sydney Council does plan to landscape the rail corridor and viaduct, which are sites of particularly pronounced avoidance levels in peak A (AJC, 2006).This will allow for greater spatial definition, increased amenity, more activity along its edges and improved surveillance (AJC, 2006). There are also plans to redevelop a series of vacant lots on the western edge of the study site (AJC, 2006). However, the research results show that overall avoidance is low along the affected streets and that people are not avoiding these areas because the presence of rundown/abandoned buildings trigger their fear of crime. It is therefore likely that the reduction of fear of crime will require alternative CPTED strategies in Woolloomooloo. The population percentile bands and elevation of the land show collective avoidance throughout the study site increases during the night. In all of the maps the average avoidance hardness did not exceed ‘not very hard’. In the each of the maps, the jump in the extent to which respondents try to avoid areas (from ‘not hard at all’ to ‘not very hard’) generally coincides with the onset or middle of the three major avoidance peaks.

Safe Areas and Cognitive Barriers William Street The fear maps show that William Street acts as a divide separating areas that trigger high levels of avoidance (north-side) from those areas that do not (south-side). Brantingham and Brantingham (1993) propose that people include perceptual edges in their cognitive maps to indicate the spatial limits of high-crime areas. The avoidance maps demonstrate that such edge effects exist and that they influence how hotspots of crime and fear are cognitively defined, with people commonly perceiving William Street as a physical barrier between safe and unsafe areas. The maps also provide evidence that such phenomena do affect peoples’ spatial choices and behaviours. Brantingham and Brantingham (1993) suggest that edges have increased levels of crime because they represent the limits of territoriality and the surveillance or identification of strangers who may commit crime. However, given the fact that visitors frequent the suburbs to the north and south of William Street, it is unlikely that this is the cause of the change in fear and avoidance levels. More likely, it is perceived changes in crime levels, environmental cues, land-use types and demographic characteristics that influence levels of fear and avoidance. For example, those suburbs immediately to the north of William Street have higher crime levels, more signs of disorder, a higher density of adult entertainment premises and lower average incomes than the suburbs to the south.

188

7 The Kings Cross Study

Darlinghurst Road Similarly, the fear maps establish that during the day and night respectively, 5% and 20% fewer respondents avoid Darlinghurst Road than the surrounding areas. This establishes that Darlinghurst Road is considered a distinctly safe thoroughfare through fear hotspots A and B. This finding is interesting given that most signs of disorder, robbery incidents and assault incidents in Kings Cross occur along Darlinghurst Road. In line with this, Brantingham and Brantingham (1993) comment that crime is concentrated along high-activity pathways, like Darlinghurst Road, that are frequented by people travelling to work, shopping centres and entertainment premises. The research results suggest that although Darlinghurst Road is high in crime, the fact that it is a major pathway with the attractions of transport, shopping and entertainment may mean people’s levels of fear of crime are not great enough to warrant avoidance behaviour. It could also indicate the presence of signs of safety or cues that counteract feelings of fear. Further research into why such high-crime pathways may have low levels of fear will help with CPTED efforts. Nevertheless, the research results show that Kings Cross is losing a very large number of potential customers and the presence of crime and disorder means that Darlinghurst Road should remain a focus for CPTED and police attention. The City of Sydney has recognized this and taken action to encourage ‘better quality’ entertainment and retail activities so that Kings Cross continues to be attractive to local and global visitors (AJC, 2006). Victoria and MacLeay Streets Victoria and MacLeay Streets are also defined on the fear maps as areas where collective avoidance is considerably lower than in the surrounding areas, although not to the same extent as along Darlinghurst Road. Victoria Street separates hotspots A and B. These streets are both largely residential with a few specialty stores and coffee shops. They generally do not have the same level of signs of physical and social disorder evident along Darlinghurst Road and have not been targeted for gentrification by the City of Sydney.

Exploring the Underlying Reasons for Fear of Crime While fear and crime can be spatially coincident this is not always the case (Smith, 1987; Nelson et al., 2001). Commonly, research has ascertained that fear in certain areas is higher than warranted by actual rates of victimization (Liska et al., 1988; Painter, 1996). The occurrence of this spatial discrepancy has led researchers to query the validity of crime survey questions (Taylor and Hale, 1986). This section of the chapter provides alternative explanations for the failure of fear and crime to match spatially. It first looks at the coincidence between crime and fear of crime and then explores those environmental cues that trigger people to feel afraid of being robbed, beaten or attacked in Kings Cross.

Results and Discussion

189

The Presence of Crime Not surprisingly, most robbery and steal-from-person incidents occur in close proximity to the entertainment strip, forming a well-defined hotspot running along Darlinghurst Road between its intersections with William and McCleay Streets (Fig. 7.17). The side streets of Bayswater Road, Kellet Street, Kellet Way, Rosyln

Fig. 7.17 Sites of robbery and steal-from-person incidents in the Kings Cross study site, in the six-month period prior to interviewing (October 2003 to April 2004)

190

7 The Kings Cross Study

Street, Earl Place and Springfield Plaza are also contained within the hotspot. These areas provide the focus for police deployment with high-visibility uniformed foot patrols supported by specialist undercover police. In Kings Cross, there are areas of the study site where crime and fear of crime appear to coincide. However, there are regions of discrepancy where levels of fear appear either lower or higher than the occurrence of crime would justify (when using those spatially congruent areas as a point of reference). A Positive Association Between Crime and Fear In the northern half of the study site (above William Street) a positive association between crime and fear of crime appears. Areas with a high density of assault or robbery correspond to areas that many respondents avoid (fear hotspots B and C, which are avoided by 10–15% of respondents during the day and 30–35% of respondents at night). Similarly, in areas north of William Street where the incidence of assault and robbery is high but more sporadic than in the aforementioned areas, there has been a proportionate decrease in fear (with 5–10% and 15–30% of the respondents avoiding these areas during the day and night respectively). These areas are referred to as ‘secondary’ areas of fear and crime. However with regard to the area of the study site south of William Street, the correlation between fear and crime is not as apparent. A Spatial Mismatch Between Crime and Fear There is a comparable amount of crime between the southwestern quadrant of the study site, bounded by and encompassing William Street and Victoria Street, and the ‘secondary’ areas north of William Street. Despite this, considerably fewer people avoid the area (less than 5% during the day and less than 15% during the night). Fear of crime in this southwestern quadrant appears lower than expected given the level of crime3 . In the southeastern quadrant of the study site, bordered by William Street and Victoria Street, there are no cases of assault or robbery. As few people avoid this area during the day (less than 5% of respondents), there is congruence between levels of fear and crime. However, a discrepancy exists during the night, where up to 15% of the respondents avoided the area. When comparing levels of avoidance in the northern half and southwestern quadrant, this percentage appears higher than warranted by spatial crime statistics. Nevertheless, an increase of approximately 10% of respondents avoiding an area during the night than during the day is consistent throughout most of the study site. This suggests that darkness triggers approximately 10% of the population to feel

3 Using the levels of fear experienced by the respondents in the northern half of the study site as a benchmark.

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more fearful4 . In areas where the increase is proportionately greater, for example either side of Darlinghurst Road, it is likely that night-time social changes also play a role in heightened levels of fear (Koskela, 1999). For example, the presence of intoxicated persons may increase during the night. Time affects one’s perception of events and phenomena (Allen and Kautz, 1985 cited in Block, 2000; Tversky and Taylor, 1998). As intoxicated persons were found to be a significant environmental cue during the night, and most liquor outlets are on Darlinghurst Road, this proposition is likely. Nonetheless, spatial analysis of environmental cues is needed for conclusive hypotheses. Accounting for the Mismatch Between Crime and Fear This part presents possible explanations for the mismatch between crime and fear of crime in Kings Cross. First, researchers argue that unreported crimes can justify high levels of fear in low-crime areas (Koskela and Pain, 2000). Given that approximately half of the respondents who had been victims of crime in Kings Cross reported it to the police, this argument may account for spatial mismatches. The crime map shows only the crimes sites that respondents felt fear towards (robbery and assault). Other forms of crime may occur and trigger fear of being robbed, beaten or attacked, thus crime in general may predominate in the heavily avoided areas. Furthermore, the spatial crime data encompassed the six-month period prior to interviewing. If data over a larger time period was presented, the analysis may show that levels of crime were more strongly associated with avoidance patterns. As discussed in the previous section of the chapter, while spatial cognition is an ongoing process, a person can formulate a cognitive map after a brief encounter with an environment. Hence, a person can take a static ‘snapshot’ of an environment and immediately choose to avoid it (Block, 2000). Downs and Stea (1973) advocate spatial behaviour is habitual. Given this, it is likely people would not return to environments they initially avoided and their current avoidance patterns are the result of ‘snapshots’ produced some time ago. The avoided areas would therefore not coincide with current crime hotspots. As Kings Cross is undergoing rapid change, this may be an important factor explaining the mismatch between fear and crime. Cognitive maps are regarded as incomplete and distorted, due to the subjectivity of spatial cognition (Downs and Stea, 1973). People overestimate distances, particularly the spatial extent of conceptually important areas (Day, 1976). An area one fears and avoids could be considered conceptually important. Thus, respondents may have exaggerated the extent of avoided areas, explaining why fear of crime may be exaggerated in low-crime areas.

4 Researchers agree that people exhibit more fear after dark. Physically, the reduction in visibility and recognition abilities, and the creation of blind spots, shadows and potential places of entrapment could play a role (Fisher and Nasar, 1995; Nasar and Jones, 1997; Painter, 1996; Samuels and Judd, 2002).

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Block (2000) states that people overestimate short distances and underestimate long distances. This may have occurred when the respondents illustrated the avoided areas. It was observed that many respondents roughly circled the areas they avoided, rather than going into specific detail. This encourages more emphasis in the analysis to be placed on the central regions (peaks) of avoided areas, rather than their peripheries (lower-lying areas). Additionally, sharp edges between the areas of high and low avoidance indicate the respondents collectively perceive those streets as specific fear boundaries (namely William Street, Darlinghurst Road, MacLeay Street and Victoria Street). In contrast, when the land’s slope is gentle it indicates the respondents were less accurate in drawing or that collective avoidance is gradual and generalized. This could account for why hotspots B and C, which have defined edges, coincide with a high density of crime incidents, yet hotspot A, which has much softer boundaries, is an area with more dispersed incidents of crime. As discussed in Chapter 3, people experience fear of crime when they encounter certain environmental cues. These cues may be present in areas where there is no crime. Examination of the study site during interviewing revealed that both physical and social environmental cues occurred in a higher density north of William Street. This is consistent with areas of higher avoidance levels. Thus, it is likely the presence of signs of disorder and incivility do influence the mismatch between crime and fear of crime. While this study did not examine the actual presence of environmental cues, it did investigate the perceived presence of environmental cues, which is equally as likely to provoke fear of crime. This is discussed further next. Environmental Cues Triggering Fear of Crime This study is primarily exploratory research into the environmental cues that trigger people to feel afraid of being robbed, beaten or attacked in Kings Cross. Knowing which environmental cues are most likely to trigger fear of crime has promising implications for policy and planning, for example, allowing limited public resources to be validly directed towards combating the most pertinent environmental cues that trigger fear. Table 7.9 lists the 16 social and physical incivilities according to the degree to which they triggered an avoidance reaction. Three columns are shown in the table for the day and night. The first column shows the percentage of the respondents indicating that the particular cue was a factor in triggering their fear of being robbed, beaten or attacked. The percentage is derived from the proportion of respondents adopting avoidance behaviour during the day (n = 138) and night (n = 252), not the sample as a total (n = 381). Ranks were assigned to each environmental cue according to this percentage, with the environmental cue ranked ‘1’ triggering fear of crime in the largest proportion of respondents. These ranks are shown in the second column and were used to order the environmental cues from highest to lowest. Additional ranks are listed in the third column, which were allocated after examining the fear maps for each environmental cue and ordering the cues according to the proportion of respondents avoiding each area of the study site. This later rank reflects the levels of avoidance triggered by each of the environmental cues.

44.93 44.20 42.75 39.13 35.51 34.78 33.33

32.61 25.36 24.64 21.74 21.01 17.39

Low Homeless people Rundown/abandoned buildings Vandalism Spruikers Offensive/degraded shops Sex workers 12 14 13 15 11 16

4 7 6 5 10 8 9 30.56 32.14 28.57 24.60 25.40 19.84

49.60 48.41 45.63 42.46 52.38 35.71 36.51 12 11 13 15 14 16

5 6 7 8 4 10 9

1 2 3

Rank by %

14 11 12 15 13 16

6 4 8 7 5 9 10

2 1 3

Rank by density

8.40 12.07 9.97 8.40 9.19 6.82

16.54 16.01 14.70 13.91 21.78 11.02 12.07

19.42 16.80 17.06

% (n = 381)

Change

Note: Percent is based on the proportion of the sample adopting avoidance behaviour during the day (n = 138) and night (n = 252), not the sample as a total (n = 381).

11 12 13 14 15 16

4 5 6 7 8 9 10

64.29 56.35 55.16

Middle Laneways Rubbish/syringes Loitering people, Areas to hide Poor street lighting Blocked escape Pedestrian absence

3 1 2

63.77 56.52 53.62

Top Drug users Gangs Intoxicated persons 1 2 3

% (n = 252)

Rank by density

% (n = 138)

Environmental cue

Rank by %

Night

Day

Table 7.9 List and rank of environmental cues by percent and density of avoiding respondents indicating that the environmental cue triggered feelings of fear of crime and avoidance behaviour

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Table 7.9 is divided into three sections grouping the top, middle and lowest ranked environmental cues. These groups were identified when examining the avoidance maps. However, the groups also roughly parallel natural breaks that can be observed when looking at the avoidance percentages for each environmental cue. Despite arranging the environmental cues in a slightly different sequence, the rank by avoidance density and the rank by avoidance percentage categorize each environmental cue into the same group. Drug users, gangs and intoxicated persons made up the top group, with the highest proportion of respondents avoiding each area. According to the avoidance percentages, the top three environmental cues for both the day and night were drug users, gangs and intoxicated persons; 64% of the respondents who avoided areas of the study site indicated that the presence of drug users was a factor in triggering their fear of being robbed, beaten or attacked during the day and the night. For gangs, these numbers were 57% (day) and 56% (night). For intoxicated persons, these numbers were 54% (day) and 55% (night). Laneways, areas to hide, loitering people, rubbish/syringes, blocked escape, pedestrian absence and poor street lighting made up the middle group, while rundown/abandoned buildings, homeless people, offensive/degraded shops, vandalism, spruikers and sex workers made up the lowest group. The presence of sex workers was the environmental cue triggering fear of crime in the least amount for respondents for both the day (17%) and night (20%). The last column in Table 7.9 shows the change in percentage of respondents avoiding areas of the study site from the day to the night. This percentage is derived from the whole sample (n = 381). Percent-wise, the most pronounced temporal variation between the day and night fear maps was found with poor street lighting (19%). An assessment of the fear maps also found a marked increase in avoidance between the day and night for all of the 16 environmental cues. This was most pronounced for drug users, intoxicated persons, gangs, rubbish/syringes and poor street lighting. The temporal variation found in the avoidance maps is quite consistent with that found by an examination of the percent-based changes in the proportion of respondents who stated each environmental cue triggered their fear of crime. Mapping the Perceived Presence of Disorder and Incivility To illustrate differences in the proportion of respondents who avoided the study site because of the different environmental cues, four cues are shown here using 3D visualization. Two of the top-three ranked environmental cues, drug users and gangs, were chosen for 3D mapping. Percent-wise, drug users ranked above gangs by 7% during the day and 8% at night. While this is not a statistically significant difference, it is interesting because the assessment of the fear maps ranked gangs well above drug users in terms of the proportion of respondents avoiding each area. The maps also revealed slight areawise differences in the patterns of avoidance for these two environmental cues. In contrast to these highly ranked environmental cues, sex workers were ranked lowest according to avoidance density and avoidance percentage and are also shown

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here in 3D for comparative purposes. Being the highest and lowest ranked environmental cues, drug users and sex workers were additionally chosen for 3D mapping to show patterns of avoidance adopted by the male and female respondents, and visitors and residents. Overall, the maps show that each of the environmental cues triggered the respondents to adopt very similar patterns of avoidance. As discussed earlier, avoidance is generally heightened in the northern half of the study site, specifically in the street blocks making up hotspots A, B and C. While the patterns of avoidance were reasonably consistent across all of the environmental cues, the areas-to-hide avoidance maps were more spatially general than the others. The areas-to-hide map was ranked in the middle grouping in Table 7.9. Areas to hide are also shown using 3D visualization to provide a comparison with the maps of the other environmental cues. The avoidance hardness maps for each of the environmental cues during the night were quite consistent. The average avoidance hardness weighting for the majority of the study site, regardless of the environmental cue in question, was ‘quite hard’. Many of the avoidance hardness maps for the night also had smaller areas where the avoidance hardness weighting was in the middle (mid/don’t know) or top (‘very hard’) of the Avoidance Hardness Index. The ‘mid’ areas generally ran along, or centred over, Darlinghurst Road. The ‘very hard’ areas were generally situated on the outer regions of the study site. For the day, there is a little less consistency between the avoidance hardness maps for each environmental cue. Most of the environmental cues have avoidance hardness weightings ranging from ‘mid’ to ‘quite hard’. A few of the avoidance hardness maps for the day also have areas where the avoidance hardness weighting was in the ‘not very hard’ category. The Perceived Presence of Drug Users: Exploring the Maps Drug users were the top environmental cue triggering fear of crime in terms of total number of survey respondents adopting avoidance behaviours. The avoidance maps for all respondents illustrate that they avoided large areas of the study site because of drug users, and therefore avoidance is high over the entire study site (see Fig. 7.18). The fact that avoidance is high and fairly generalized mirrors hypotheses made by other researchers who imply drug users are connected with crime and therefore create fear of crime (Skogan, 1990). This also lends support for the signal crimes and disorder/incivilities hypotheses. For example, crime is frequently considered a means of financing drug use and drug users might appear threatening and unpredictable, therefore triggering fear of crime (Tulloch et al., 1998a, 1998b). In addition, drug users may not even be construed as rational criminals because they do ‘crazy things that are unnecessary and violent’ (Skogan, 1990). What is more, an amplification effect could cause drug users to trigger high levels of fear and avoidance. With the amplification effect, there is a cumulative impact of numerous weak related signals in close proximity with one another. With regard to drug users, these weak signals could be signs of drug use like syringes or people offering drugs.

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Fig. 7.18 Areas where the respondents stated the presence of DRUG USERS triggered their fear of being robbed, beaten or attacked – during the day and night

Meanwhile, the avoidance mapping also introduces some more enlightening and useful information about public fear of crime in Kings Cross. Despite the fact that some people avoid large areas, others were spatially sensitive, identifying specific areas where drug users triggered their fear of crime. These fear hotspots

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reflect the three peaks displayed in the overall avoidance maps, which is not surprising, considering that drug users are the primary cue triggering fear of crime. However, what is noteworthy for strategies aimed at combating fear of crime is an assessment of these areas of fear in terms of actual presence of drug users or dealers. In relation to drug users, avoidance in the study site was highest in hotspot C, which was avoided by 33 respondents during the day and 76 respondents during the night. Hotspot C begins at William Street, Darlinghurst Road, Roslyn Gardens and Baroda Street. However, avoidance climbs more steadily over Bayswater Road and Roslyn Street to peak over Kellett Street, where the rear exit from the Medically Supervised Injecting Centre (MSIC) is located. Avoidance on Darlinghurst Road was also greatest at the front entrance to the MSIC. These results indicate that the public could be avoiding the area because of an awareness of the existence of the MSIC and a perception or knowledge that drug users frequent its surrounds. This further reflects a conjecture that the public are attentive to the presence of drug users, especially those who are about to use drugs, or may have recently used and are intoxicated upon leaving the MSIC. With such high fear levels, the need for planning could be recommended to offset this fear and avoidance. A response could take the form of boosting the social infrastructure available to the drug users, for example expanding and improving the recovery area in the MSIC so that the users do not have to loiter in the public streets when they are intoxicated. While Darlinghurst Road visibly divides hotspots B and C on the drug users maps, half of the respondents who avoided Darlinghurst Road did so because the presence of drug users triggered their fear. This result reflects the reality of a large number of drug users in the immediate locality. For example, a 1999 study revealed 90% of ambulance overdose call outs occurred with a 300-metre radius of Darlinghurst Road (Van Beek, 2004). This points towards the recommendation for improved social infrastructure to help reduce the number of drug users in the area, such as increased facilities to help users overcome their drug addiction and more robust policing of the drug trade. Fear hotspot A is apparent on the drug users avoidance maps. Avoidance is heightened in two places during the day, one over Sydney Place and the other over the Forbes and Judge Street walkways, which are avoided by a maximum of 22 and 24 respondents respectively. During the night, avoidance peaks at 65 respondents in two places, one between Windeyer Street and Rae Place and the other above Kings Cross Railway Station. Sydney Place was identified as a fear hotspot in a preliminary study comparing areas of fear with sites of recorded crime. A mismatch over this area alerted the police to, and substantiated, intelligence that drug dealing was regularly occurring in the area. This displays the value of the avoidance mapping method as a police tool. The fact that drug users triggered fear of crime in this area should be addressed. This is largely because of the location of Plunkett Street Public School, situated next to Sydney Place, and the subsequent problems arising from dealing that could impact on school children. Drug-related crime should additionally be addressed because community members may not be accessing social infrastructure in the middle of fear hotspots, which includes a public vegetable

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garden, playground, BBQ setting and tennis court. However of note, drug users trigger fear and avoidance in this area predominantly during the night and much less so during the day when these facilities would be in use and school children about. The avoidance mapping further identified the presence of drug users caused people to avoid hotspot B, specifically Earl Place and Springfield Mall (avoided by a maximum of 28 respondents during the day and 72 respondents at night). It is acknowledged that the street-based drug scene in Australia is focused in Springfield Mall and Plaza, where drug supply and use is concentrated (CoSC, 2005e; NSW Legislative Assembly Hansard, 2001; Van Beek, 2004). Likewise, The City has already acknowledged the ongoing incidence of antisocial behaviour in this vicinity and is already taking action to discourage loitering and improve the public amenity, something these results confirm is necessary to help reduce fear of crime (CoSC, 2005g). As the Village Centre and a hub for retail activity, pedestrian attendance in Springfield Mall is particularly important to the vitality of Kings Cross. Therefore, the social programmes mentioned in Chapter 4, which are designed to ‘activate the street life’ and could potentially deter the occurrence of drug dealers or users, are recommended. Also of note is the apparent lack of a relationship between fear of crime and the location of syringe bins for drug users. In some areas fear of crime reflects the location of syringe bins and in others it does not. The maps show that there are some syringe bins located in high-fear areas, which makes it appear fear of crime could be related to the presence of syringe bins and drug users making use of them. However, there are also locations with syringe bins and low fear of crime. This could be brought about by a number of reasons. First, it could mean that the respondents did not go to those locations for reasons other than fear of crime. It could also be that the respondents were not aware of the location of the syringe bins or the presence of drug users near them. In contrast, the respondents may have been aware of the syringe bins and drug users yet did not find their presence threatening in those locations. If this were the case, it could suggest that people react differently to signs of disorder, for example drug users, when there is evidence that the disorder is being monitored and managed, like through the establishment of syringe bins. This may have implications for policy and planning and therefore looking at the secondary effects of disorder management on fear of crime may be an area worthy of future research. Lastly, the low levels of fear around certain syringe bins could mean that drug users do not operate near these bins. This last possibility has also implications for The City, and it may be worthwhile that the bins are assessed in terms of the appropriateness of their location. Another dissonance between the avoidance maps and the actual location of drugrelated crime occurs in Roslyn Street. Avoidance is low along Roslyn Street despite previous reports that drug dealers regularly congregated there (NSW Legislative Assembly Hansard, 2001). However, Roslyn Street is considered to be a major venue for cannabis, rather than heroin dealing (Darcy, Personal communication, 17/3/04). Therefore this dissonance is not surprising considering the fact that the

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respondents were asked whether the presence of junkies,5 rather than drug users in general, triggered their fear of being robbed, beaten or attacked. Nevertheless, this uncovers an opening for research focusing on how different types of drugs and drug-related activity differentially affect fear of crime and avoidance. Some additional avenues for further research have come about from analyzing the drug users avoidance map. By breaking down the drug users category into subgroups more information on public fear of crime can be obtained. For example delineating drug users as drug dealers, known drug users or people who look like drug users could potentially provide some more useful results with regard to policy and planning. The police could target areas where drug dealers are perceived to operate. Areas where drug users are located, when they do not spatially coincide with drug dealers, could be improved through the provision of better social infrastructure. Furthermore drawing on the signal crimes perspective, future survey questions could be more qualitative when asking respondents how drug users trigger their fear of crime. This might include asking if the drug users must be outwardly verbally or physically threatening to trigger fear or if drug users trigger fear even when they appear quiet and unobtrusive. The Perceived Presence of Gangs: Exploring the Maps The avoidance maps illustrate that gangs triggered the respondents to avoid large areas of the study site, and therefore avoidance was high overall (see Fig. 7.19). Aggregate avoidance was greatest over hotspots B and C. For hotspot B this was particularly the case along Earl Place, which is avoided by a maximum of 34 and 72 respondents during the day and night respectively. Peak C begins at William Street, Darlinghurst Road, Roslyn Gardens and Baroda Street. However, avoidance climbed more steadily immediately over Bayswater Road and within the area bounded by Roslyn Street and Ward Avenue to peak over Kellett Way and Kellett Street (avoided by 34 and 75 respondents during the day and night respectively). Hotspot A is evident, however not clearly defined. During the day, avoidance reaches a maximum of 29 respondents in the areas immediately surrounding Sydney Place and Stephen Street. During the night, avoidance reached a maximum of 65 avoiding respondents along Windeyer Street and the western side of Victoria Street where it intersects Earl Street. This finding that avoidance is high due to the perceived presence of gangs could suggest that fear of crime experienced by these respondents is irrational given the current objective levels of risk from gang-related crime. Yet, coming to this conclusion would be premature without considering the historical and situational context of the area. Kings Cross does have a history of gangs and gang-related organized crime, knowledge of which assists in our understanding of the high levels of fear. For example the Razor Gang wars, which occurred over the control of prostitution,

5

Junkies are colloquially known as intravenous drug users, users of other illicit drugs and drug dealers (Darcy, Personal communication 12/3/04).

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Fig. 7.19 Areas where the respondents stated the presence of GANGS triggered their fear of being robbed, beaten or attacked – during the day and night

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‘sly grog’ and cocaine were prominent in Kings Cross between the 1920s and 1930s and to a lesser extent until the 1960s (SESIAHS, 2005).6 ‘Bikie’ gangs, linked to the illicit drug trade, have further been associated with Kings Cross from the 1950s.7 Kings Cross has more recently seen instances of gang-related organized drug crime in the 1980s and 1990s (Wood, 1997).8 This history could account for the respondents’ fear of crime due to gangs at the time of interviewing. This implies that public fear of crime could be based predominantly on past events and possibly an outdated reputation of the area as ‘gangland’. In this case public perception, rather than the reality of actual gangs, would be responsible for fear and avoidance and could be targeted in fear-reduction strategies. This may have implication for previous research, for example after finding that fear of crime in Glasgow was not reduced following street lighting enhancements, Nair et al. (1993) concluded that fear of crime is an intractable and resistant problem. An historical assessment of the area and the effect of past experiences or levels of disorder on current levels of fear and avoidance behaviours may have assisted their study. Comparing the research results with ideas about the presence of gangs can lead to further observations about public fear of crime. For instance, the results indicate it may not be the frequency with which the public see or hear about gangs but rather, drawing on the signal crimes perspective, the denotative meaning, connotative meaning and signal value of gang-related crime. The fact that avoidance due to the presence of gangs was high and quite generalized in Kings Cross suggests gangs are an environmental cue with a high signal value, probably because the crimes they denote are severe. Gang-related crimes are often conceptualized as being violent, are perceived as serious in nature, connote increased risk and elicit greater fear (Clark, 2003). This may also account for Katz et al. (2003) finding that fear of gangs in Arizona was equally high for residents of high- and low-gang areas and not simply a consequence of objective threat or prior experiences of victimization. Katz et al. (2003) explain this finding by insinuating that residents of high-gang areas do not have increased levels of fear because they recognize that gangs are more likely to victimize other gang members rather than them. However, this explanation does not account for the high levels of fear experienced by residents of low-gang areas, which may be a result of the possible high signal value of gangs and gang-related crime. 6

The Razor Gangs’ Kellett Street riot of 1929, reportedly one of Sydney’s most vicious riots, is now acknowledged in The City’s pavement signage on the corner of Kellett Street and Bayswater Road (SESIAHS, 2005). 7 The history of such motorcycle groups and their prominent members is acknowledged on a tree plaque in Kings Cross. 8 Various newspaper articles published after the interviewing for this study also indicate that gangrelated crime still occurs in Kings Cross. For example, there was the fatal gang shooting of a member of the Bandidos Motorcycle Club in Chapel Street during April 2006 and the shooting of a Kings Cross club bouncer by a Hells Angel bikie member in February 2006 (Braithwaite and Baker, 2007; Cummings, 2007). Referring to a police crackdown on bikies through Operation Ranmore, the Gangs Squad Commander Scott Whyte reported that ‘by week three [of the operation] there was no evidence of bikies . . . in Kings Cross. That’s something that hasn’t been seen for many years’ (Braithwaite, 2007).

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Furthermore, as the avoidance maps indicate, there is a strong behavioural effect resulting from this environmental cue. This is consistent with Lane and Meeker’s (2000) findings from their qualitative survey based in California, which found that respondents reported that they modified their behaviour when entering gangland during the day and completely avoided certain streets at night to keep away from gangs. Despite the increase in avoidance during the night that was documented by Lane and Meeker (2000), it is interesting to note that the difference in avoidance levels during the day and night on the gangs avoidance maps is comparatively less than that for most of the other environmental cues. This suggests that the temporal component to people’s fear of crime due to the presence of gangs is not very prominent, which could indicate that respondents think gang-related victimization is equally as likely during the day as at night. Alternatively, the findings could suggest that people’s fear of being a victim of serious gang-related crime is severe enough to warrant avoidance during the day too. These results reflect the premise that gang culture is based on and contributes to public fear and intimidation (Lane and Meeker, 2003). Skogan (1990) indicates that gangs can be perceived as ranging from casual groups of loitering youths to more threatening groups engaging in public drinking, drug use or harassment of passers-by, and to real organized ‘fighting squads’. Interpretation of the research results could be assisted by further investigations into public perception of gangs and determining whether the public conceive gangs in Kings Cross as only representing members of organized criminal groups or also as groups of people loitering in a localized area. If the latter is true, the high levels of fear and avoidance are understandable considering the reputation Kings Cross has as a ‘hang out’ for groups of threatening people who may appear to be gang members. For example, a restaurant owner in Kellett Way has reported that gangs of young men harass pedestrians in the area (NSW Legislative Assembly Hansard, 2001). Occasional media exposure of criminal incidents occurring in Kings Cross, for example the gang rape of a lady in 1979, may also contribute to the fear of crime triggered by gangs and the consequent avoidance reaction. However despite this, these severe criminal incidents are not necessarily a frequent or ongoing occurrence. The Perceived Presence of Sex Workers: Exploring the Maps Sex workers triggered the least fear of crime of all of the environmental cues, in terms of total number of survey respondents adopting avoidance behaviours. The avoidance maps reflect this by displaying low avoidance density during the day and night in comparison to the other 15 environmental cues (see Fig. 7.20). The low avoidance density is expected, as sex workers are not frequently discussed in fear-of-crime literature as being threatening in their own right. During the day avoidance was very low and constant over the study site, with peaks A, B and C barely evident. Despite being relatively low, avoidance is slightly higher along Darlinghurst Road (avoided by 5–7 respondents) and in the street block that makes up hotspot C (a maximum of 11 respondents) than in the remainder of the study site. As Darlinghurst Road is the primary location where daytime sex workers solicit potential clients, this result indicates fear of crime and avoidance reflects the

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Fig. 7.20 Areas where the respondents stated the presence of SEX WORKERS triggered their fear of being robbed, beaten or attacked – during the day and night

actual presence of sex workers and commercial brothels. Similarly, the avoidance in hotspot C reflects the location of a sex workers’ outreach centre and the MSIC on Darlinghurst Road. It is widely acknowledged street prostitution is intimately connected with drug use and dealing, and therefore it is likely respondents could link the presence of sex workers with the MSIC (ESNA, Undated).

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Avoidance in these two areas and over the remainder of the study site increases during the night. During the night, Darlinghurst Road provides a small partition between hotspots B and C, being avoided by 22–26 respondents. Avoidance over hotspot C increased from 11 during the day to 32 respondents during the night. This increase is to be expected given the increase in number of sex workers operating in the afternoon and evening hours. However avoidance increased more notably around Forbes Street near William Street, and along Darlinghurst Road south of William Street. These two sites fall within another known area for sex workers, which is bounded by Bourke, William, Victoria and Burton Streets (Perkins, 1991). While avoidance was also slightly heightened south of William Street, along Victoria Street and in an area east of Darlinghurst Road, these streets are not documented as having a presence of sex workers. In converse, avoidance is not particularly noteworthy along Bourke and Liverpool Streets, where illegal street prostitution is regarded as a problem (ESNA, 2002). However despite the low overall fear levels and slight avoidance hotspots in some areas, the avoidance maps for sex workers do reveal large avoided areas that are surprisingly general. Drawing from the disorder/incivilities thesis, this could be because the presence of sex workers can be considered indicative of a breakdown in standard social norms and possibly law enforcement, and therefore may well instigate public perceptions of crime and fear of crime. In line with this, ESNA9 states prostitution can be accompanied by heroin trafficking in the streets; the reckless disposal of syringes; large volumes of litter; the use of laneways as places for sex; open-air toilets and ‘shooting galleries’; the menacing presence of ‘minders’ who may be involved in drug dealing and ‘turf wars’; the verbal and physical abuse of residents by the sex workers; the frequent accosting of female residents by clients, who assume that all females in the streets are sex workers; and the threatening behaviour and presence of young, often drunk, men who are attracted to the street as clients or voyeurs (ESNA, 2002). Following on, prostitution has also been associated with crime, for example there have been numerous rapes and assaults of street sex workers by their clients (ESNA, 2002). It is perhaps these potential accompanying factors, which may also independently signify crime, that account for the generalized fear of crime triggered by sex workers. As mentioned in the previous section, the City’s Adult Entertainment and Sex Industry Premises Development Control Plan 2006 aims to reduce any negative side effects arising from the presence of adult entertainment and sex industry premises (CoSC, 2006h).10 For example, the plan permits the location of sex industry premises in areas that optimize the safety and security of staff and visitors. Sex

9 ESNA also describes multiple other dangers and nuisances arising from the presence of street sex workers (ESNA, 2002). 10 This includes commercial brothels, local business brothels, safe house brothels for street workers, escort agencies offering sexual services, restricted premises and sex on premises venues, bondage and discipline venues, swingers clubs and the like, but does not include private sex workers home-business premises (CoSC, 2006h).

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industry sites should further ensure that the premises do not have an adverse impact on the character or amenity of the area and neighbouring properties (CoSC, 2006h). However, ESNA maintains the City’s plan does not cover the large number of homebusiness brothels that do not require development approval and do create adverse effects (ESNA, 2002). Moreover, Sections 19 and 19A of the Summary Offences Act 1988 declare street prostitution and curb crawling ‘near or within view from a dwelling, school, church or hospital’ as an offence (ESNA, 2002). ESNA similarly argues that this act is not enforced and therefore street prostitution is left uncontrolled (ESNA, 2002). ESNA hopes for a unified effort between the community, police and government to remove prostitution from residential zones to mixeduse, commercial and industrial zones. While the results from the avoidance maps show sex workers are by no means a priority environmental cue that triggers fear of crime, the fact that avoidance still occurs, especially in residential zones, favours the suggestions presented by ESNA. The Perceived Presence of Areas to Hide: Exploring the Maps The avoidance patterns, adopted by respondents who indicated areas to hide triggered their fear of crime, were extremely general during the day (see Fig. 7.21). As in the other avoidance maps, avoidance is comparatively low south of William Street and along Darlinghurst Road and Macleay Street. However, unlike in the other avoidance maps, minimal differentiation was made between peaks A, B and C and the remainder of the study site north of William Street. With surprisingly little spatial variance of avoidance during the day, it is difficult to see how an analysis of this avoidance map can affect policy and planning. This is disappointing given that the fear mapping studies conducted by Nasar et al. (1993); Fisher and Nasar (1995) and Nasar and Jones (1997) found that specific areas with low prospect, high concealment and blocked escape, which included areas to hide, were significantly associated with increased levels of fear of crime. Fisher and Nasar (1995) propose that potential victims may feel greater exposure to risk and loss of control over their immediate environment in these areas and therefore experience heightened fear of crime. While this may be true, the results from the current study could suggest that the levels of fear of crime triggered by areas to hide in Kings Cross may not warrant micro-level changes in avoidance. Nevertheless with no known reports or studies investigating the whereabouts of areas to hide in Kings Cross, it is difficult to compare these fear maps with the existence of areas to hide in reality. An assessment of the areas may or may not reveal that the respondents’ perceptions reflect the physical environment. If their perceptions reflect the physical environment, the avoidance mapping will have been useful for identifying areas where development should focus on the removal of hiding and entrapment spots. If not, this could indicate that respondents do not successfully visualize the areas they avoid when considering this environmental cue. It could also indicate that the thought of areas to hide affects public fear and avoidance behaviours even when areas to hide are not present in reality.

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Fig. 7.21 Areas where the respondents stated the presence of AREAS TO HIDE triggered their fear of being robbed, beaten or attacked – during the day and night

Fear Experienced by Different Socio-Demographic Groups Table 7.10 shows that only one (the respondent’s sex) of the five socio-demographic variables was associated with fear of crime. There was no significant association between fear of crime and age, housing tenure type, income or residential status. The respondent’s sex was strongly related to fear of crime (p = 0.0057), with females

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Table 7.10 Chi-square analysis of socio-demographic variables and fear of crime

Socio-demographic variable

Percent feeling unsafe or afraid (Global measure)

Percent adopting avoidance during the day

Percent adopting avoidance during the night

Sex Male Female

p = 0.0057 28.86 42.53

p = 0.0001 25.37 44.89

p = 0.0001 62.77 82.1

Age (over 18 years) 18–29 30–59 60+

p = 0.0526 42.11 36.00 25.58

p = 0.2712 29.82 40.34 29.89

p = 0.1031 69.03 71.93 57.47

Housing tenure Government housing Non-owner-occupier Owner-occupier Backpacker Community shelter

p = 0.3689 50.00 35.33 31.11 34.48 37.5

p = 0.6242 26.47 37.5 35.56 31.03 12.5

p = 0.3162 55.88 71.08 68.94 57.14 62.5

Residential status Resident Visitor

p = 0.8829 35.63 34.9

p = 0.1092 40.23 30.05

p = 0.074 73.56 62.57

Income (per annum) $0–$15,599 $15,600–$41,599 $41,600–$77,999 $78,000 or more

p = 0.4864 33.02 41.53 32.97 33.33

p = 0.8427 33.02 37.82 30.77 39.39

p = 0.0646 58.49 76.27 69.32 63.64

Note: While percentages are shown in this table, note that the chi-square analysis calculations were based on the raw data numbers and not percentages

being more fearful than males. The results show that 43% of females and 29% of males indicated that they had felt fearful when walking in Kings Cross. In terms of avoidance, 45% (day) and 82% (night) of women avoided one or more areas in Kings Cross during the day and night respectively, as opposed to 25% (day) and 63% (night) of men. Mapping Avoidance Adopted by Selected Groups The avoidance maps also provided insights into how different demographic groups experience fear of crime and react to different environmental cues. This was demonstrated by exploring the drug users and sex workers avoidance maps according to the residential status and sex of the respondents. In contrast to the chi-square analysis, which indicated that residents of and visitors to Kings Cross have similar levels of fear, the avoidance maps revealed that they adopted very different avoidance patterns in response to the perceived presence of sex workers and drug users. While the chi-square analysis showed that the female respondents had much higher levels

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of fear than the male respondents, the avoidance maps illustrated that overall patterns of avoidance for the males and females were more similar than expected given the chi-square result. This exploration of the avoidance maps demonstrated that fear mapping can provide new information concerning public fear of crime that is not apparent through traditional statistical methods. Fear of Crime Between Residents and Visitors The results show that the proportion of residents (36%) and visitors (35%) who had been afraid when walking in Kings Cross is very similar. This study provides a specific analysis of fear of being robbed, beaten or attacked. Statistically, the residents of the inner-east are very slightly more fearful of these crimes, with 40% and 74% of residents avoiding areas during the day and night respectively, as opposed to 30% and 63% of men. The chi-square analysis further revealed that no significant relationship was found between residential status and fear of crime. However, Figs. 7.22, 7.23, 7.24 and 7.25 indicate that there were very different patterns of avoidance adopted by visitors and residents in response to both sex workers and drug users triggering their fear of crime. Residents Use Local Knowledge to Avoid Specific Areas Figures 7.22, 7.23, 7.24 and 7.25 show that the residents were limited in their avoidance. During the day, few residents adopted avoidance behaviour due to the presence of sex workers, with only small parts of the study site actually being avoided (see Fig. 7.24). Aggregate avoidance was at its limit alongside Darlinghurst Road, which was avoided by three to four residents. This area reflects the actual whereabouts of daytime sex workers. During the night, aggregate avoidance was greatest in hotspot C over Kellett Way (avoided by 8–15 residents), hotspot B (avoided by 9–12 residents) and Darlinghurst Road (avoided by 8–10 residents) (see Fig. 7.25). Those specific areas with strip clubs, brothels and x-rated shops had pronounced levels of avoidance among the residents at night. Residents also avoided localized areas due to the presence of drug users (see Figs. 7.22 and 7.23). For example, the steep slope of the land around peak C illustrates that the residents may have identified the MSIC and avoided a specific area around it during the day and night. Avoidance was specifically heightened for the residents in Kellett Street and Kellett Way (where drug users enter and exit the MSIC), which were avoided by 14 and 39 residents during the day and night respectively. Avoidance was also heightened over Sydney Place in hotspot A (avoided by 9 and 31 residents during the day and night respectively), over Butler Stairs and in Walla Mulla Park (where syringe bins are located and where drug users are known to congregate). Increased avoidance at these sites indicates an intimate knowledge of the presence of drug users. Similarly, during the day the residents avoided Darlinghurst Road nearly as much as they did the neighbouring street block

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Fig. 7.22 Areas the RESIDENTS and VISITORS stated the presence of DRUG USERS triggered their fear of being robbed, beaten or attacked during the DAY

(avoided by 6 residents during the day and 20–27 during the night). This further points towards a residential awareness of drug users on Darlinghurst Road and suggests that during the day residents may be traversing through and patronizing areas other than their own neighbourhood centre.

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Fig. 7.23 Areas the RESIDENTS and VISITORS stated the presence of DRUG USERS triggered their fear of being robbed, beaten or attacked during the NIGHT

Visitors Indiscriminately Avoid Large Areas Visitors to Kings Cross account for a majority of those respondents who indicated that the presence of sex workers and drug users triggered their fear of crime. The visitors also avoided a much broader area than the residents, and were more spatially general in their avoidance patterns. This was evident on the maps for both

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Fig. 7.24 Areas the RESIDENTS and VISITORS stated the presence of SEX WORKERS triggered their fear of being robbed, beaten or attacked during the DAY

the sex workers and drug users (see Figs. 7.22, 7.23, 7.24 and 7.25), although an increase in avoidance over Darlinghurst Road was evident on the sex workers map, indicating that they too identified this as the primary location for sex workers. The visitors did not have heightened avoidance of Darlinghurst Road due to the presence of drug users during the day, which was unlike the residents maps (4 and 19

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Fig. 7.25 Areas the RESIDENTS and VISITORS stated the presence of SEX WORKERS triggered their fear of being robbed, beaten or attacked during the NIGHT

visitors avoided the sides of Darlinghurst Road during the day and night respectively). Instead the visitors had increased avoidance over hotspots B and C during the day (both avoided by a maximum of 8 visitors) and hotspot A during the night (avoided by 12 visitors). However, avoidance in these hotspots was not well defined in comparison to avoidance in the surrounding area (see Figs. 7.24 and 7.25).

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This is the same for the avoidance by visitors due to the perceived presence of drug users. While avoidance for the visitors was greatest in hotspots A (avoided by up to 16 visitors during the day and 35 during the night) and B (avoided by 21 and 40 visitors at Earl Place during the day and night respectively), neither peak A nor peak B was visible in comparison to levels of avoidance in the surrounding area. Peak C was avoided by a maximum of 19 and 38 visitors during the day and night respectively (see Figs. 7.22 and 7.23). Overall these results suggest that residents’ fear of crime is more consistent with the reality of risk of victimization than visitors’ fear of crime. This is likely because residents are familiar with their environment, as inferred in other studies (see Ferraro and LaGrange, 2000; Gray and O’Conner, 1990; Gilchrist et al., 1998). The results support the proposal that knowledge of actual crime rates, or in this case the presence of drug users, can affect perceptions of risk. Knowledge also plays a role in reducing fear of crime (Garofalo, 1981). It consequently strengthens the argument that community involvement in combating crime and the dissemination of crime-prevention information through newsletters and meetings could be effective in further reducing fear of crime. Similarly, the results also mirror studies that propose residents with a strong sense of place attachment to their home and neighbourhood perceive fewer incivilities and have lower fear of crime (Brown et al., 2003).11 The results additionally insinuate that visitors, who may be less familiar with the environment, are likely to avoid a larger area than is perhaps warranted by the risk of victimization in order to reduce their fear levels. If this is the case, it is difficult to judge whether something should be done to change the extent of this method of risk minimization. However, the results could also mean that visitors’ fear of crime and consequently patterns of avoidance are based more on the reputation of the area rather than on the actual presence of drug users. In this situation, improving the reputation of Kings Cross could help reduce fear of crime in the region. Fear of Crime Between Men and Women A larger proportion of women were fearful, with 43% of females and 29% of males indicating they had been afraid when walking in Kings Cross. These percentages confirm previous trends12 (Gray and O’Conner, 1990). This study provides a specific analysis of fear of being robbed, beaten or attacked. Statistically, women are more fearful of these crimes, with 45% and 76% of women avoiding areas during the day and night respectively, as opposed to 25% and 58% of men. The chi-square

11 Those residents who have strong place attachments are often those who have resided in the neighbourhood through times of decline and high residential turnover, are older and own their own homes (Brown et al., 2003). 12 Women’s comparative fearfulness is the most consistent result within the research field (see also: Gibson et al., 2002; Hanson et al., 2000; Kanan and Pruitt, 2002; Toseland, 1982; Riger, 1978 [Braugart et al., 1980; Lebowitz, 1975 cited in Clarke and Lewis, 1982]; [Anderson and Leitch, 1996; Mirrlees-Black et al., 1996; Pain, 1993 cited in Gilchrist et al., 1998]; LaGrange and Ferraro, 1989; Gray and O’Conner, 1990; Pain, 2000).

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analyses found that the sex of the respondent was significantly associated with fear of crime (p = 0001), Overall, the spatial analysis confirms that more women than men adopt avoidance behaviour because of fear of these crimes in specific areas (see Figs. 7.26, 7.27, 7.28 and 7.29). On average, the female respondents also tried harder to avoid those areas.

Fig. 7.26 Areas that MALES and FEMALES stated the presence of SEX WORKERS triggered their fear of being robbed, beaten or attacked during the DAY

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Fig. 7.27 Areas that MALES and FEMALES stated the presence of SEX WORKERS triggered their fear of being robbed, beaten or attacked during the NIGHT

The difference is best indicated by looking at the maps indicating that the presence of sex workers triggered fear of crime (see Figs. 7.26 and 7.27). In addition to the differences in overall avoidance levels, the avoidance maps point out some subtle differences in the patterns of avoidance due to the presence of sex workers. Those few males that adopt avoidance behaviour do so mainly along Darlinghurst Road

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7 The Kings Cross Study

Fig. 7.28 Areas the MALES and FEMALES stated the presence of DRUG USERS triggered their fear of being robbed, beaten or attacked during the DAY

(avoided by a maximum of 5 male respondents during the day and 10 during the night). The majority of the remainder of the study site was avoided by less than three male respondents during both the day and the night (no male respondents avoided the south and north east of the study site during the day). For the female respondents, avoidance was also greatest along Darlinghurst Road during the day and night due to the perceived presence of sex workers (avoided

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Fig. 7.29 Areas that MALES and FEMALES stated the presence of DRUG USERS triggered their fear of being robbed, beaten or attacked during the NIGHT

by 5 and 16 female respondents during the day and night respectively). However, during the night avoidance was very broad and generalized, extending into much of peak B and particularly peak C (avoided by 9 female respondents during the day and 23 at night). These slight differences may also reflect that the females were being more cautious than the males by avoiding much larger areas.

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7 The Kings Cross Study

The avoidance maps for the drug users also show differences and similarities in the avoidance behaviours adopted by the two sexes (see Figs. 7.28 and 7.29). During the day, avoidance patterns adopted by the male and female respondents are varied. The female respondents were quite spatially general in their avoidance patterns. While avoidance was highest in the three main hotspots (hotspot A was avoided by a maximum of 12 females, hotspot B by 17 and hotspot C by 21), the slope of the surface shows that avoidance was relatively constant throughout the study site. This is different from the avoidance adopted by the male respondents during the day, who have more clearly defined levels of increased avoidance over hotspots A, B and C. For the male respondents, aggregate avoidance during the day was greatest in peak B, which was avoided by 16 males over Earl Place. Hotspots A and C were avoided by a maximum of 11 and 12 males respectively. An interesting difference is that Fitzroy Gardens seems to portray a distinctly safe area on the avoidance map for the female respondents. This could reflect propositions that signs of ‘naturalness’ and vegetation, for women at least, help to create a sense of safety and reduce fear of crime (Kuo and Sullivan, 2001; Nasar, 1998). However, despite these daytime differences in avoidance, during the night the avoidance patterns adopted by the male and female respondents in response to the presence of drug users are more similar. For the males, aggregate avoidance during the night was greatest in hotspot C, which is defined by two peaks. The first of these illustrates a quick increase in avoidance from approximately 20–25 males over Bayswater Road. The second illustrates another sharp increase in avoidance to 38 males directly over Kellett Way. A peak of 36 avoiding males also defines hotspot B, predominantly along Earl Place. Aggregate avoidance reached a maximum of 28 males over Woolloomooloo, with the gradient of this peak being quite gradual. Between 17 and 28 males avoided Darlinghurst Road. It is clear from the night maps that fear triggered by drug users is not solely a women’s problem. The spatiality of men’s fear and patterns of avoidance are not overly different from women’s. Women’s Heightened Fear of Crime Increased fear of crime among women contrasts statistics showing they are less likely to be victimized than men (Nelson et al., 2001; Hanson et al., 2000; MirrleesBlack and Allen, 1998). This ‘fear-victimization paradox’ prompted the following explanations for women’s heightened fear (Clarke and Lewis, 1982; Hanson et al., 2000; LaGrange and Ferraro, 1989). Accounting for women’s increased fear of crime is a number of proposed physical vulnerabilities. These are that women may believe that they do not have the physical strength or self-defence skills necessary to resist or flee such an attack by a drug user (Garofalo, 1981; Gilchrist et al., 1998; Gray and O’Conner, 1990; Riger, 1978; Smith and Tortensson, 1997; Toseland, 1982; Will and McGrath, 1995). Consequently, women may also consider themselves a more attractive target than men and feel additionally fearful (Gilchrist et al., 1998). Furthermore, women may feel like they are more likely to be injured than their male counterparts during

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victimization (Garofalo, 1981; Riger, 1978; Smith and Tortensson, 1997). In contrast, men are socialized to believe they can physically resist and recover from an attack and therefore could have lower levels of fear than women (Gilchrist et al., 1998; Smith and Tortensson, 1997). It is not surprising that fear of crime during the day was higher for the female than the male respondents. As mentioned previously, fear triggered by drug users could arise because they are often regarded as being threatening, unpredictable and likely to attack and rob passers-by. Drawing on notions of social vulnerability, women are said to fear maledominated areas like ‘red light’ districts (Koskela, 1999). This is partly attributed to the idea that women can be socialized to fear strangers, men and public spaces (Gilchrist et al., 1998).13 Hale (1996) similarly comments that women have been socialized to feel dependent on men and powerless in society, particularly in such male-dominated areas (Katz et al., 2003). A number of researchers further suggest this powerlessness causes women to perceive they have less control over their personal space and the public domain (Pain, 2000; 1991; Pain, 1993 cited in Gilchrist et al., 1998; Toseland, 1982). These vulnerabilities consequently elicit feelings of being at greater risk of victimization than men. Thus women have elevated levels of fear of crime. With the large number of brothels and other adult services targeting male clients, Kings Cross can be considered a male-dominated area that could therefore provoke fear in women.14 This could be particularly the case for those specific sites in which sex workers are perceived to be present. Thus these sites would trigger heightened fear of crime in the female respondents. Women’s heightened fear of being robbed, beaten or attacked could originate from the inclusion of sexual assault in these crimes, the one type of crime that women are more likely to experience than men (Hanson et al., 2000). Women’s fear of sexual assault, particularly rape, is high because such crimes are perceived to be extremely serious and relatively likely, particularly in environments with sex workers (Braumer, 1978 cited in Gray and O’Conner, 1990; Warr, 1985). Given the point made in the previous section that sex workers are often accompanied by ‘menacing’ males, it is expected that sex workers would trigger fear of being robbed, beaten or attacked among women (ESNA, 2002). When thinking about sex workers, sexual acts and ‘menacing’ males the female respondents could become fearful of being sexually assaulted. They would therefore be inclined to adopt avoidance behaviours. In contrast, men do not generally consider themselves likely victims of sexual assault. Their fear is lower and therefore it is expected their levels of avoidance would be lower than that of the female respondents, as was the case in this study (Hanson et al., 2000; Gilchrist et al., 1998; Riger, 1978). Women may be more emotionally affected by crime than men due to additional indirect experiences of victimization, through personal communication or media

13 For further information on women’s increased social vulnerabilities see Katz et al., 2003; Pain, 2000; Gilchrist et al., 1998; and Toseland, 1982. 14 Additionally, 87.5% of the ‘persons of interest’ in assaults in inner Sydney are male (Briscoe and Donnelly, 2001).

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reports involving female victims (Ditton and Duffy, 1982 and Stanko, 1987 cited in Koskela, 1999; Winkel and Vrij, 1990 cited in Gilchrist et al., 1998). This may be as traumatic as direct victimization (LaGrange and Ferraro, 1989). Women frequently experience low-level victimization, for example verbal harassment, which reminds them of their susceptibility to attack and intensifies their fear (Junger, 1987 and Stanko, 1990 cited in Gilchrist et al., 1998; Smith and Tortensson, 1997). Women’s elevated levels of collective avoidance may be because women are more likely than men to exercise this reaction. There are differences in the precautions men and women employ to prevent victimization (Warr, 1985). Avoidance behaviours are more commonly employed by women (Pain, 2000; Valentine, 1989; Warr, 1985). This may be due to a decreased willingness to take risks, which may account for women’s low victimization rates (Hale, 1996 in Katz et al., 2003; Koskela, 1999; Valentine, 1992 cited in Koskela, 1999). Men’s Abnormally High Fear of Crime While men’s fear of crime is comparatively lower than women’s, a large proportion of men are fearful and adopt avoidance behaviours. This contradicts usual statistical reports that men’s fear is low (Gilchrist et al., 1998; Smith, 1987; Warr, 1985). It is theorized that men believe they can physically resist an attack or that such societal values of invulnerability prevent them from admitting to feeling fear (Clemente and Kleiman, 1977; Crawford et al., 1990 cited in Pain, 2000; Gilchrist et al., 1998; Smith and Tortensson, 1997; Young, 1997 cited in Nelson et al., 2001). This study supports this assumption. Only 28.5% of males indicated they had felt fearful in Kings Cross and yet a maximum of 58% avoided areas due to fear of being robbed, beaten or attacked. This suggests men fail to admit to fear when asked directly and do when avoidance is the focus. This may also account for men’s fear being comparatively high in qualitative studies enabling them to talk more about their reactions, rather than definitively admitting to fear (Gilchrist et al., 1998; Pain, 2000). Regardless, 28.5% of men indicating they had felt fearful is a high proportion. This insinuates men may be changing as a group and are more willing to admit their vulnerabilities, which could be unique to this area. In contrast, male respondents may have been aware of high victimization risks and without a comparative study these assumptions cannot be justified. It is clear from the statistics and spatial maps that fear is not only a women’s problem. The male and female respondents collectively avoided the same three areas in the study site (peaks A, B and C), showing that spatiality of men’s fear and patterns of avoidance are not overly different from women’s (see Figs. 7.26, 7.27, 7.28 and 7.29). The implication of this is that some study methodologies fail to identify fear in males quite as well as this survey. This signals the need for ‘more sensitive qualitative understanding’ of men’s fear, particularly through spatial analyses (Gilchrist et al., 1998). Additionally, it stipulates that strategies aimed at combating fear should do so from a variety of perspectives (Gilchrist et al., 1998).

Integrating the Fear Mapping Results with Policy and Community Crime. . .

221

Integrating the Fear Mapping Results with Policy and Community Crime and Fear-of-Crime Prevention The study finds that people are afraid of crime in Kings Cross and provides confirmation that people do react through avoidance when they experience this fear. The avoidance levels in Kings Cross are high, with 36% and 66% of the respondents avoiding at least one area of the study site during the day and night respectively. The literature suggests that these elevated levels of fear could have numerous negative social, physical and economic consequences for Kings Cross. For instance, the economic growth of the area may be being hindered through reduced numbers of business patrons. Fear of crime and avoidance may also be disrupting neighbourhood cohesion and the sense of community among Kings Cross residents. Likewise, it could be creating opportunities for disorderly behaviour and serious crime because of the potential reduction in natural surveillance and social control, which could further encourage greater levels of fear and avoidance. While it is not definite that this will occur, the potential for these and other problems seem to warrant action being taken to address both crime and fear of crime in Kings Cross. The avoidance mapping has successfully revealed three main fear hotpots in Kings Cross. It has pinpointed specific streets that have comparatively high or low levels of avoidance. In doing so, the maps have provided useful knowledge for policy, planning and practice and allowed specific management recommendations to be made based on the research findings.

Addressing Crime The NSW Police are the primary agency responsible for maintaining social order and preventing, detecting and investigating crime. Efforts made by the NSW Police to reduce crime and fear of crime in Kings Cross have been much assisted though the information gained from the fear-of-crime maps produced in this study. For example, avoidance was higher than expected around Sydney Place given the level of reported crime in the area. Further investigation alerted the police to unreported drug dealing in the area. The avoidance maps therefore assisted the police in identifying areas of disorder that they were previously unaware of and focusing intelligence and foot patrols on those areas. The combating of fear of crime through the identification and targeting of fear hotspots in this way can be worked into a variety of the policing models adopted by different police departments across the world. Of key relevance is the problemoriented policing, zero-tolerance, community-oriented and reassurance policing models. By doing so, police can not only target fear of crime in its own right, but also possibly predict future locations of crime and prevent crime. This is particularly important as police continue to move from traditional retrospective policing to proactive and preventative policing. Likewise, it encourages the recognition of community needs and response to disorder in addition to violations of criminal law.

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Local government also has a legislative role in responding to crime and community safety concerns. The City of Sydney therefore complements the work of the NSW Police in reducing and preventing crime and fear of crime: and works with community stakeholders to build and strengthen the community, prevent community harm and enhance quality of life (CoSC, 2006n). This work is outlined in the Safe City Strategy, which aims to improve actual and perceived safety across the City of Sydney and provides a framework to direct the work of the City of Sydney Council in addressing priority crime and safety issues (CoSC, 2006n). The City of Sydney Council has acknowledged that Kings Cross has high rates of crime and is carrying out community safety audits to identify issues in the physical environment that may impact negatively on public safety in the area. These safety audits are performed in partnership with the police and other stakeholders (CoSC, 2006n). They are used to identify areas for high-visibility patrolling with the City Rangers and NSW Police in key entertainment precincts during peak times for assaults (CoSC, 2008) The City of Sydney Council has also responded by providing residents with high quality, targeted information on how to respond to or prevent crime (CoSC, 2006j, 2006k, 2006l). This includes a series of safety fact sheets and a fridge magnet with key safety contacts, which are developed and disseminated in partnership with the NSW Police and the NSW Attorney General’s Department Crime Prevention Division. As part of this education campaign, the council is also focusing on building closer community networks, for example through a series of ‘Good Neighbour’ BBQs in conjunction with the NSW Police and NRMA Insurance. These ‘meet and greet’ occasions also provide residents with further information to reduce their risk of personal and property crime. These initiatives are not only designed to reduce crime, but also improve a sense of personal capability and improve perceptions of safety (CoSC, 2006n). The City of Sydney Council’s approach to urban design is based on CPTED principles with the aim of preventing crime and encouraging neighbourhood interaction in order to maximize casual surveillance so that people feel safer at all times of the day and night. To promote CPTED, ‘Safer By Design’ trainings are provided to city planners and other staff by the NSW Police. The council has also developed a series of CPTED guidelines and checklists that are used when planning its own developments and when considering private development applications. A key component of the guidelines is the notification of relevant development applications to the NSW Police. In this way police can provide comments on the development from a crimeprevention perspective prior to the application being considered by council (CoSC, 2006n). The City of Sydney Council is also continuing to provide a 24-hour monitored closed circuit television (CCTV) network, largely as a preventative measure and as a substitute for natural surveillance. It is hoped that the presence of CCTV cameras may deter criminal activity, as any potential offender may be fearful of being identified and prosecuted, and assist the police to apprehend and convict perpetrators of crime. CCTV cameras have been installed along Darlinghurst Road, Kings Cross (CoSC, 2008).

Integrating the Fear Mapping Results with Policy and Community Crime. . .

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The City of Sydney Council recognizes that crime and fear of crime can be reduced by engaging residents in community activities. As a result, the council is providing an extensive network of recreation and learning centres that host activities ranging from sports, arts, music, fitness, self-defence and street safety advice, employment programmes, sexual health education, nutrition courses, adult education, youth programmes and children’s services, and provide targeted recreation and diversionary programmes for children and young people. The council offers these latter programs so that children and young people in particular can engage in legitimate activities as a diversion from risk-taking behaviour (CoSC, 2006n). Recreation and learning is being promoted at a number of venues in the study site including Mary McDonald Centre (Corner of Bourke and Charles Streets), Juanita Nelsen Centre (Corner Nicholson & Dowling Streets), Sydney Place and Walla Mulla Park, which are all in Woolloomooloo. Furthermore, the City of Sydney Council is working with the community on a programme of cultural events which could include outdoor film screening, string quartets, jazz in the square, carols in the park and street theatre. A City East village website has also been set up to help people in the community connect with each other and to provide local information and links to services (CoSC, 2007). The City of Sydney Council is further partaking in advocacy for increased public transport services to Kings Cross, thereby providing the public with better services and encouraging intoxicated persons to leave the public spaces at the end of the evening. This includes more general bus and train services, additional nightrider (late night) bus services from the entertainment area and increasing after-hour taxi ranks (CoSC, 2007).

Targeting Pertinent Signs of Disorder and Incivility The research finds that environmental cues did trigger fear of crime and avoidance in Kings Cross. The results also indicated that different environmental cues have different signal values and triggered different levels of fear of crime, as suggested by the signal crimes perspective. Generally, this knowledge allows fear-reduction efforts to focus on those environmental cues that trigger the most fear of crime. This section briefly discusses each of the 16 environmental cues examined in this study.15 The environmental cues are discussed in order from the most to the least likely to trigger fear of crime according to the percent-based rank listed in Table 7.9. The different strategies used to address the different environmental cues are also discussed. 15 The environmental cues are not discussed with reference to findings from previous studies examining the link between environmental cues and fear of crime. This is because comparisons between studies are problematic due to vastly varying research methods. For instance it is unlikely the environmental cues would have the same effect on the formless levels of fear examined in most studies, as on the levels of avoidance examined in this research. Therefore comparing the results is somewhat ineffective.

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Top-Ranked Cues In Kings Cross, drug users, intoxicated persons and gangs triggered the highest levels of fear of crime. Consequently, they have also been the focus of many strategies aimed at reducing crime and fear of crime.

Reducing Levels of Intoxication It is not surprising that intoxicated persons16 triggered the most fear of crime, as Kings Cross has a distinct history of problems associated with these social groups. In fact, the City of Sydney Council readily acknowledges that a large proportion of crime, disorder and fear stems from alcohol intoxication and that areas within and surrounding Kings Cross have serious drug- and alcohol-related issues that compromise the ability of public spaces to be attractive places ‘to work, live and recreate’ (AJC, 2006; CoSC, 2006d, 2006e, 2006f). Since the time of interviewing and release of the research findings, the City of Sydney Council has developed specific policies and plans in order to deal with drugand alcohol-related issues, for instance the ‘Drug and Alcohol Strategy 2006–2011’ and the draft liquor bill 2005 (CoSC, 2006g, 2005c). First, the council has implemented the City of Sydney Street Drinking Strategy to respond to the specific issues generated and faced by chronic alcohol-addicted groups and individuals. This strategy incorporates various components. Through this strategy, the City of Sydney Council is redesigning public space to reduce harmful alcohol consumption via good landscaping, lighting and change of use. This includes providing public toilets in entertainment precincts and in areas which have a high level of public drinking activity (CoSC, 2008). The City of Sydney Council has also developed the City’s Street Drinking Strategy 2006–2011. Key hotspots for street drinking have been identified in Woolloomooloo (Talbot Lane, Bourke Street Park, Tom/Uren Square and Walla Mulla Park) for the improvement of health and community services responses to street drinkers and reduce the negative social impact of street drinking (CoSC, 2007). The City of Sydney Council is continuing to prohibit alcohol consumption in high-conflict public spaces (spaces with high pedestrian volumes or adjacent residential uses) via the establishment of Alcohol Free Zones (AFZs) on roads and footways and Alcohol Prohibited Areas in public parks. The council has identified additional locations for Alcohol Free Zones and has extended the time period in which those AFZs are effective (CoSC, 2006c). Alcohol Free Zones in the study site include

16 Intoxicated persons triggered fear of crime in 54% (day) and 55% (night) of the avoiding respondents.

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Effective until December 2014 • • • • • • • • • • •

Macleay Street from Darlinghurst Road to Wylde Street Victoria Street from Darlinghurst Road to McDonald Lane Brougham Street from William Street to Brougham Lane Brougham Street from Brougham Lane to Rae Place Brougham Street from Rae Place to Cowper Wharf Road Brougham Lane from Victoria Street to Brougham Street Dowling Street from northern side of railway viaduct to Sydney Place Francis Street from Riley Street to College Street Liverpool Street from Yurong Street to College Street Kings Lane from Riley Street to Crown Street Little Burton Street Effective until November 2011

• • • • • • • • • • • • • • • • • • • • • • • • • •

Darlinghurst Road from Bayswater Road to Liverpool Street Victoria Street from Bayswater Road to Burton Street Barncleuth Lane from Roslyn Street to Barncleuth Square Barncleuth Square from Darlinghurst Road to Ward Avenue Bayswater Road from Victoria Street to Kings Cross Road Darlinghurst Road from Victoria Street to Ward Avenue (includes road closure adjacent to Fitzroy Gardens) Goderich Lane from Pennys Lane to Ward Avenue Kellett Place cul-de-sac Kellett Street from Bayswater Road to Ward Avenue Kellett Way from Kellett Street to Roslyn Street Kings Cross Road from Victoria Street to Roslyn Street Mansion Lane cul-de-sac Pennys Lane from Kings Cross Road to Bayswater Road Roslyn Street from Darlinghurst Road to Kings Cross Road Ward Avenue from Barncleuth Square to Kings Cross Road Earl Place from Earl Street to Springfield Avenue Earl Street from Victoria Street to Orwell Street Hughes Lane from Orwell Street to Hughes Street Hughes Place cul-de-sac Hughes Street from Victoria Street to Macleay Street Llankelly Place from Darlinghurst Road to Orwell Street Orwell Lane from Orwell Street to Hughes Street Orwell Street from Macleay Street to Victoria Street Springfield Avenue from Darlinghurst Road (Springfield Plaza) to Springfield Mall Springfield Mall from Llankelly Place to Earl Street All streets and laneways bounded by Cathedral Street, Forbes Street, Palmer Street and William Street including Bourke Street, Burrahpore Lane, Corfu Street, Cross Lane, Egan Place, St Kilda Lane, Talbot Place and William Lane

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Dowling Street from Pring Street to Cowper Wharf Road Forbes Street from Cowper Wharf Road to William Street Nicholson Street from Dowling Street to Bourke Street Sydney Place bounded by Stephen Street and the Hills Stairs, McElhone Street Cathedral Street from Forbes to Bourke Street

The City of Sydney Council is also taking a comprehensive approach to combating alcohol-related harm, antisocial behaviour and crime on and immediately around licensed premises (CoSC, 2006a). For example, the council has partnered in an Accord with Licensed Premises, which aims to ‘reduce alcohol related crime and antisocial behavior in and around licensed premises and to improve the perception of safety’ (CoSC, 2006b). Council support to Liquor Accords includes contributing to the development of the NSW Government patron education campaign, about appropriate behaviour and disseminating the materials across licensed premises (CoSC, 2008). Licensed premises with high levels of assaults and other issues are targeted in joint operations with NSW Police and the Office of Liquor Gaming and Racing (CoSC, 2010a). In an attempt to further curb alcohol-fuelled violence on and around licensed premises, the City of Sydney Council further introduced a 12-month freeze on liquor licenses in Kings Cross in June 2009. This freeze meant that no new premises could obtain a liquor license and that existing license holders could not change their premises boundaries, increase their venue capacity or alter hours of operation (CoSC, 2010a). Minimizing Harm from Drug Use and Dealing It is not surprising that drug users17 and intoxicated persons18 triggered the most fear of crime, as Kings Cross has a distinct history of problems associated with these social groups. For instance, the East Sydney Neighbourhood Association (ESNA) identifies one of the main problems for pedestrians as being ‘personal risk from drug dealers and associated criminal elements, drugged street sex workers and their minders’ (ESNA, 2002). While the supply of illicit drugs is a crime, and ultimately the responsibility of the NSW Police, the City of Sydney Council is aiming to reduce this crime by making it a requirement of premises with a nightclub license to include a Harm Minimization Plan, which will reduce the likelihood of drug dealing occurring (COSC, 2006j). The council has also activated public space for legitimate uses, such as entertainment and outdoor footway dining to discourage drug dealing and continues to provide a responsive safety camera CCTV network, enabling the NSW Police to apprehend drug dealers (CoSC, 2008).

17

Drug users triggered fear of crime in 64% (day) and 64% (night) of the avoiding respondents. Intoxicated persons triggered fear of crime in 54% (day) and 55% (night) of the avoiding respondents. 18

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Understanding Public Perceptions of Gangs The results strongly indicate that the perceived presence of gangs19 in Kings Cross also triggered large numbers of people to feel afraid of being robbed, beaten or attacked and adopt avoidance behaviour. This avoidance reaction was somewhat unanticipated because neither The City nor the police appear to have publicly recognized gang activity as a current problem in the Kings Cross area. While The City and police do accept gangs as an environmental cue signifying the threat of criminal victimization, they chiefly act to deal with gang-related problems in other parts of Sydney. In contrast, a review of Australian newspaper articles printed in the three years following the interviewing does show that information regarding gang-related crime in Kings Cross is occasionally published.20 The research results suggest that in order to reduce public fear of crime, action is needed to address gangs in Kings Cross. Nevertheless, as gangs are not considered part of the current Kings Cross environment, a deeper look at public perception of gangs may be necessary. Middle-Ranked Environmental Cues Revitalization of Laneways and Streets Laneways21 were top of the middle-ranked environmental cues that triggered fear of crime in the respondents.22 Prior to this study the City of Sydney Council did focus on the management of laneways in development plans and policies, for example in the ‘City of Sydney Policy for the Management of Laneways in Central Sydney’23 (CoSC, 1999). Only one of the laneways located in the Kings Cross study site, McElhone Stairs, is discussed in this policy. References to the management of McElhone Stairs were limited to stating that the steps were to be retained with enhanced pedestrian activity. However since the time of this study, the City of Sydney Council has realized that laneways can be the location of criminal and antisocial behaviour and evoke fear of crime. In response to this, the council has begun a Laneways Revitalization Strategy to reclaim Sydney’s laneways so that they become safe, lively and more pedestrian friendly (CoSC, 2006n). Laneways will be reactivated through art and cultural exhibitions, and small businesses such as cafes and bars, specialist retail, fashion or galleries (CoSC, 2009). In addition, physical improvements will be made to selected lanes. Works may include improved lighting, better paving, removal of

19

Gangs triggered fear of crime in 57% (day) and 56% (night) of the avoiding respondents. See Braithwaite (2007; Braithwaite and Baker, 2007; Cummings, 2007). 21 Laneways triggered fear of crime in 45% (day) and 50% (night) of the avoiding respondents. 22 According to Darcy’s (2005) study in 2003, laneways were ranked as the equal least common reason for making the public feel unsafe. Dark laneways were ranked as the equal fourth most common reason for making the public feel unsafe. 23 Generally, this policy states that laneways will be improved with appropriate lighting (CoS, 1999). This plan may help reduce the extent to which laneways trigger public fear of crime. 20

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high planter walls to improve visibility, improved landscaping, planting new trees, the provision of additional space for outdoor dining, and the installation of trafficcalming measures to improve pedestrian amenity and respond to business activation (CoSC, 2010b). Several streets and laneways within the study site have been targeted for revitalization. These include Sydney Place, Forbes Street, Talbot Lane, Bourke Street, Harmer Street and Cathedral Street, which are all located in the fear hotspot A over Woolloomooloo (CoSC, 2010c). It is hoped that improvements to these streets, and others in Woolloomooloo, will increase pedestrian traffic through Woolloomooloo by residents of the area and visitors travelling to Kings Cross, Potts Point, East Sydney and Darlinghurst. Increased wayfinding signage will also facilitate this (CoSC, 2010c). Earl Street and Earl Place, making up fear hotspot B, have also been targeted for a complete upgrade (CoSC, 2011a). Safe Collection of Rubbish, Syringes and Injecting Equipment Rubbish/syringes24 ranked quite high as an environmental cue that triggered the respondents to feel afraid of crime.25 The City of Sydney Council has recognized that a poorly maintained environment can impact negatively on people’s perceptions of safety and security. The council has a street cleaning service that ensures the flushing and cleaning of footpaths, malls and plazas and the emptying and steamcleaning of street furniture, litter and recycling bins and cigarette ash cylinders on a regular basis (CoSC, 2006n). In addition, the council is educating Department of Housing residents on waste management and the upkeep of their residential premises (CoSC, 2010c). Similarly, the City of Sydney Council has acknowledged that the presence of discarded syringes and other drug-injecting equipment poses a serious risk to public health and safety and has developed a Syringe Management Plan 2005–2010. This document outlines key strategies designed to reduce the number of syringes inappropriately discarded by drug users in public spaces (CoSC, 2005b, 2006g).26 The plan outlines how the council provides a fast, coordinated and efficient response to public injecting and the associated waste; installs community sharps bins in response to injecting hotspots; and educates residents and businesses on what to do if they find a discarded syringe, via the ‘Who To Call’ card which promotes the free call 24-hour Needle Clean Up Hotline. With regards to the sharps bins, 62 community sharps bins have been placed throughout the Sydney LGA, many of which are located in the Kings Cross study site (CoSC, 2006g). 24 Rubbish/syringes triggered fear of crime in 44% (day) and 48% (night) of the avoiding respondents. 25 According to Darcy’s (2005) study in 2003, lack of cleanliness was ranked as the equal second least common reason for making the public feel unsafe. 26 This plan also demonstrates The City’s ‘commitment to public health, harm reduction and the improvement of safety and cleanliness of the public domain for the entire community – residents, visitors and workers alike’ (CoSC, 2006g).

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Removal of Areas to Hide Areas to hide27 are placed towards the top of the middle group of environmental cues that trigger people’s fear of being robbed, beaten or attacked. A review of council plans and policies found little reference to addressing areas within public spaces that can be used as hiding or entrapment places. One recommendation was found in the ‘South Sydney Plan 1997’, in which The City refers to Green Square Town Centre and states that external lighting needs to make potential hiding spots visible. The research results suggest that the council also needs to consider how areas to hide trigger fear of crime in Kings Cross.

Moving Loitering People The presence of loitering people28 is placed in the middle group of environmental cues that trigger people to feel afraid of crime.29 NSW has the power to move loitering people on and regularly does so in the Kings Cross Area (Snowball et al., 2008). The City of Sydney Council also identifies loitering people as a problem in Kings Cross and targets them through various policies. For example, because loitering people can cause ‘disturbance’, the council has disallowed people loitering outside sex industry premises through the ‘City of South Sydney Sex Industry Policy 2000’. The presence of loitering people is also restricted through planning, as the council requires that loitering people be addressed by businesses in development applications. For instance, the council stipulates that the licensee and staff of a proposed licensed hotel in Potts Point ‘take all reasonable steps to ensure that there is no loitering by persons seeking admittance to the premises’ (CoSC, 2005f).30 Other approaches focusing on loitering, for example that occurring in Springfield Plaza, involves the occurrence of concerts as ‘part of an overall program to activate the space in a very positive way’ (CoSC, 2006e). The results suggest these actions may also be sensible for addressing fear of crime.

27

Areas to hide triggered fear of crime in 39% (day) and 43% (night) of the avoiding respondents. Loitering people triggered fear of crime in 43% (day) and 46% (night) of the avoiding respondents. 29 According to Darcy’s (2005) study in 2003, loitering people were ranked as the equal least common reason for making the public feel unsafe. 30 Similarly, The City also demanded that another application be approved for Kings Cross if the management or licensee of the premises is responsible for ensuring loitering patrons do not detrimentally affect the amenity of the neighbourhood (CoSC, 2006e). The management was also held responsible for ‘the control of noise, loitering and litter generated by patrons of the premises and shall ensure that people leave the premises and area in an orderly manner’ (CoSC, 2006e). 28

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Ensuring Accessibility and Minimizing Blocked Escape Blocked escape31 was also placed in the middle group of environmental cues that triggered people to feel afraid of crime in Kings Cross. In the ‘South Sydney Plan 1997’, The City recommends that building designs, particularly entry points, minimize the presence of entrapment spots (another term for blocked escape). In reference to one city area, Green Square Town Centre, The City also states that entrances to public open space should encourage pedestrian use and also provide visual security through the establishment of clear sight lines (CoSS, 1997). The public domain is to be designed to ensure there are no dead ends or similar blocked escape routes. With regard to fear of crime, the research results indicate blocked escape can adequately be addressed through such plans. Lower Ranked Environmental Cues Better Street Lighting During the night,32 poor street lighting ranked towards the top of the middle-ranked environmental cues that triggered fear of crime in the respondents, and towards the bottom of this category during the day33 . The City of Sydney Council34 recognizes that good street lighting is a measure which has the effect of enhancing feelings of safety not only for its visual effect of improving overall surveillance but also as a means of attracting people to a site. Street lighting in its various forms is therefore a central consideration of the council and is being addressed through ‘The City of Sydney Exterior Lighting Strategy’ (CoSC, 2000). The first objective of this strategy is ‘to improve the illumination of the City of Sydney at night to ensure public safety, public enjoyment, architectural appreciation, and night-time entertainment’. Additional aims are to illuminate public and pedestrian areas ‘to a standard that provides a safe and comfortable visual environment’, and ‘to a level that will reduce the risk of crime to people and property’ (CoSC, 2000). As part of City Lights Strategy, the council has outlined target minimum levels for public domain lighting throughout the city and standardized lighting infrastructure. The council is further targeting the upgrade of street lighting in identified hot-spot locations, laneways and Department of Housing streets (CoSC, 2006n, 2010c).

31

Blocked escape triggered fear of crime in 35% (day) and 36% (night) of the avoiding respondents. 32 Poor street lighting triggered fear of crime in 36% (day) and 52% (night) of the avoiding respondents. 33 The fact that poor street lighting also triggered a fear-of-crime response during the day could suggest this issue is a very salient issue in the minds of the respondents, that some streets like small laneways need street lighting during the day, or that there were problems with the survey interviewing procedure. 34 According to Darcy’s (2005) study in 2003, lighting was ranked as the equal second least common reason for making the public feel unsafe.

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Activation of Public Spaces for Pedestrians Pedestrian absence35 was at the bottom of the middle-ranked environmental cues that triggered people to feel afraid of crime. An assessment of many City of Sydney plans and policies indicates that most of The City objectives are either directly or indirectly aimed at encouraging pedestrian use of and activity in public spaces. Similar to the Laneways Revitalisation Strategy, the City of Sydney Council has been focusing on public parks and spaces for activation through buskers, outdoor events, markets and organized sporting competitions. These activities are designed to increase the legitimate use of open spaces and reduce opportunities for criminal activity (CoSC, 2006n). Several parks in the study site have also been identified for activation and improvements. For example, the City of Sydney Council is renewing Walla Mulla and Bourke Street Parks as part of the Woolloomooloo Improvements Plan to ensure that they are safe, attractive and accessible. Key improvements include the building of green walls, upgrade of toilet facilities, removing and lowering retaining walls to improve visibility and emergency vehicle access, installing new lights to make the park safer, thinning thick vegetation to increase visibility, installing new furniture and better paving and reducing clutter in the park to increase open space. Bourke Street Park will also feature the creation of a secure space for a future community garden and exercise equipment (CoSC, 2011c). Upgrade to those parks which are located under the railway viaduct has also been identified by the City of Sydney Council as a priority. These include Sir John Young, Tom Uren Square, Forbes Street Park and Talbot Lane. These areas will also feature options for innovative lighting solutions and public art programmes (CoSC, 2007). Fitzroy Park and Lawrence Hargrave Reserve in Kings Cross proper are also being revitalized. Improvements to these parks include making a space for markets within the park, providing a better play space among the gardens, improving public toilets and making the landscape more attractive. Pedestrian access to Lawrence Hargrave Reserve is also being improved and a community garden area and fruit tree orchard installed (CoSC, 2011b, 2011c). Maintenance of Rundown and Abandoned Buildings Of the lower-ranked environmental cues, rundown/abandoned buildings36 triggered the most fear of crime in the respondents. The City realizes that vacant building sites and buildings have a negative effect on ‘the quality of the public domain, and on businesses and residents surrounding these sites’ (CoSC, 2001). In the ‘Central

35 Pedestrian absence triggered fear of crime in 33% (day) and 37% (night) of the avoiding respondents. 36 Rundown/abandoned buildings triggered fear of crime in 25% (day) and 32% (night) of the avoiding respondents.

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Sydney Development Control Plan 1996’37 The City aims to improve the appearance of such sites and, where practicable, ensure that ongoing temporary active uses or landscaping at the street frontage is provided.38 A review of more recent council plans and policies developed after the study site has not shown additional reference to the maintenance of rundown or abandoned buildings. The results from this research therefore indicate that The City could do more in ensuring these objectives are met, thereby reducing fear of crime.

Supporting Homeless People The presence of homeless people39 was ranked in the lower group of environmental cues that trigger people to feel afraid of crime.40 The City of Sydney Council is implementing various initiatives to support homeless persons in the area. Three major services for homeless people include the Homeless Persons Information Centre, the Homelessness Brokerage Program and the City Street Outreach Service (CoSC, 2006e). The council has the only dedicated homelessness unit in Australia and has developed a Homelessness Strategy, which features components that foster affordable housing, increase accommodation and support options for people who are homeless and advocate for improved mental health, drug and alcohol and other essential services in the area. The council is working with key stakeholders to provide this assistance, including the St. Vincent de Paul Society and the NSW Department of Housing. In particular, the council is working with the Woolloomooloo Homelessness Coordination group which involves local homeless services, police, mental health and drug and alcohol services. Similarly, the council is beginning initiatives to engage homeless people to gain skills via working on local projects for example, redeveloping park or garden areas, providing cleaning services or contributing to other community-based activities (CoSC, 2007). These social services are not directed at reducing fear of crime, but rather at assisting homeless people to find accommodation and live independently. However, it is possible that a reduction in fear of crime is an unexpected benefit, as suggested in Chapter 4.

37

Consolidated in 2001. The plan states ‘it is important that construction sites and vacant sites present an attractive appearance to the streets and public areas in order to enhance the amenity of Central Sydney’ (CoSC, 2001). 39 Homeless people triggered fear of crime in 33% (day) and 31% (night) of the avoiding respondents. 40 As mentioned earlier drug users/homeless were ranked as the most common reason for making the public feel unsafe in Darcy’s (2005) study in 2003. The results from this study suggest that the drug users/homeless category in Darcy’s study was ranked high because of the existence of drug users within it. 38

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Removing Vandalism and Graffiti Vandalism41 also ranked in the lower group of environmental cues that trigger people’s fear of crime. The City of Sydney Council recognizes that public spaces and streets that are clean and in good condition increase the public’s confidence in the safety of the city. Under their ‘Strategic Plan 2006–2009’ the council therefore ascertains that public assets are to be maintained as ‘clean, accessible, safe, aesthetic, fit for purpose, and meet community needs’ (CoSC, 2006i). The City states that, ‘high levels of community ownership of public domains, parks and facilities reduce the incidence of vandalism and through timely reporting assists in proactive maintenance of City assets’ (CoSC, 2006i). In this document, little more is said on how the City plans to achieve this goal. However other policies, such as the Aerosol Art and Graffiti Resolution (CoSC, 2003) and Graffiti Management Policy (CoSC, 2004) target specific types of vandalism (graffiti) and are more thorough in terms of outlining strategies. These policies state that in addition to regular street cleaning, waste collection and litter control, the council also monitors for signs of vandalism and graffiti. For example, graffiti ‘hotspots’ are inspected every 24 hours and graffiti removed within 24 hours of identification, or when consent from the building owner or resident is obtained. The remainder of the LGA is inspected every five days and graffiti removed within 24 hours of identification (CoSC, 2004, 2006n). Offensive/Degraded Shops The presence of offensive/degraded shops42 was not a key environmental cue triggering people’s fear of crime. Offensive/degraded shops can take a variety of forms. First, the City of Sydney Council recognizes that the public can consider music and crowd noise from shops, particularly licensed premises, as offensive or a nuisance. With this in mind, The City puts in place noise restrictions and actively supports the investigation of noise complaints by other parties like the Department of Gaming and Racing Legal and Licensing Section (CoSC, 2006h). Second, The City recognizes that shops advertising or displaying products associated with sexual behaviour can also be offensive. The ‘City of South Sydney Sex Industry Policy 2000’ refers to Section 578E of the Crimes Act 1900, in restricting the terms of selling or disposing of these products (CoSS, 2000).43 Similarly, under the objectives of the Central Sydney Local Environment Plan 1996,44 the impact of premises which degrade the 41

Vandalism triggered fear of crime in 25% (day) and 29% (night) of the avoiding respondents. Offensive/degraded shops triggered fear of crime in 21% (day) and 25% (night) of the avoiding respondents. 43 The act documents that ‘any person who carries on, or who is engaged in, the business of selling or disposing of products to which this section applies must not: Advertise, or cause another person to advertise, in any manner the nature of that business, or exhibit or display any such products: (i) to a person who has not consented to or requested the exhibition or display, or (ii) in a manner so that they can be seen from outside the premises of the business by members of the public’ (CoSS, 2000). 44 Consolidated in 2005. 42

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amenity of Central Sydney, such as brothels, restricted premises and late opening pubs, will be minimized. This involves the assurance that these premises ‘are not concentrated together, and that their cumulative impact is assessed’ (CoSC, 2005a). The results of this research do not indicate that any additional strategies focusing on fear of crime in relation to offensive/degraded shops need to be implemented. Spruikers The presence of spruikers45 was the second lowest-ranked environmental cue triggering people’s fear of crime in Kings Cross.46 The City is officially in opposition to spruikers. Sex-industry related premises, strip clubs and other properties require development consent from The City to undertake spruiking activities (CoSC, 2006a). Approval may be subject to the applicant’s devising and complying with a stringent Spruiker Management Plan (CoSC, 2005g). For example under their Spruiker Management Plan, Playbirds International directs spruikers not to use swearing or offensive language, spruik at a volume that other pedestrians are disturbed, act as a physical barrier to pedestrians, touch potential customers, approach disinterested persons and more (CoSC, 2005g). The results of this research indicate in terms of fear of crime the City of Sydney Council’s attention to the behaviour of spruikers is sufficient. Sex Workers The presence of sex workers47 was the lowest-ranked environmental cue triggering the respondents’ fear of being robbed, beaten or attacked.48 The City’s Adult Entertainment and Sex Industry Premises Development Control Plan 2006 controls the development and operation of sex industry premises (CoSC, 2006h). The plan is designed to minimize any negative impact arising from these premises (CoSC, 2006h). The results from this study indicate the city is doing a good job in managing the presence of sex workers in terms of their impact on peoples’ fear of crime. Conversely, ESNA argues that illegal brothels, street prostitution and curb crawling are a significant problem in the area (ESNA, 2002).

45

Spruikers triggered fear of crime in 22% (day) and 25% (night) of the avoiding respondents. As mentioned earlier spruikers/intoxicated persons were ranked as the third most common reason for making the public feel unsafe in Darcy’s (2005) study in 2003. The results from this study suggest that the spruikers/intoxicated persons category in Darcy’s study was ranked high because of the existence of intoxicated persons within it. 47 Sex workers triggered fear of crime in 18% (day) and 20% (night) of the avoiding respondents. 48 According to Darcy’s (2005) study in 2003, sex workers were ranked as the second most common reason for making the public feel unsafe. The contrasting result from this study is noteworthy, however not necessarily surprising given the different research approaches taken in the two studies. 46

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Sensitively Addressing the Environmental Cues Despite arguing that the highly ranked environmental cues should be targeted when designing and implementing fear-reduction strategies, it is recognized that there is a need for sensitivity. There are two ends of a continuum that focus on the targeting of social incivilities as a form of social control. One end is dominated by the thoughts of ‘communitarians’ or ‘universalists’ who propose the rigorous maintenance of social control and the need for all fear-inducing environmental cues to be eradicated from affected communities, for the common good (Kelling and Coles, 1997). At the other end, the ‘rights’ activists argue that people involved in so-called disorderly behaviour are being made scapegoats and inappropriately marginalized through social control (at the expense of their fundamental liberties and rights to express themselves) (Kelling and Coles, 1997; Pain, 2001). The ‘rights’ activists argue that social control and the maintenance of public order can involve the subordination of these groups to the norm, their retribution and/or their estrangement from the community (Bauman, 2000; Hubbard, 2003; Kelling and Coles, 1997). Using the example of homeless people, Kelling and Coles (1997) agree that this ignores the fact that most homeless are decent, responsible and lawabiding people, with individual emotional, psychological and physical needs that need to be considered (Gold and Revill, 2003; Kelling and Coles, 1997; Phillips and Smith, 2003). While not prejudging what is or is not ‘incivil’, the results from this study have empirically identified drug users and gangs as definite signs of disorder. However, this research does not recommend police and other authorities indiscriminately target these social groups in an insensitive manner. In the same token, while stating a large number of people were fearful and avoided Kings Cross because of these environmental cues, other people may be drawn to the area for the sense of excitement that encountering these environmental cues may bring.

Targeted Intervention The avoidance maps illustrated that fear of crime triggered by different environmental cues is expressed through different patterns of avoidance. For instance, comparing the avoidance maps for sex workers and drug users revealed the very different levels and patterns of avoidance. This indicates that the situational context of environmental cues also plays a role in whether they trigger fear of crime and how that fear manifests through avoidance. Accordingly, it is also noteworthy that the ‘significant’ environmental cues are dense along Darlinghurst Road, where collective avoidance is relatively low. It may be that the aforementioned cues may only trigger fear when they are present in laneways, and not major thoroughfares. Despite the proposed higher incidence of social environmental cues along Darlinghurst Road during the night, fear is much lower than in the surrounding areas. Street lighting is adequate along this main street and may diminish fear triggered by the other cues (poor street lighting was ranked fourth during the night; see Table 7.2). Additionally, Darlinghurst Road into MacLeay Street is the major thoroughfare north of William Street. Thus, while the

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environmental cues may be present and trigger fear, this may not be enough to deter people from using the main street. The environmental cues avoidance maps can be used to compare fear of crime with actual presence of the environmental cues. If there is a spatial match between perceptions and reality, the avoidance maps can enable fear-reduction strategies to target the pertinent environmental cues in the most appropriate areas. This could increase the chances of successfully combating fear of crime while minimizing resource expenditure. As mentioned above, in instances of a spatial mismatch, the avoidance maps could potentially alert the government and police to areas of disorder that they were previously unaware of. Similarly, the finding that avoidance did not always increase around council syringe bins might indicate drug users are not using some bins and that the bins could be relocated to more suitable locations. In other instances, differences between the perceptions and reality of environmental cues may provide information about the content, effect and signal value of those signs of disorder and incivility. The gangs avoidance maps indicated that avoidance is very high and generalized throughout the study site despite the fact that gangs are not considered to be currently operating in the area. This is useful information as it clarifies the need for attempting to combat fear of crime by altering public perceptions of disorder. Providing the public with information that gang-related crime in King Cross is rare and unlikely to do this. The results further provide new information that different socio-demographic groups experience different levels of fear of crime and, more importantly, adopt different patterns of avoidance. Realizing social groups react differently to fear of crime means fear-reduction strategies can target more fearful groups, like visitors rather than residents. For example, investing in advertising that encourages visitors to Kings Cross and improves the reputation of the area may be more successful in decreasing local fear of crime than crime reports distributed in Kings Cross community meetings. Similarly, the fear mapping results for the male and female respondents provided new information that fear of crime is not solely a women’s problem, does affect a large proportion of men and that both men and women adopt similar patterns of avoidance. This is interesting for the field of research, as traditional analyses of fear of crime using global measures indicate that fear of crime is low among male populations.

A Snapshot from the Future The following photos of the areas within the study site were taken in 2007, two years after the time of interviewing (Figs. 7.30, 7.31, 7.32, 7.33, 7.34, 7.35).

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Fig. 7.30 Sydney place, Woolloomooloo. Featuring public amenities including the tennis courts, play ground, community garden, graffiti art and laneways

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Fig. 7.31 Council signs to control disorder around Sydney place, Woolloomooloo

Fig. 7.32 Public and private CCTV around Sydney place, Woolloomooloo

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Fig. 7.33 William Street, looking east

Fig. 7.34 A laneway in Woolloomooloo

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Fig. 7.35 The railway viaduct in Woolloomooloo

Assessments of Techniques and Approaches Developed in the Kings Cross Study The Survey and Interviewing Procedure By employing a largely quantitative survey, the data gained in this study could be easily and quickly acquired and analysed. This was beneficial to the study and the development of an avoidance mapping technique. However, it also meant that many of the advantages of qualitative research could not be harnessed. For example if the

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survey was qualitative, more information could be gained on the respondents’ experiences of fear of crime, their mechanisms to cope with it, their patterns of avoidance and responses to those environmental cues that trigger their fear. This would be very valuable information. Therefore the presence of open-ended questions in a future survey is recommended. While the street-based survey approach was rigorous and necessary to ensure the safety of the interviewers, it had one limitation. By interviewing respondents in the public arena, the interviewers were unable to include the most fearful people in the research sample. Such people were those too afraid to leave their residences, referred to in the literature as ‘prisoners of their own homes’ (Joseph, 1997; Stephens, 1999). This raises the possibility that fear-of-crime surveys carried out in high-crime areas may be harder to manage than other residential surveys. Postal questionnaires are thus recommended to elicit a sample truly representative of those members of the public who are really afraid and confined to their homes. However, as intended in the research approach chapter, this survey was not intended to be representative of the regional demographic. A similar limitation of the survey relates to the chosen sampling region and style. Sampling was primarily restricted to the main roads located within the study site. While this was suitable for the purposes of this study, which was to develop an informative technique for visualizing and mapping spatial fear-of-crime data, it has some drawbacks that are mentioned. First, had sampling been conducted outside of the study site boundaries or in the smaller roads within the study site, the results may have been different. By carrying out most of the sampling within the study site or along main roads in the region, the sample could have been biased towards those people who are not afraid of crime and do not avoid the area, particularly the main streets. For example, the sampling would not include those people who were actually avoiding the entire Kings Cross area because they were afraid of crime. In contrast, if surveying were to occur over a more expansive street setting it is likely that reported avoidance levels would have increased all over the study site, particularly in the main streets where interviewing primarily took place. Had a postal survey been conducted this may have been pronounced, providing the methods for completing the mapping section of the questionnaire were clearly presented. Cognitive Mapping Any potential limitations with the resulting avoidance maps, in terms of their accuracy, largely rest in the fact that avoidance mapping is dependent upon cognitive mapping. Due to the subjective nature of spatial cognition, one’s cognitive map can be regarded as incomplete, distorted, schematized, augmented and overly simplified (Downs and Stea, 1973; Nasar, 1998). Consequently, cognitive maps are not representative of reality. Nor are the individual avoidance maps the respondents drew necessarily representative of their own cognitive maps. Therefore, the resulting avoidance maps cannot be regarded with absolute authority when it comes to policy and planning, but merely an informative guide to be used in triangulation with other information sources.

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People have a tendency to overestimate distances, particularly the spatial extent of familiar and conceptually important areas (Day, 1976). An area worthy of being avoided because one fears being robbed, beaten or attacked could be considered as a conceptually important area. Specifically, Block (2000) states that people usually overestimate short distances and underestimate long distances. This may have occurred when the respondents illustrated the areas that they avoided. It was observed that many survey respondents roughly illustrated the areas that they avoided, rather than going into specific and careful detail. While these possibilities do not discount the spatial maps produced, they can act to encourage more emphasis on the analysis to be placed on the central regions of those avoided areas, rather than on their peripheries. One approach to test validity of the resulting avoidance maps would be to present the respondents with different looking maps of the same area or with maps that have a different scale, and compare the results. However, with the presence of such obvious fear hotspots, safe thoroughfares and cognitive barriers between safe and unsafe areas, which clearly represent consistent public behaviour, it is likely this is not necessary.

General Summary of the Kings Cross Study The Kings Cross study continued to develop a technique for avoidance mapping to provide a spatiotemporal investigation into people’s fear of being robbed, beaten or attacked. It explored the environmental cues that trigger people’s fear and their consequent avoidance reaction. By doing this, the research tests the hypothesis that the spatial visualization of avoidance can provide new information concerning public fear of crime. The 2D maps confirmed that all of the environmental cues triggered fear of crime and that avoidance levels were consistently higher during the night than the day. They also illustrated that each of the environmental cues triggered different levels of avoidance. The perceived presence of drug users, intoxicated persons and gangs triggered the highest levels of avoidance. The perceived presence of sex workers triggered the lowest levels of avoidance. The avoidance maps further revealed that the environmental cues triggered distinct patterns of avoidance, showing obvious fear hotspots, as well as streets perceived to be safe thoroughfares through those fear hotspots. Likewise, many of the avoidance maps displayed streets that act as cognitive barriers separating seemingly safe and unsafe areas. This information provides some new spatially sensitive insight into how people react to fear of crime through avoidance. It additionally provides an evidence base that can be used by police and governments when allocating resources to specific environmental cues in those critical fear hotspots. The 2D avoidance maps for four environmental cues were selected for further exploration using three-dimensional (3D) mapping. These were drug users, sex workers, areas to hide and gangs. The 3D avoidance maps exposed microscale differences in patterns of avoidance between these environmental cues. An

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exploration of the avoidance reaction adopted by different socio-demographic groups in response to drug users and sex workers was additionally carried out. Separate maps were produced for men and women, and residents of, and visitors to, Kings Cross.

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Chapter 8

Future Avenues for Fear Mapping: Potential Applications and Improvements

Has Collective Avoidance Behaviour Changed in Wollongong and Kings Cross? While the Wollongong and Kings Cross studies were conducted over an eight-year period, the research was cross-sectional in nature. In both cases, surveys were conducted over a period of months and, as a result, the findings are relatively specific to the time and context of the two projects. Further, many of the interpretations and recommendations, such as those integrated into the crime prevention plan for Wollongong (WCC, 2007), were based on the spatiotemporal implications of behavioural responses to fear. One logical extension of this body of research would be to explore temporal patterns of collective avoidance behaviour in more depth. At the simplest level, this could involve rerunning fear mapping surveys in the study areas several years after the initial research. As outlined in the concluding sections of Chapter 6, the Wollongong CBD area underwent substantial landscape design changes as part of a broader city centre revitalization strategy (WCC, 2005). This process has resulted in significant changes to the character and physical layout of many areas within the CBD. For example, run-down buildings have been replaced in McCabe Park, including those shown in Figure 6.9. The council has also removed many structures that were previously obscuring sight lines and reducing the potential for natural surveillance (see Figs. 8.1 and 8.2 below for a comparison between 2003 and 2010). Other changes include the approval of high-density apartment complexes that are a fusion of commercial and residential interests. Figure 8.3 provides an example of one of these new buildings which has restaurants and cafes in the lower section and with several floors of apartments above that overlook Crown Street. Such buildings were designed with an aim to generate a more balanced mix of residential and commercial land use in the CBD area (Irwin et al., 2003) and have undoubtedly changed the character of the city core. A further activity which may have influenced collective avoidance behaviour, and the ‘five o’clock flight’, are the new ‘Market Day Fridays’ in Crown Street Mall. The idea for a produce market arose from the workshops held during the early stages of the revitalization strategy and the recognition of the ‘mall problem’ among a range of stakeholders (WCC, 2007). The markets are held weekly between January and July each year and offer B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7_8,  C Springer Science+Business Media, LLC 2012

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Fig. 8.1 A public walkway in McCabe Park in 2003

Fig. 8.2 The same walkway in 2010 after extensive modifications as part of the City Centre Revitalization Strategy

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Fig. 8.3 One of many mixed residential apartment-commercial buildings that have been constructed in the Wollongong CBD since 2003

‘. . .a selection of handmade jewellery, wooden albums, photography, leatherware, handmade cards, soaps, toys, handmade cakes, biscuits, honey and farm fresh fruit and veggies. There is also a selection of hot food stalls including traditional Turkish cuisine, Asian inspired curries, Korean style food and Hungarian donuts’ (WCC, 2011). Anecdotally, it would appear that the mall area now has a very different atmosphere on Fridays compared to the time of the Wollongong study when it was a focus for social disorder leading into the weekend. In general, it would appear that the design and character of the CBD has improved significantly over the intervening years – but has there been a corresponding change in the collective avoidance behaviour among people living or working in the area? Have hotspots of collective avoidance behaviour shrunk in some areas and expanded in others, or in the best case scenario, have they disappeared entirely from some parts of the city? A followup fear mapping project could be used to investigate such questions in Wollongong. Similarly, Kings Cross has experienced significant change through ongoing City of

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Sydney order maintenance and gentrification strategies since the time of the study presented in Chapter 7. It would be instructive to investigate the same questions posed above in this context as well. At a broader level, fear mapping could be used as part of monitoring and evaluation procedures to assess the impact of urban renewal and gentrification in other locations. Closed-circuit televisionis being increasingly used in to address crime, disorder and fear in Europe and North America (Hier et al., 2007). The effects of CCTV programmes, while often designed to improve safety, may actually serve to increase fear of crime in some individuals (Williams and Ahmed, 2009). Ditton (2000) found that the instillation of CCTV cameras in Glasgow did not deliver an improvement in feelings of safety among survey respondents. This was despite a majority of the sample indicating that CCTV would make them feel safer. Such findings point towards a strong need for rigorous monitoring and assessment regarding such programmes. Given the relative simplicity and cost effectiveness of the cognitive mapping procedure, as well as the increased adoption of GIS software by government and non-government agencies, fear mapping projects could be conducted by local authorities and integrated with urban renewal, gentrification and CCTV programs.

Investigating Behavioural Responses in Relation to Different Types of Crime In order to overcome limitations associated with cognitive and emotional measures, in both the Wollongong and Kings Cross study, we used an approach that was spatially explicit and specifically tapped fear of personal crime. The underlying motivations for collective avoidance behaviour were investigated in detail in the Kings Cross research and the outputs demonstrated considerable variation in patterns across time and space according to different types of social and physical disorder. The diverse results illustrate the complexity of fear of crime but they also point towards the potential utility of investigating the spatial nature of behavioural responses in relation to other types of crime. For example, a frequent observation in the literature is that altruistic fear, namely fear on behalf of others, may be an important issue for some people but one that is rarely investigated (e.g. Snedker, 2006; Tulloch, 2004; Warr, 2000). What are the likely behavioural responses to, and consequences of, altruistic fear? One possible connection may relate to parents placing restrictions on their children in terms of being out in neighbourhoods alone. This, in turn, could have health-related implications in terms of physical inactivity and childhood obesity which is an emerging concern in many western countries (e.g. Nitzko, 2010; Rahman et al., 2011). It would also be possible to investigate what specific crimes trigger altruistic fear and to customize fear mapping exercises to tap relevant types of fear. In a similar vein, recent studies looking into fear of crime among university students suggest that the types of crime that students fear

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most, and how those fears manifest themselves, may differ from other sectors of society (e.g. Barberet and Fisher, 2009; Cubbage and Smith, 2009; Morrall et al., 2010; Wu, 2010). Morrall et al. (2010: 827) argue that [Students] are enculturated with values and codes of behaviour that are internally normative for both the culture of their host organization and their subcultural lifestyle. Whether they are attending inner-city or outer-city universities, students tend to congregate in specific geographical locations both for leisure and residence (‘student ghettos’). . .These factors are likely to shape the process of victimhood, reactions to actual or perceived risk of victimization, and health effects of crime and the fear of crime.

Such observations are consistent with informal discussions that frequently took after the two-step survey procedure used in the Wollongong study. In the case of young men, who were under-represented in the sample, a number of participants stressed that while they were not afraid of being robbed, beaten or attacked in the CBD area, they were very concerned about theft when surfing at nearby beaches. One respondent described how he was often very afraid about leaving his wallet and car keys either on the beach or in his car after being a frequent victim of theft. In his case, he felt compelled to change the times or places where he surfed but he also made it clear that this was a trade-off with the desire to gain access to the best surfing conditions. As such, it would seem that replicating fear mapping in relation to different types of crime that are directly relevant to different subgroups within society may be an avenue worthy of exploration. It would also seem that there is scope for more innovative models to investigate behavioural responses to fear. In relation to female students on university campuses, Cubbage and Smith (2009) propose . . .that women adopt a range of strategies to manage their fear, and take personal security measures such as moving in groups, monitoring their environment, remaining alert and aware of surroundings and other people, enrolling in self-defence courses and carrying mobile telephones and mace spray. These strategies indicate that women generally maintain a positive attitude and refuse to assume the role of victim. Women display a sense of territorial concern for the open space and maintain surveillance of their area.

These observations bear a striking similarity to behavioural theories describing predator-prey interactions in terrestrial animals. A number of studies have investigated the effect of moonlight, a factor generally linked with increased exposure to predation, on the foraging behaviour of small terrestrial mammals (e.g. Price et al., 1984; Daly et al., 1992; Kotler et al., 1993). These studies have generally found that animals respond to bright moonlight by reducing their activity levels and concentrating their foraging in denser micro-habitats. This behaviour is frequently attributed to a predator response, whereby animals balance the marginal value of energy against the cost of predation (e.g. Price et al., 1984; Daly et al., 1992; Kotler et al., 1993; Kotler et al., 1994; Rogowitz, 1997). This trade-off between the need to access suitable food resources and the risk of predation is termed the ‘ecology of fear’ by Brown et al. (1999). This approach to predator-prey interactions is based on the assumption that fierceness is a property of the prey, rather than the predator (Brown et al., 1999). In a similar sense, the ability of people to adopt avoidance

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behaviours in relation to their fear of crime is a proactive response to risk (Oc and Tiesdell, 1997). However, as with the logic behind the ‘ecology of fear’, the adoption of avoidance behaviour involves the loss of access to potential resources. In the case of animals this is potentially detrimental to overall fitness and chances of survival. In the case of people adopting avoidance behaviour in relation to their fear of crime the cost can be expressed in terms of isolation, reduced potential to form social ties with neighbours and increases in levels of mistrusts (Ross and Mirowsky, 2000). A further parallel with predator-prey interactions relates to prey displaying higher degrees of vigilance when they encounter a predator (Jarman and Wright, 1993). Brown et al. (1999) suggest that as a predator becomes more abundant, prey should become more vigilant. The results from the exploratory activity diary analysis conducted as part of the Wollongong study suggest a similar pattern. In general, the results demonstrate that people seem more likely to adopt protective measures in situations where they have less potential to adopt avoidance behaviours and, therefore, are exposed to a relatively higher risk of victimization. The greater likelihood of respondents adopting protective measures in relatively more fearful situations supports the results of studies into micro-level cues to fear of crime (e.g. Nasar et al., 1993; Nasar and Jones, 1997). Interestingly, Jackson and Gray (2010) conducted the first empirical study which differentiated fear into something that is dysfunctional compared to something that is functional. They found that recently some of the respondents took precautions that made them feel safer and neither the precautions nor the worries reduced quality of life. The authors pose a counter argument to the notion that fear of crime is an unqualified social ill and ask whether some level of emotional response may comprise a natural defence against crime. In a field where innovation is rare, it would seem that a transfer of well-developed logic from predator-prey modelling to investigate the functional role of fear of crime may be one useful future research direction.

Further Avenues for Investigating Links Between Fear, Crime and Disorder There is also potential to integrate fear mapping exercises with longitudinal studies that have a spatial focus such as ‘The Crime Experiment’, an innovative study currently being undertaken by the Greater Manchester Police and Professor Lawrence Sherman from Cambridge University. The aims of the experiment are spelt out in the following quotation: The idea is based on earlier American tests of policing crime hotspots which Professor Sherman developed in the late 1980s. In 1987, he discovered that just 3% of the street addresses in Minneapolis produced more than half of all calls to police. In parts of the US, concentrating police on these streets has since successfully cut crime by two-thirds within the hotspots. What remains unknown from US studies is how much this strategy may encourage offenders to commit crimes at other locations. . . The idea of systematically mapping police patrols so that they focus on a list of long-term pressure points has never

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been subject to a controlled test in Britain. . . The experiment will divide 200 hotspots into two groups. The first will be policed normally, but in the second, the police presence will be intensified with officers stationed in pressure points for many more minutes during highcrime periods. Researchers will then test the comparative effects over the course of about a year, measuring the average change in crime over time in one group with that of the other. (University of Cambridge, 2011).

Research of this nature will undoubtedly provide fresh insights into questions of displacement and the effects of strategic policing at a local level. From a broken windows perspective, such projects would also be an ideal setting to explore the role of fear of crime in relation to patterns of crime and disorder over space and time. As set out in Chapter 6, the spatial arrangement of collective avoidance hotspots relative to concentrations of crime or disorder may facilitate the linking up of crime or disorder hotspots over time. A recent study conducted over two years in Canada used a very similar approach to ours to investigate spatial patterns of fear of crime in relation to disorder within a high-crime community (Kohm, 2009). The author used ‘perceptual mapping’ to look into areas where respondents felt unsafe and why, different types of social disorder, frequency of victimization and the perceived crime rate relative to other neighbourhoods. The key finding regarding spatial patterns of fear was that Perceptual mapping has considerable potential to shed new light on the micro-level spatial dynamics of neighbourhood fear. As discussed above, the mapping exercise in this study suggests that disorder is a powerful explanation for the spatial patterning of fear at the local level. In addition, the spatial pattern of fear associated with disorder appears more focused and arguably more accurate than spatial fear attached to crime.

Kohm (2009) emphasizes that while links between disorder, crime and fear are problematic and that there are many justified criticisms of these relationships (e.g. Harcourt, 1998; Harcourt and Ludwig, 2006), there is more than simply anecdotal evidence for disorder being linked to fear of crime. The author also draws attention to the ongoing influence of the broken windows theory on policy initiatives aiming to address crime in North American central cities and cites O’Shea (2006: 174) who argues that the theory is the most ‘. . .policy-influencing work in the crime and place literature’. These trends alone, serve to justify spatially explicit analyses of fear, crime and disorder. However, the influence of the broken windows theory and zero-tolerance style policing extends beyond Northern America, and beyond the city context. A very topical issue in Australia at the current time is the federal government’s response to the plight of Aboriginal people in the Northern Territory. In 2007, the Australian Commonwealth Government introduced the Northern Territory National Emergency Response Act (NTERA) which has become more commonly known as ‘the intervention’. This act is explicitly race-based in that it applies legislation to all Aboriginal communities in the NT, termed ‘prescribed areas’ under the Act. This controversial act was designed to positively impact the lives of Aboriginal people in remote communities distributed throughout the Northern Territory. One of the primary intervention measures took the form of welfare quarantining, or income management, for all Aboriginal people receiving welfare payments. This measure

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was designed to reduce the proportion of social security payments spent on ‘antisocial behaviours’ such as excessive gambling and alcohol consumption. The stated rationale of this measure was to ‘protect children and make communites safer’ (AIHW, 2009; Commonwealth of Australia, 2007; FaHCSIA, 2009; Stevens and Young, 2009). Therefore, it can be argued that the federal intervention draws upon the logic of broken windows in identifying the control of disorder, both social and physical, as a means of preventing longer-term detrimental impact on communities across the Northern Territory. In conjunction with the initiatives implemented as part of the federal intervention, there have been major changes or additions to local council by-laws in remote towns across the Northern Territory such as Alice Springs, Tennant Creek, Katherine and Nhulunbuy. In the case of Alice Springs, the Management of Public Spaces Bylaws 2009 (ASTC, 2010) were put into effect on 1 February 2010. The Human Rights Law Resource Centre (HRLEC, 2009) and the Tangentyere Council (2009) have raised a number of serious concerns in submissions to the Alice Springs Town Council when the by-laws were first proposed. Many of these concerns focused on how the definition and localized policing of antisocial behaviour is likely to have a disproportionate impact on vulnerable sectors of the community, particularly homeless people, young people and Aboriginal people. Some of the provisions that drew the most concern from the HRLRC and Tangentyere Council are outlined below in the table below (Table 8.1). Many of these concerns receive support in the context of broader literature on the management of social disorder. For example, Noaks (2004) investigated the impact of private security on a local community in the UK and found that the private company’s style of policing was based on targeting individuals, thereby jeopardizing individual rights and civil liberties. In South Africa, Berg (2010) found that private security companies interpreted by-laws in fluid ways when managing public space and went on to make a strong argument for a greater understanding of the hierarchical structure of policing (i.e. the relationship between private and state-controlled measures), as such measures are becoming progressively more spatially intrusive. In the case of Alice Springs, the concerns expressed by the Tangentyere Council (2009) and the HRLRC (2009) suggest that the measures enacted under the federal intervention and the ASTC Management of Public Spaces By-laws, may have given rise to rapid ecological change within the community and the displacement of social and physical disorder among vulnerable sections of the community. According to a number of authors (e.g. Sampson and Groves, 1989; Markowitz et al., 2001), communities that experience rapid ecological change are more likely to show increases in crime and fear of crime. In turn, fear of crime can potentially give rise to protective and avoidance behaviours, further atomization of the community and create potential opportunities for crime (Doran and Lees, 2005; Kelling and Coles, 1997; Taylor and Covington, 1993). As such, there is an urgent need to gain insights into the spatial, temporal and social impact of the recent policies implemented in towns across the Northern Territory like Alice Springs. A Geographic Information Systems (GIS) and cognitive mapping-based approach is a potentially appropriate means of investigating these issues.

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Table 8.1 Summary of concerns raised in submissions to the ASTC regarding the management of public spaces by-laws 2009 ASTC management of public spaces by-laws 2009 clauses • Clauses 49–50, 52–56 which are provisions that create a number of public space offences

• Clause 57 which contains provisions that criminalize begging

• Clauses 80, 81 and 83 that extend police powers to council officers, particularly ‘move on’ powers

Concerns expressed • ‘Given that the activities mentioned above are offences when they are committed in public spaces, these offences will disproportionately impact on people who are homeless’ (HRLRC, 2009) • ‘The Proposed Bylaws will have a substantial impact on people in public places in the Alice Springs area, many of whom are Aboriginal’ (HRLRC, 2009) • ‘Tangentyere is concerned that the by-laws indicated above will have a significant impact on those individuals who are forced into the condition of “sleeping rough” due to a lack of appropriate accommodation’ (Tangentyere Council, 2009) • ‘The implementation of these by-laws is a contradiction to the Alice Springs Transformation Plan which recognizes that homeless people are disadvantaged and “at risk”.’ (Tangentyere Council, 2009) • ‘It is difficult to understand what will be achieved by fining people, who have to resort to begging in the first place. . . .The proposed fine system is a very harsh one which we believe will disproportionately target Aboriginal . . .Even if people can somehow pay their fine, their income will be further reduced, and they may have to again resort to begging’ (Tangentyere Council, 2009) • ‘. . .given the financial status of the people who will be targeted by the begging offence, it will be highly unlikely that, if found guilty, the individual will have the ability to pay any fine imposed on them. . . .Clause 57 also discriminates on the basis of social status, as it disproportionately impacts on persons who are living in poverty’ (HRLRC, 2009) • ‘These provisions, in effect, enable Council officials to act in the capacity of police officers, without having to undergo law enforcement training’ (HRLRC, 2009) • ‘These provisions effectively give an authorised officer the power to forcibly remove a person from a park, garden or reserve and to ban them from returning to such public place for up to 6 h. . .The move on powers clearly target persons sleeping or inhabiting public spaces, and to the extent these provisions disproportionately affect Aboriginal people’ (HRLRC, 2009) • ‘Tangentyere is concerned that this draft by-law will bring those people in contact with the law who may be doing nothing intrinsically wrong. The execution of this by-law is likely to create circumstances that could lead to altercations between people being targeted and the ASTC’ (Tangentyere Council, 2009) • ‘Tangentyere feels that these powers should be reserved for the police. Police receive sufficient training and operate under a transparent and well-established code of conduct/procedural framework’ (Tangentyere Council, 2009)

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The federal intervention and its impact across the Northern Territory on the policing of disorder is used as an example here to emphasize the need for research into fear of crime to extend beyond high-density urban environments in the United States, the United Kingdom and Australia. There are increasing calls for this to take place. For example, Morrall et al. (2010) highlight the need for fear-of-crime research to be conducted in universities outside city conurbations. Karakus et al. (2010) note that there is a growing interest in cross-cultural research in criminology and validity of existing fear-of-crime models may be limited given this bias of studies in western countries. The recent increase in fear-of-crime studies in nonwestern contexts (e.g. Adu-Mireku, 2002; Karakus et al., 2010; Liu et al., 2009; Zhang et al., 2009) strongly suggests that there will be a growing need for appropriate tools for cross-cultural means of communication in the field and, potentially, approaches other than traditional survey techniques to investigate fear of crime. An important contribution made by Kohm (2009) was to combine mapping exercises on fear of crime with qualitative questions in an attempt to gain a more nuanced understanding of spatial dimensions of fear. Similarly, Soini (2001) suggested that combining mapping exercises with other tools such as interviews is an appropriate means of obtaining more complex spatial information. This is reflective of a broader attempt to more frequently incorporate qualitative approaches into GISbased research (Leszczynski, 2009). Cognitive mapping is often used in conjunction with qualitative interviews in Participatory GIS (PGIS) research, which has been particularly effective in aid and development applications (e.g. Bauer, 2009). Thus it would seem that there is genuine potential to adapt cognitive mapping and techniques from behavioural geography in general for use in these emerging areas of fear-of-crime research in non-western and non-urban contexts. In a recent review of behavioural geography, Argent and Walmsley (2009) note the vast contribution of this area to the broader discipline of geography and emphasize that ‘. . . it highlighted the need to consider interrelationships between individuals, groups, society and environment thereby bringing into prominence the ways in which shared environmental meanings are contested and negotiated’. However, the authors also note that behavioural geography has shifted from a ‘. . .cutting edge sub-discipline to a branch of enquiry that is now much less prominent in mainstream human geography’ and that it may increasingly find expression in behaviour-oriented research of an interdisciplinary nature. While this trend represents a loss for geography in general, it has been our experience that the GIS-based analyses presented in this book were richer and more rigorous through drawing techniques and principles from the field of behavioural geography. If researchers looking into fear of crime are to address the calls and challenges of investigating the issue beyond densely populated city environments in western countries, it is likely that approaches based on behavioural geography will make a valuable contribution.

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Broken Windows Theory in the Transit Context Evidence for the broken windows theory is often found in transit environments (e.g. Loukaitou-Sideris, 1999; Loukaitou-Sideris et al., 2002). Indeed, the development and early focus of the theory has its roots in the New York Transit System (Jang et al., 2008). The use of public transport is a discretionary activity for many people. Discretionary activities, no matter how attractive they are as options, are likely to be forgone if people feel their safety cannot be guaranteed (Cothran and Cothran, 1998). Thus, fear of crime, and avoidance behaviour in this context incurs a direct cost to transit authorities through loss of ridership (Loukaitou-Sideris et al., 2002). As argued elsewhere in this book and by others (e.g. NCAVAC, 1998), a key factor in being able to address the negative aspects of fear of crime is an understanding of where and when people are afraid of crime. Traditional survey tools are limited in this respect, as they are not geographically structured. Beyond illustrating the extent of fear of crime within administrative boundaries, such as census districts or suburbs, traditional surveys cannot highlight the times and areas that people avoid due to their fear of crime. Fisher and Nasar (1995) argue that this means much extant research is limited because studies using standard global measures cannot reveal the location of specific ‘fear spots’ or what types of cues stimulate the feargenerating process in individuals or across groups. In turn, this presents a substantial problem for the institutions responsible for managing fear of crime, including transit authorities. GIS began to be used widely in transportation research and management in the late 1980s (Thill, 2000). Applications frequently involve using GIS as a decision support system (DSS) in urban transportation policies (e.g. Arampatzis et al., 2004; Horner and Grubesic, 2001). Often these applications focus on improving the efficiency of services (e.g. Murray, 2003) or evaluating suitable locations for associated facilities such as park-and-ride lots (e.g. Horner and Grubesic, 2001). O’Sullivan et al. (2000) argue that the goal of any transport system is not mobility per se, but access to facilities. Accessibility is considered to be a multifaceted concept which, in part, includes safety in getting to and from the points of access on the transit system to one’s intended destination (Murray, 2003). Loukaitou-Sideris et al. (2002) emphasizes that perceptions of violence also cause loss of ridership and revenue by impacting on people’s decisions to use public transportation. There exist a number of well-developed transport GIS approaches specifically tailored to investigate the spatiotemporal nature of accessibility and interactions between origin boarding and alighting points as well as the transport network itself (e.g. Liu and Zhu, 2004; Yigitcanlar et al., 2006). Adapting such approaches and integrating them with behavioural measures could further the understanding of fear of crime in transit situations and may prove a powerful means of informing strategy and policy designed to improve accessibility. For example, the results from any such research could be used in situational crime prevention in transit contexts and assistance in ranking crime- and disorder-related problems along rail lines (e.g. Loukaitou-Sideris et al., 2002).

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Fear Mapping and Advances in Spatial Technology A final area where there are numerous potential applications for fear mapping relates to rapid advances in spatial technology. Elwood (2009:256) writes eloquently on emerging questions and linkages between GIS-based research and the rapid development of geospatial technology: In the world of geospatial technologies, change is afoot. In the past five years, we have seen the emergence of a wide array of new technologies that enable an ever-expanding range of individuals and social groups to create and disseminate maps and spatial data. Online mapping platforms such as Google Maps and Google Earth, Microsoft’s Virtual Earth, or Wikimapia allow users to add their own geographic information to web-based displays or modify the contributions of others.

A fascinating series of applications are presented the book ‘Emotional Cartography: Technologies of the Self’ which displays biometric measurements in relation to geocoded locations (Nold, 2009). A visualization displaying heightened physiological arousal at a busy traffic intersection (Nold, 2009: 13) is conceptually very similar to experiments by Nasar and Jones (1997) who provided respondents with a dictaphone and asked to them verbally record feelings of safety or unsafeness and any emotional reactions while walking a specified transect at night. In relation to fear mapping, Kohm (2009) suggests that hard-copy mapping exercises could potentially be shifted to a computer-based environment and eliminate the need to digitize off paper. Similarly, Bugs et al. (2010) suggest that Web 2.0 applications facilitate the creation of sophisticated mapping interfaces that improve participatory mapping and strengthen interactions between the public and decision makers. While there is no doubt merit in these claims, and many innovative visualization avenues to explore, other researchers caution that when used for data collection, the challenges of web-based citizen science are challenging and, to be effective, websites must be easy to use, support a range of tasks and ensure data quality (Newman et al., 2010). Given the general methodological challenges of obtaining valid fear-of-crime data, web-based mechanisms for fear mapping may prove to be a confounding influence rather than an improvement to traditional approaches if not carefully implemented. Elwood (2009) describes a website based on Google Maps that is used to display the attributes of ‘rotten’ neighbours. Expressed in terms of different types of disorder such as noise, trash and nosiness, Elwood (2009) emphasizes that despite social concerns about such a site, it would generate spatial data that is patchy and not necessarily representative of the communities where it is obtained from. A further issue regarding the suitability of web-based fear-of-crime research relates to the fact that many of the subgroups that generally exhibit higher levels of fear, such as the elderly, people of non-Caucasian origin and the poor, may also be subject to the ‘digital divide’ and often have relatively lower IT literacy or access to the internet (Bruno et al., 2011). A greater contribution may lie in the application of more sophisticated GIS-based models to the analysis of fear of crime. Index models are a powerful means of conducting combinatory analyses (Chang, 2010). They have been used extensively to investigate issues characterized by complexity and produce outputs that explicitly support decision making in biophysical and social settings (e.g. Carver, 1991; Jankowski et al., 2001; Jiang and Eastman, 2000;

References

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Doran and Young, 2010). Algorithms such as G∗ and Geographically Weighted Regressions (GWR) provide rigorous means of assessing the spatial significance of locational attributes (e.g. Cromley and McLafferty, 2002; Fotheringham et al., 2006; O’Sullivan and Unwin, 2010) and go far beyond describing the spatial concentration of and intensity of phenomena, as is often the case with hotspot and overlay models. In various studies, Kwan (1999a, 1999b, 2000a, 2000b) has demonstrated the use of techniques for space–time visualization of activities and has investigated issues such as employment-related restrictions among women working full or part-time. In essence the impact of fear of crime is inherently geographic – it varies over time and space. Kohm (2009) emphasizes the limited nature of literature covering the spatial impact of fear of crime. It would seem there remain many openings for GIS-based research to make valuable contributions to the understanding and management of this pervasive problem, and many more ways in which spatial science can be used to ‘put fear on the map’. In the final analysis, it is our hope that the GIS-based approaches used to investigate fear of crime in the Wollongong and Kings Cross studies may contribute on several levels. First, the studies provided locally specific information on where and when people were afraid of crime in the study areas. In Wollongong, the research findings and spatial outputs were integrated with crime prevention and city centre revitalization strategies. Being able to identify hotspots of collective avoidance and overlaps with crime and different types of disorder, the outputs were useful in defining the degree of institutional involvement and strategic inputs from different agencies concerned with the management of crime, disorder and fear within the CBD area. The approach developed in the Wollongong study was transferred and refined for the research subsequently conducted in Kings Cross where there was a fear mapping program already underway. The research team worked alongside the NSW Police Service to gain a deeper underlying of motivations for avoidance behaviour, as well as responses to different cues. The benefits of 3D visualization were explored, as well as extending GIS-based analysis of fear of crime to a densely populated inner-city area characterized by high crime rates. As outlined in this chapter, there are a number of avenues to explore in both of the study sites and to extend the approach more broadly. In terms of challenges facing fear-of-crime research at the current time, there appear to be many opportunities for GIS-based behavioural research to make valuable strategic and methodological contributions to the field.

References Adu-Mireku, S. (2002). “Fear of crime among residents of three communities in Accra, Ghana”. International Journal of Comparative Sociology 43: 153–168. AIHW. (2009). Report on the evaluation of income mangement in the Northern Territory. Canberra. Alice Springs Town Council (ASTC). (2010). Alice Springs Management Of Public Places Bylaws 2009. Alice Springs Alice Springs Town Council (ASTC). Arampatzis, G., C. T. Kiranoudis, et al. (2004). “A GIS-based decision support system for planning urban transportation policies.” European Journal of Operational Research 152(2): 465–475. Argent, N. M. and D. J. Walmsley (2009). “From the inside looking out and the outside looking in: whatever happened to ‘Behavioural Geography’?” Geographical Research 47: 192–203.

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Index

Note: The letters ‘f’ and ‘t’ following the locators refer to figures and tables in the text. A Activity diaries, 84–86, 96–98, 103, 105–106, 139, 141, 140t–142t, 143, 145, 149–151, 256 behavioural geography technique, 85 and daily routines, 85–86 diary method, 85 game-based method, 85 recall method, 85 time-space budgets, 86 Adams, R. E., 52–53 Adu-Mireku, S., 260 Age, 104, 115, 115f, 116, 162, 169, 172–173, 175f, 206, 207t Ahmed, J., 254 Akers, R. L., 27 Allen, G. L., 84 Allen, J., 3, 9, 38–39, 73, 79–80, 102, 143, 191, 218 Altruistic fear, 71–72, 254 Anticipatory fear, 30, 70 Anti-social behavior, 33, 57, 164, 198, 226–227, 258 Anxiety, 9, 27, 30–32, 35, 37, 43, 67, 70 Areas to hide, 41, 170t–171t, 193t, 194–195, 205, 206f, 229, 242 Argent, N. M., 260 Arnold, H. R., 10, 18, 145 Ashby, D. I., 51, 81, 86 Avoidance-based measure of fear, 78–79, 166, 169, 178t Avoidance behavior, 9–12, 16–17, 19, 32, 78–79, 82, 84–85, 87–88, 95, 103, 105–107, 112, 115, 119, 120t, 133, 135, 144–147, 149–151, 172f, 179–180, 188, 192, 193t, 194–195, 201–202, 205, 208, 214–215,

218–220, 227, 242, 251, 253–256, 258, 261, 263 Ayers, I., 16, 18–19 B Baker, T., 52–53, 86 Balkin, S., 2–3 Bannister, J., 9 Barberet, R., 255 Bauer, K., 260 Beck, U., 31–32 Beck, V. S., 71 Behavioural responses, economic impact of, 16–19 avoidance behaviours, safety concerns, 17 CCTV surveillance system, 18 ‘hidden costs,’ 16 ‘mall problem’ in Australia, 16–17 protective behaviours, 17–18 protective measures and redistribution of crime between communities, 18–19 ‘pub-and-club’ youth culture, 17 ‘safety elasticity of demand,’ 17 spatial concentration of crime in poor neighbourhoods, 18 work in evenings and at night, 17 Behavioural theories, predator-prey interactions, 255–256 Bennett, T., 15, 52–53, 98 Berg, J., 258 Bialystok, E., 149 Blakely, E. J., 60 Blalock, H., 35, 169 Blocked escape, 171t, 193t, 194, 205, 230 Block, R. A., 82–84, 191–192, 242 Borooah, V., 2, 12, 15, 27, 53, 73, 104, 117, 143

B.J. Doran, M.B. Burgess, Putting Fear of Crime on the Map, Springer Series on Evidence-Based Crime Policy, DOI 10.1007/978-1-4419-5647-7,  C Springer Science+Business Media, LLC 2012

269

270 Bowers, K. J., 11, 86 Bowling, B., 13, 55 Box, S., 2, 4, 9–10, 19, 80, 104 Braithwaite, D., 201, 227 Brantingham, P. J., 25, 40, 57, 82, 187–188 Brantingham, P. L., 25, 40, 57, 82, 187–188 Bratton, W. J., 11, 55–56, 87, 95, 146 Brennan, A., 11 Briscoe, S., 219 British Crime and Disorder Act 1998, 58 Broken windows hypothesis, 11, 12f, 13, 15, 19, 51–52, 55, 95–96, 112, 115, 129, 137–138, 145, 146t, 150, 170t, 257–258, 261 Bromley, R., 3, 11, 17, 61, 80, 86, 109, 138 Brown, B., 213 Brown, J. S., 255–256 Brown, M., 3–4, 9, 16, 53, 60 Bruno, G., 262 Brunt, P., 17 Bugs, G., 262 Burgess, M., 166 Burnett, P., 83–84, 179 Burrough, P. A., 86 Bursik, R. J., 25, 33–34 Butel, E., 156, 160–162 C Cameron, D., 53 Carcach, C., 2, 12, 15, 27, 53, 73, 80, 104, 117, 143 Carlson, J., 68–70 Carvalho, I., 38, 77–78 Carver, S., 262 Cates, J. A., 27, 30, 82 Causes, fear of crime criminal opportunity and risk of victimization theories, 25–26 demographic theories, 26–31 environmental theories, 38–44 social theories, 31–37 See also Individual entries CCTV Closed Circuit Television, 18, 41, 60, 147–148, 148f, 222, 226, 238f, 254 Central business district (CBD) area, 82, 95–96, 98–101, 99f, 103–107, 110–111, 119–120, 121f, 122, 122f, 123, 128, 131f–132f, 133–134, 137–139, 145–151, 177, 251, 253f, 255, 263 Chadee, D., 3, 86

Index “The Challenge of Crime in a Free Society” (paper), 1 Chang, K., 106, 262 Chiricos, T., 28, 35 City Centre Revitalisation Strategy, 147, 151 City of Sydney Council, 181, 184, 187, 222–224, 226–234 City Street Outreach Service, 232 Clarke, A. H., 213, 218 Clark, J., 2, 26–27, 29, 37, 69–70, 76, 201 Clemente, F., 2–4, 220 Clerici, C., 2, 28, 73, 180 Cochran, J. K., 25, 33 Cognitive assessments, 68, 179 Cognitive barriers, 187, 242 Cognitive mapping, 82–84, 95, 103, 105, 133, 144, 146, 149, 151, 241, 254, 258, 260 Cohen, L. E., 4, 25 Coles, C. M., 4, 15, 18, 52, 55, 87, 95, 112, 138, 146, 169, 235, 258 Collective avoidance, 13, 78, 82, 106, 106f, 107, 112, 114f, 119–122, 129–130, 132–135, 137–139, 145–146, 149–150, 172–173, 181, 183f, 187–188, 192, 220, 230, 235, 251, 253–254, 257, 263 Community concern hypothesis decline model, 36 neighbourhood, vitality/viability/quality of, 36 Community oriented policing, 51–53, 56 Concern or worry about crime, 69, 75 Conklin, J. E., 2, 16, 36 Contemporary criminology, 3 Control signals, 41 Cordner, G. W., 53 Cothran, C. C., 17, 261 Cothran, D. A., 17, 261 Couclelis, H., 83–84, 180 Covington, J., 3–4, 11–12, 31, 33–36, 73, 87, 95, 104, 171t, 258 Cozens, P., 25, 40–41, 57–58 CPTED, see Crime Prevention Through Environmental Design (CPTED) Crank, J. P., 26–27, 29, 36, 38–39, 52 Cressey, D. R., 70 Crime Prevention And Community Safety Plan, 96, 138–139, 146, 151 Crime Prevention Through Environmental Design (CPTED), 56–59, 147, 181, 187–188, 222 Crime specificity, 74, 76, 78

Index Crime-specific measure of fear, 74, 76, 166, 169, 178–179, 178t, 181 Criminal opportunity and risk of victimization theories, 25–26 symbolic interactionism, 25, 43 variation in routine activities, 25 Criminal opportunity theory, 25 Cromley, E. K., 80, 263 Crowe, T. D., 57 Cubbage, C. J., 10, 40, 255 Cummings, A., 201, 227 D Daily routines, 74–75, 85–86, 96, 107, 139, 141, 143–145, 151 Daly, M., 255 Darcy, D., 155, 162, 164, 166, 168, 171t, 198–199, 227–230, 232, 234 Darkness, 58, 83, 177, 190 Davidson, N., 58–60 Davis, M., 18, 60 Day, R. A., 84, 191, 242 Dean, M., 31 Decline model, 36 ‘Defensible space’, concept of, 57–58 Defining fear of crime emotion, not cognition, 68–69 cognitive assessments, 68 concern or worry about crime, 69 emotion, definition, 68 negative emotional reactions, 68 perceived risk, definition, 68 perceptions of risk and threat of victimization, 68–69 taxonomy of crime perceptions, 68t individual terms ‘fear’ and ‘crime’, 67, 70–71 involves violation of criminal law, 70–71 social codes or ‘laws of morality’, 71 social concept of crime, 71 traditional jurisprudential definitions, 70 relation to emotional reaction/stimuli, 69–70 immediate danger, 70 one of primary human emotions, 69 symbols associated, 69–70 warnings, 69 types of fear of crime, 71–72 altruistic fear of crime, 71 personal and altruistic fear, distinguishing, 71–72

271 subject of victimization (personal or altruistic), 71 type of victimization (personal or property), 71 DeFronzo, J., 77 Delinquent behaviour, 33 Demographic theories indirect victimization hypothesis interpersonal communication and fear, 29 media and fear, 28 victimization hypothesis, 26–27 vulnerabilities hypothesis, 29–30 Department for Transport Urban Planning and the Arts in Australia, 57 Dickinson, J., 28, 69–70 Dietz, A. S., 52–53 Dililio, J. J., 18 Ding, Y., 86 Disorder and decline hypothesis, 13–16 causal effect, 16 economic impact, 14–15 fear of personal and housing crime, 15 flow chart, 14f links between serious crime and, 15 perceptions, 13–14 signs/impacts, 13 Disorder/incivilities hypotheses, 11–14, 36, 38–43, 195, 204 ‘direct’ encounter, 39 disorder, definition, 39 incivilities, definition/warning signals, 38–39 ‘less targeted’ encounter, 39 physical disorder, 39 signs of disorder or visible cues, 38 social disorder, 39 social incivilities, 39 ‘soft’ crimes, 38 Disorder/incivilities, targeting pertinent signs (King Cross study), 223–240 addressing environmental cues, 235 Council signs to control disorder, 238f lower ranked environmental cues better street lighting, 230–231 maintenance of rundown and abandoned buildings, 231–232 offensive/degraded shops, 233–234 sex workers, 234 spruikers, 234 supporting homeless people, 232 middle-ranked environmental cues

272 Disorder/incivilities, targeting pertinent (cont.) accessibility and minimizing blocked escape, 230 moving loitering people, 229–230 removal of areas to hide, 229 revitalization of laneways and streets, 227–228 safe collection of rubbish/syringes/ injecting equipment, 228–229 Sydney place, 237f–238f public/private CCTV, 238f targeted intervention, 235–236 top-ranked cues minimizing harm from drug use and dealing, 226–227 reducing levels of intoxication, 224–226 Disorderly behaviour, 11, 12f, 221, 235 Disorder policing, see Zero-tolerance policing Ditton, J., 3, 18, 35, 60, 67, 69, 73, 76, 78, 86, 169, 220, 254 Dixon, J., 53 Doeksen, H., 2, 19, 39, 41, 59, 171t Donnelly, N., 219 Doran, B. J., 11–12, 40, 78, 80, 82, 86, 146–147, 151, 168, 177, 181, 258, 263 Douglas, M., 32 Downs, R. M., 82–84, 149, 191, 241 Drug users, 39, 168–171, 193t, 194–199, 207–211, 213, 216f–217f, 218–219, 224, 226, 228, 235–236, 242 Drummond, W. J., 110 E Eastman, J. R., 262 ‘Ecology of fear’, 255–256 Eco, U., 42 Egenhofer, M. J., 86 Ellison, C. G., 17, 77 Ellis, R., 156, 160–161 Elwood, S., 262 ‘Emotional Cartography: Technologies of the Self’, 262 Emotion-based measures of fear, 75–76 Emotion, definition, 68 Environmental cues, 13, 25, 41, 44, 58, 69–70, 84, 155, 166, 169, 170t, 172, 177, 180, 187–188, 191–192, 193t, 194–195, 202, 207, 223, 227, 229–233, 235–236, 241–242 lower ranked better street lighting, 230–231

Index maintenance of rundown and abandoned buildings, 231–232 offensive/degraded shops, 233–234 sex workers, 234 spruikers, 234 supporting homeless people, 232–233 middle-ranked accessibility and blocked escape, 230 moving loitering people, 229–230 removal of areas to hide, 229 revitalization of laneways and streets, 227–228 safe collection of rubbish/syringes/ injecting equipment, 228–229 sensitively addressing, 235 social/physical environmental cues used in survey, Kings Cross study, 170t–171t triggering fear of crime, 192–207, 193t Environmental design, 56–61 CPTED, 56 British Department of Environment’s Secured by Design scheme, 58 concept of ‘defensible space’, 57 controls, 58 primary goal, 57 criticisms, 60 environmental or order-related improvements, 58 criticisms, 60 improved lighting, 58 ‘fortress city’, concept of, 60 negative effects of protective measures, 60 situation in Sweden (case), 60 Environmental mobility restrictor, 10 Environmental theories, 38–44 disorder/incivilities hypothesis, 38–39 signal crimes perspective, 41–43 threatening and safe, 40–41 See also Individual entries Erskine, H., 2 Ethnicity, 35, 104 Evans, D. J., 27 Ewald, U., 29, 31–32, 67, 70 Experience of victimization, 1, 26–27, 29, 117, 118f, 175–176, 176f Exposure to risk, 30, 77, 145, 205 See also Vulnerability F Fagan, S., 97 Familial controls, breakdown in, 33

Index Farrall, S., 3–4, 15, 26, 30, 69, 78, 86, 104–106, 118–119 Fear as a concern, see Social disorganization hypothesis Fear hotspots, 82, 181, 184–185, 188, 190, 196, 221, 228, 242 Fear mapping beginning of, 84–85 advantages of studies, 85 ‘denotative meanings’, or people’s knowledge of a city, 84 pedestrian activity, 85 future avenues for, see Fear mapping, potential applications and improvements integrating results, Kings Cross study, 221–243 Fear mapping, potential applications and improvements broken windows theory in transit context, 261 GIS as a decision support system (DSS), 261 limited traditional survey tools, 261 transit environments, 261 collective avoidance behaviour with Wollongong and Kings Cross, 251–254 fear mapping, use of, 253–254 ‘Market Day Fridays’ in Crown Street Mall, 251 mixed residential apartmentcommercial buildings, 253f public walkway in MacCabe Park (2003 and 2010), 252f investigating behavioural responses in relation to crime, 254–256 complexity, 254 predator-prey interactions, comparison with, 255–256 two-step survey procedure, 255 investigating links between fear, crime/disorder, 256–260 ‘antisocial behaviours’, 258 cognitive mapping and Participatory GIS (PGIS), 260 ‘The Crime Experiment’, 256 effects of strategic policing, 257 GIS and cognitive mapping-based approach, use of, 258 influence of broken windows theory, 257

273 influence of zero-tolerance style policing, 257 management of public spaces by-laws 2009, submissions to ASTC, 259t need for research, 260 need for tools for communication, 260 ‘perceptual mapping’, use of, 257 ‘prescribed areas’, 257 spatial technology, 262–263 G∗ and Geographically Weighted Regressions (GWR), 263 geospatial technology, 262 GIS-based approaches, contributions, 263 hard-copy mapping exercises, 262 index models, 262 space–time visualization of activities, 263 Fear of crime activity diaries and daily routines, 85–86 See also Activity diaries avoidance behaviours, 9–10 disorder/decline hypothesis, see Disorder and decline hypothesis endocrinic changes, 9 environmental mobility restrictor, 10 geographic information systems (GIS), 86–87 See also Geographic information systems (GIS) hypothesized links between the fear of crime, disorder and crime, 11–13 individual reactions, 9–11 mapping, see Fear mapping MAUP and ecological fallacy, effect of, 80–81, 81f physiological changes, 9 protective or avoidance behaviours, 9 psychological perspective, 9 research, 30, 72, 76, 156, 179, 260, 262–263 spatial analyses, advantages of, 81–82 ‘geodemographic’ analyses, 81 spatial cognition and cognitive mapping, 82–84 types of, 71–72 See also Defining fear of crime Fear-reduction strategies, 26, 31, 61, 78, 88, 139, 146, 201, 235–236 Felson, M., 4, 25 Ferraro, K. F., 9, 11, 17, 25, 27, 29, 39, 67–76, 80, 143–144, 171t, 213, 218, 220

274 Fisher, B. S., 9, 40, 74, 78, 81, 84–85, 88, 105, 150, 171t, 181, 191, 205, 255, 261 Fishman, G., 4, 9, 143 Fitzgerald, J., 150 Fletcher, M., 27 ‘Fortress city’, concept of, 60 Fotheringham, A. S., 263 Fotheringham, S., 86 Freeman, S., 18 Freundschuh, S., 84 Furstenberg, F. F. Jr., 1–2, 37, 67, 69, 75 Fyfe, N., 9 G Gabriel, U., 76 Gall, O. R., 72 Gallup and Harris polls, 2 G∗ and Geographically Weighted Regressions (GWR), 263 Gangs, 39, 109t, 109, 160–161, 170t–171t, 193t, 194, 199, 200t, 201–202, 224, 227, 235–236, 240 Garofalo, J., 3–4, 11, 16, 19, 27, 36, 52, 67, 69–71, 73, 78, 213, 218–219 Gender, 4, 43, 169 Geographic information systems (GIS), 79–80, 82, 86–87, 95, 106, 109, 133, 146, 149–151, 172, 254, 258, 260–263 computer-based tools, 87 links between fear and occurrence of crime, 87 NYPD’s CompStat procedure, 87 relationship between fear and victimization, 87 set of tools, 86 usefulness, 86 Geospatial technology, 262 Gibbons, S., 11, 16 Gibson, C. L., 36, 134, 213 Gilchrist, E., 213, 218–220 Girling, E., 31 GIS, see Geographic information systems (GIS) Glackman, W., 30 Glensor, R. W., 51–52 Global measure of fear, 76, 85–86, 104, 119, 143–144, 169, 178–179, 181 Gold, J. R., 81, 235 Goldstein, H., 51 Golledge, R. G., 82–83, 85–86, 105, 139, 145 Gomme, I. M., 78 Goodchild, M. F., 80, 149

Index Gottfredson, S. D., 32, 39, 57 Grabosky, P. N., 3, 9, 16, 53 Graffiti, See Vandalism Graham, J., 60 Grasmick, H. G., 34 Gray, D. E., 27, 77, 213, 218–219 Gray, E., 16, 60, 256 Greene, J., 11, 13, 52, 55–56 Green, G., 9 Grennan, H., 162 Greve, W., 76 Groves, W. B., 33–34, 98, 258 Grubesic, T. H., 261 Gupy, M., 97 H Hale, C., 3, 15, 76, 219–220 Hale, M., 2, 25–26, 29–30, 36, 39, 71, 188 Hamermesh, D. S., 16–18 Hanson, R. F., 3, 27, 73, 213, 218–219 Harcourt, B. E., 11–13, 16, 51–52, 56, 95, 257 Harries, K., 87 Hartnagel, T. F., 2 Hatfield, E., 68–69 Helsley, R. W., 18, 60 Herbert, D., 58–60 Herrington, V., 11, 15, 38, 41, 53 ‘Hidden costs’, 16 Hier, S. P., 254 Hill, G. D., 11, 27 Hindelang, M. J., 78 Hirschfield, A., 11 Hollway, W., 2, 4, 32, 37, 144 Homelessness Brokerage Program, 232 Homeless Persons Information Centre, 232 Homicide, 17 Horner, M. W., 261 Housing tenure, 169, 175, 206, 207t The Human Rights Law Resource Centre (HRLEC), 258 Hunter, A., 34, 37, 39 Husserl, E., 83 Hyman, J. M., 2 Hypothetical questions, 74, 76, 78, 80, 103, 105, 143–144 I Immediate danger, 70 Income, 1, 30, 60, 80, 86, 104, 116, 116f, 162, 169, 172, 176–177, 187, 206, 207t, 257, 259t Indirect victimization hypothesis, 27–29 interpersonal communication and fear, 29

Index media and fear, 28 ‘non-victims’ and fear, 27 Individual reactions, 9–11 avoidance behaviours, 10 endocrinic changes, 9 environmental mobility restrictor, 10 negative feelings, 9 parallel between social exclusion and the fear of crime, 11 physiological and psychological effects, 9 physiological changes, 9 “prisoners of fear” (old people), 11 protective, 9–10 Informal social control, 12–14, 12t, 14t, 33, 38 Innes, M., 13, 28, 41–43, 53, 69 Interpersonal communication consequences comparison to victim, 29 reputation, 29 relationship between fear of crime and indirect victimization, 29 ‘Intervention’, 257 Intoxicated person, 168t, 171t, 191, 193t, 194, 223–224, 226, 242 Investigating fear of crime analysing fear-of-crime data activity diaries and daily routines, 85–86 advantages of spatial analyses, 81–82 beginning of fear mapping, 84–85 GIS, 86–87 spatial cognition and cognitive mapping, 82–84 defining fear of crime fear in relation to emotional reactions and stimuli, 69–70 fear is emotion, not cognition, 68–69 types of fear of crime, 71–72 measuring fear of crime behavioural approaches to, 76–79 improvements through affective approaches, 75–76 problems with cognitive approaches, 72–75 See also individual entries Irwin, J., 96–97, 100, 137–138, 149, 151, 251 J Jackson, J., 16, 60, 256 Jacobs, J., 19, 40, 57–58, 171t Jang, H., 261 Jankowski, P., 262 Jarman, P. J., 256

275 Jefferson, T., 2, 4, 32, 37, 144 Jeffery, C. R., 57, 71, 83 Jenkins, J., 69 Jiang, H., 262 Jochelson, R., 162 Jones, K. M., 10, 38–40, 78, 83–86, 105, 116, 145, 150, 171t, 191, 205, 256, 262 Joseph, J., 3–4, 11, 80, 143–144, 241 Judd, B., 19, 40, 76, 78, 81, 171t, 191 K Kanan, J. W., 213 Kaplan, S., 70, 83 Karakus, O., 3, 80, 260 Katz, C. M., 27–30, 35, 52, 73, 180, 201, 219–220 Keane, C., 4, 9–10 Kelling, G. L., 4, 11–15, 18–19, 34, 51–52, 55, 73, 87, 95, 112, 138, 146, 169, 235, 258 Kennedy, L. W., 10, 77, 105 Kenney, D. J., 52, 77 Keuleers, B., 139 Killias, M., 2, 28, 30, 73, 180 Kings Cross study background, 155–156 Community Safety Mapping Project, 155 goals of, 156 integrating fear mapping results addressing crime, 221–223 assessments of techniques/approaches, 240–242 targeting signs of disorder and incivility, 223–240 Kings Cross study, methods interviewing approach, 168 social/physical environmental cues in survey, 169t–170t spatial data visualization ‘collective-avoidance’ maps, 172f, 173 2D ‘avoidance hardness’ maps, 172f, 173 3D collective avoidance’ maps, 173 survey design and questions, 168–172 crime-specific avoidance-based question, 169 Kings Cross study, research setting background, historical cheap rents in late 1940s, 161 destination for visitors, 162 Queens Cross (now Kings Cross), 160 ‘red light’ district, 161 ‘the dirty half mile’, 161

276 Kings Cross study, research setting (cont.) crime, 162–164 inner-Sydney hotspot of assault and robbery, 162 selected criminal incidents recorded, 167t selected offences recorded by NSW police, 163t, 165t trends in selected offences, 166t demographic characteristics, 162 fear of crime, 164–168 factor triggering to feel unsafe, 168t geographic location Darlinghurst Road, 158f–159f fountain and Fitzroy Gardens, 159f Springfield Avenue, 160f street map of study site, 157f travel organizations advertisements, 156 Kings Cross study, results and discussion exploring reasons for fear of crime areas, drug users triggered fear during day, 209f, 216f areas, drug users triggered fear during night, 210f, 217f areas, sex workers triggered fear during day, 211f, 214f areas, sex workers triggered fear during night, 212f, 215f environmental cues triggering fear, 192–207, 193t fear between men and women, 213–218 fear between residents and visitors, 208 fear experienced by different groups, 206–207, 207t local knowledge to avoid specific areas, 208 mapping avoidance by selected groups, 207–220 mapping perceived presence of disorder/incivility, 194–195 men’s abnormally high fear, 220 perceived presence of areas to hide, 205–206, 206f perceived presence of drug users, 195–199, 196f perceived presence of gangs, 199–202, 200f perceived presence of sex workers, 202–205, 203f positive association, crime and fear, 190 presence of crime, 189–192

Index sites of robbery and steal-from-person incidents, 189f spatial mismatch between crime and fear, 190–192 visitors avoiding large areas, 210–213 women’s heightened fear, 218–220 people, afraid of crime degree of avoidance hardness, 178t indicating fear of crime, 178t scenario 1: fearful people choosing not to avoid, 179–180 scenario 2: fearless people avoiding areas, 180–181 people avoiding specific areas mapping, fear-of-crime hotspots, 181–182 safe areas and cognitive barriers, 187–188 sample characteristics, 173–177 age distribution of respondents, 175f distribution, experience of victimization, 176f 3D map cross-section, 174f Kings Cross study, techniques and approaches cognitive mapping, 241–242 survey and interviewing procedure, 240–241 Kirasic, K. C., 84 Kitchen, T., 58, 81 Kitchin, R. M., 82–84 Kleiman, M. B., 220 Knox, M., 162 Kohm, S. A., 257, 260, 262–263 Koskela, H., 3, 29, 40, 59–60, 83, 180, 191, 219–220 Kotler, B. P., 255 Kovecses, Z., 9 Krahn, H., 10, 77, 105 Krause, N., 36 Krivo, L. J., 18 Kubrin, C. E., 28, 180 Kuo, F. E., 40–41, 218 Kwan, M. -P., 86, 98, 105, 139, 145, 263 L LaGrange, R. L., 9, 67–69, 71–75, 80, 144, 171t, 213, 218, 220 Land, K. C., 12, 28, 38–39, 67–68, 71–73, 75–76 Lane, J., 28, 30, 34–36, 71, 202 Laneways, 160f, 168t, 171t, 193t, 194, 204, 225, 227–228, 230–231, 235, 237f Laub, J., 36, 73

Index Lawton, B. A., 51 Lee, H., 99 Lees, B. G., 11–12, 40, 78, 82, 146–147, 151, 168, 258 Lemanski, C., 81 Levine, N. A., 111 Levitt, S. D., 16, 18–19 Lewis, D. A., 29, 38–39, 69, 72, 77–78, 171t Lewis, M. J., 213, 218 Lianos, M., 32 Lighting, 17, 40, 58, 59t, 74, 137, 168t, 170t–171t, 185, 193t, 194, 201, 224, 227, 229–231, 235 Lindstrom, M., 36 Liska, A. E., 2, 9–10, 16, 18–19, 25, 27, 29, 73, 77, 133, 144, 188 Liu, J. H., 260 Liu, S. X., 261 Loitering people, 170t–171t, 193t, 194, 229 Longley, P. A., 51, 81, 86, 149 Loss of control, 30, 32, 205 See also Vulnerability Loukaitou-Sideris, A., 11, 41, 95, 135, 171t, 261 Ludwig, J., 257 Lumby, R., 160–161 Lupton, D., 3, 28, 31–32, 81 Lymes, D., 18, 60 M ‘Mall problem’ in Australia, 16–17, 251 Management of Public Spaces By laws 2009, 258 Managing fear of crime environmental design, 56–61 See also Environmental design policing, 51–56, 54t See also Policing fear of crime Markowitz, F. E., 15–16, 33–36, 98, 134, 258 Martin, D., 86 Mathieu, J. T., 67, 77 MAUP and ecological fallacy, effect of, 80–81, 81f Mawby, R. I., 17, 29, 32, 67, 73, 75 Maxfield, M. G., 9–10, 13, 15, 18, 26, 28–30, 36–37, 39, 52, 74–75, 77–79, 144, 171t Mayhew, P., 3, 80 McCoy, V. H., 30 McCullagh, M. J., 87 McDonnell, R. A., 86 McGrath, J. H., 218 McIntyre, J., 1–2

277 McLafferty, S. L., 80, 263 McLaughlin, T. F., 77 Measuring fear of crime affective approaches emotion-based measures, 75–76 improvements through, 75–76 behavioural approaches avoidance-based measures, 78–79 collective actions, 77 individual coping strategies, 77 protection-based measures, 76–78 self-protection, 76–77 socio-demographic groups, 78 problems with cognitive approaches global measures, approach and criticism, 73–75 value- or concern-based measures, 75 Media and fear of crime cultivation, 28 exposure, 28 interpersonal-diffusion, 28 resonance, 28 social comparison, 28 substitution, 28 Medically Supervised Injecting Centre, 156, 197 Meeker, J. W., 28, 30, 34–36, 71, 202 Mennis, J. L., 83 Merry, E. E., 35, 73 Mesch, G. S., 4, 9, 26, 30, 68, 72, 143 Miceli, R., 9, 25, 82 Michalos, A. C., 143 Millie, A., 11, 15, 38, 41, 53 Mirowsky, J., 10, 15, 19, 38–39, 60, 171t, 256 Mirrlees-Black, C., 3, 9, 38–39, 73, 79–80, 143, 213 Mismatch between crime and fear accounting for, 191–192 spatial mismatch, 190–191 Monmonier, M., 80 Moonlight, effect of, 255 Moran, L. J., 81 Morrall, P., 9, 255, 260 Mukherjee, S., 27, 80 Murray, A. T., 18, 87, 261 Mustaine, E. E., 77–78 N Nair, G., 4, 59, 74–75, 116, 137, 143, 201 Nasar, J. L., 9–10, 19, 38–41, 74, 77–78, 83–86, 88, 105, 116, 145, 150, 171, 181, 191, 205, 218, 241, 256, 261–262

278 Natural surveillance, 12, 19, 40, 57–58, 61, 87, 112–113, 136–137, 139, 147, 185, 221–222, 251 Negative emotional reactions, 67–68 Neighbourhood cohesion hypothesis, 36 Neill, W. J. V., 69 Nelson, A., 25, 81, 83, 87, 188, 218, 220 Newman, G., 262 Newman, O., 40, 57, 84 New South Wales Police, 53, 54t Nitzko, S., 254 Noaks, L., 258 Nold, C., 262 Northern Territory National Emergency Response Act (NTERA), 257 Novak, K. J., 52 NYPD zero-tolerance program of 1990s, 52 O Oatley, K., 69 OÇonner, M. E., 27, 213, 218–219 Oc, T., 4, 9–11, 16–17, 57–58, 60–61, 69–71, 84, 87, 95, 135, 145, 256 Offensive shops, 170t–171t, 193t, 194, 233–234 Olsen, E. P., 97, 134 Olsen, P., 86 Olson, D. R., 149 Order-maintenance policing, see Zero-tolerance policing Orleans, P., 83 Ortega, S. T., 30 O’Shea, T., 257 O’Sullivan, D., 80, 261, 263 P Pain, R. H., 3, 11, 29, 32, 59–60, 81, 83, 86, 180, 191, 213, 219–220, 235 Painter, K., 2, 19, 25, 30, 39–40, 58, 61, 188, 191 Pantazis, C., 10, 30, 37, 67, 73, 78, 133, 169 Paradoxical nature of fear of crime discrepancies between official crime data and surveys, 3 mismatch between fear and incidence of crime, 2 “Paradox of fear,” 2 Peak, K., 51–52 Pedestrian absence, 170t–171t, 193t, 194, 231 Perceived risk, 29–30, 40, 58, 68–69, 73, 75, 255

Index Perceptions of risk and threat of victimization, 68 ‘Perceptual mapping’, use of, 257 Perkins, D. D., 4, 12, 38–39, 87, 150, 171t, 204 Perloff, L. S., 30 Personal and altruistic fear, distinguishing, 71–72 Personal and housing crime, fear of, 15 Peterson, R. D., 18 Phillips, T., 38–39, 169, 235 Physical vulnerability, 30 Policing fear of crime, 54t case study: New York Police Department’s (NYPD) Policing Model, 55–56 community-based initiatives, 52–53 community-oriented or neighbourhood policing, 52 mobile citizen patrols, 52 NYPD zero-tolerance program of 1990s, 52 public cooperation with police, 53 SARA model (Scanning, Analysis, Response and Assessment), 51 zero-tolerance policing, 51–52 Polk, K., 3–4, 9, 16, 53, 60 Pollack, L. M., 57 Population turnover, 33–34, 37 Potas, I., 70–71 Poveda, T. G., 1–3 ‘Prescribed areas’, 257 President’s Commission on Crime and consultants from every part of America (PCLEAJ), 1–2, 146 Previous victimization, 43, 104, 169 Price, M. V., 255 Problem oriented policing, 51–53, 156 Professional Geographer, 146, 151 Protective actions, 18, 76–77, 181 Pruitt, M. V., 213 ‘Pub and club’ culture, 17, 109, 138, 150 Public antisocial behaviour, 33 See also Anti-social behavior Public perception of crime, 1, 52, 155, 204, 236 gangs, 201–202, 227 of security, 41 R Racial diversity, 35 Rader, N. E., 27 Rahman, T., 254 Ratcliffe, J. H., 87

Index Raudenbush, S. W., 11, 16, 95, 107, 109–110, 150 Reassurance policing, 53, 221 Recreational grouping, 139–140 Reid, L., 3, 9, 75, 77–78, 80, 87 Reiss, A. J., 33, 70 Rengert, G. F., 149 Residential status, 30, 169, 206–208, 207t Revill, G., 81, 235 Revitalization strategy City Centre Revitalization Strategy, 96, 146, 149, 251, 252f Laneways Revitalization Strategy, 227 Riger, S., 9, 144–145, 213, 218–219 Risk society hypothesis, 31–32 ‘I am afraid’, statement, 31 state of ‘dangerization’, 32 ‘unconscious’ anxieties, 32 Risk- victimization paradox, 87 Robinson, J. B., 39 Rogowitz, G. L., 255 Rohe, W. M., 171t Romer, D., 28, 180 Ross, C. E., 10, 15, 19, 38–39, 60, 171t, 256 Rountree, P. W., 12, 28, 38–39, 67–68, 71–73, 75–76 Rubbish, 59t, 170t, 193t, 194, 228 Rundown/abandoned buildings, 39, 170t–171t, 187, 193t, 194, 231–232 Russo, J., 87 S Sacco, V. F., 30 Safe cues, 41 ‘Safety elasticity of demand’, 17 Salem, G., 29, 69, 72 Salmi, S., 53 Sampson, R. J., 11, 16, 33–34, 95, 98, 107, 109–110, 150, 258 Samuels, R., 19, 40, 76, 78, 81, 171t, 191 SARA model (Scanning, Analysis, Response and Assessment), 51 Scarborough, B. K., 3 Schweitzer, J. H., 57–58 Scott, H., 75 Seriousness of consequences, see Vulnerability Sex workers, 39, 169, 171t, 193t, 194–195, 202–205, 207–208, 210, 212f, 214f–215f, 216, 219, 226, 234–235, 242–243 Sholl, M. J., 82

279 Signal crimes perspective, 27, 38, 41–43, 177, 199, 201, 223 effect of signal, 42 interpretations and effect, 43 semiotics and signs, 41 ‘signal crimes’ and ‘signal disorders’, 42 signal value, 42 situational relevance, 42–43 social semiotics and symbolic interactionist sociology, 41, 43 social signs, 42 strong signal crimes, 42 strong/weak signal crimes, 42 Signals, definition, 44 Sims, B., 51–53 Situational crime prevention, 51, 57, 261 Skogan, W. G., 9–16, 18–19, 26–30, 36–39, 52–53, 60, 67–69, 74–79, 81, 87, 95, 110, 138, 144, 146, 171t, 195, 202 Smith, C. L., 10, 40, 255 Smith, L. N., 11, 27 Smith, P., 38–39, 169, 235 Smith, S. J., 3–4, 10–11, 16, 25, 53, 79, 188, 220 Smith, W. R., 2–3, 75–76, 218–220 Snedker, K. A., 254 Snowball, L., 229 Snyder, M. G., 60 Social change hypotheses, 31, 35, 37 diversifying racial heterogeneity, 37 Social codes or ‘laws of morality’, 71 Social concept of crime, 71 See also Disorder/incivilities hypotheses Social control, 12–13, 14f, 16, 19, 33–34, 36, 38, 40–41, 43, 57, 221, 235 Social disorganization hypothesis, 32–35 characters of, 34 community concern hypothesis decline model, 36 neighbourhood, vitality/viability/ quality of, 36 criticism, 34 definition, 33–34 familial controls, breakdown in, 33 heterogeneity and rapid population, 33 social change hypothesis, 37 social control, concept of, 34–35 social disorganization, 33–34 social integration/neighbourhood cohesion hypotheses, 36 social mistrust, 33 subcultural diversity hypothesis, 35

280 Social disorganization hypothesis (cont.) fear-of-crime survey questions, 35 systemic social disorganisation model, examination of, 34 unsupervised youth, 33–34 Social incivilities, 39, 58, 235 Social integration, 31, 35–37, 134, 169 Social integration/neighbourhood cohesion hypotheses collective efficacy, 36 social capital, 36 social integration, definition, 36 social support, definition, 36 Social mistrust, 33 Social psychology, 3 Social signs ‘content’ and connotative description, 42 effect of a signal, 42 ‘expression’ and denotative description, 42 Social support, 36 Social theories explaining fear of crime, 31–37 risk society hypothesis, 31–32 social disorganization hypothesis, 32–35 community concern hypothesis, 36 social change hypothesis, 37 social integration/neighbourhood cohesion hypotheses, 36 subcultural diversity hypothesis, 35 Social vulnerability, 30, 219 See also Vulnerability ‘Soft’ crimes, 38 Soini, K., 260 Sparks, R., 32, 70 Spatial analyses, 79, 82, 220 advantages of, 81–82 ‘geodemographic’ analyses, 81 Spatial cognition and cognitive mapping, 82–84 definition, 83 mental state, 83 relevant ‘landmarks’, 82 spatial behaviour, 84 spatial choice, 83 Spelman, W., 9, 16, 19, 52 Spruikers, 158f, 164, 168t, 170–171, 193t, 194, 234 Stacey, W., 156, 160–161 Stafford, M. C., 72 Stanko, E. A., 32, 59–60, 67, 220 Stea, D., 82–84, 149, 191, 241 Stephens, D. W., 11–12, 38, 109, 241

Index Stevens, M., 258 Steventon, G., 57 Stimson, R. J., 82–83, 85–86, 105, 139, 145 Stinchcombe, A. L., 30 Strange, W. C., 18, 60 Subcultural diversity hypothesis, 31, 35, 71 fear-of-crime survey questions, 35 Subject of victimization (personal or altruistic), 71 Sullivan, W. C., 40–41, 218 Sundeen, R. A., 67, 77 Sun, I. Y., 33–34 Sutherland, E. H., 70 Symbolic interactionism/interactionist sociology, 25, 43 Symbols associated with crime, 67, 69 Syringes, 107t, 170t–171t, 193t, 194–195, 198, 204, 228, 236 T Taxonomy of crime perceptions, 68t Taylor, H. A., 83, 191 Taylor, R. B., 2–4, 11–13, 25–26, 29–31, 33–39, 73, 87, 95, 104, 150, 171t, 188, 258 ‘Territoriality’, concept of, 14, 53, 57–58, 187 Teske, R. H. C. J., 10, 18, 145 Tewksbury, R., 76, 78 Thill, J. C., 261 Thomas, C. W., 2–3, 11, 17, 61, 80, 86, 109, 138 Thompson, E. E., 35–36 Thompson, T., 156, 160–162 Threatening and safe environments theories, 40–41 control signals, definition, 41 fear reducing features, 41 ‘likeable features’, decrease the fear, 41 safe environments, 41 threatening physical/social environments, 40–41 time of day, 40 Tiesdell, S., 4, 9–11, 16–17, 57–58, 60–61, 69–71, 84, 87, 95, 135, 145, 256 Tortensson, M., 2–3, 75–76, 218–220 Toseland, R. W., 77, 82, 213, 218–219 Trends in fear of crime research, 3–4 “enduring frustration” for policy makers, 4 Tulloch, J., 3–4, 25, 28, 32, 52, 67, 69, 74–76, 81, 144, 164, 195 Tulloch, M., 3, 38–39, 76 Tversky, B., 83, 191

Index U Unsupervised youth, 33–34 Unwin, D. J., 80, 263 V Vacha, E. F., 77 Valentine, G., 3, 83, 180, 220 Van Beek, I., 161, 197–198 Vandalism, 59t, 77, 107, 117, 124f, 164, 170t–171t, 193t, 194, 233 Van der Wuff, A., 3 Vanetti, E. J., 84 Victimization, 2, 4, 10, 15, 18, 25–32, 37, 40, 43–44, 51, 68–69, 71–73, 78, 87, 104, 117, 118f, 144, 169, 175, 176f, 180, 201–202, 213, 218–220, 227, 256–257 Victimization hypothesis, 26–27 blame, 26 direct experience and fear, relationship, 26 direct victimization, 26 fear neutralization techniques acceptance of responsibility or denial of future vulnerability, 27 denial of injury or damage, 27 personal (direct) victimization, impact of, 27 ‘theories of reality’, stages of emotional loss, 26 type of victimization (personal or property), 71 victimization and fear, positive/negative association, 26–27 victimization, influence of, 26–27 Vignettes, 103, 105, 118, 119t Violation of criminal law, 70–71 Vrij, A., 220 Vulnerability factors, 30 feelings of, 26 hypothesis, 29–30 physical vulnerability, 30 purist theorists, 30 social vulnerability, 30 trends, 30 W Wagner, A. E., 58 Walker, M. A., 73, 144 Walklate, S., 4, 25, 32, 52, 144 Walmsley, D. J., 260 Walsh, D. A., 70–71, 84, 149

281 Warr, M., 2–3, 9, 11, 15, 17, 19, 26, 29, 67–72, 76–77, 80–81, 87, 135, 145–146, 179, 219–220, 254 Weatherburn, D., 18 Webb, J., 34 Weitzer, R., 28, 180 Wets, G., 139 Whitaker, A. M., 160–162 White, P., 3, 80 Wikström, P. H., 60–61, 110 Williams, D., 254 Williams, P., 28, 69–70 Will, J. A., 218 Wilson-Doenges, G., 3, 9–10, 60, 80 Wilson, J. Q., 11–14, 19, 34, 38, 51–52, 55, 73, 87, 95, 138, 146 Winkel, F. W., 220 Wolfer, L., 52–53, 86 Wollongong City Council, 96–97, 100, 106, 138–139, 147, 149 Wollongong study goals of, 95–96 industrial city, background, 97–98, 97f–98f location of Wollongong, 96f Wollongong study, methods combinatory spatial analysis, 112–115 expansion and linking hotspots, 114f expansion of hotspot in relation to collective avoidance concentration, 113f expansion of hotspot in relation to partially overlapping collective avoidance concentration, 114f disorder assessment graffiti in Crown Lane/MacCabe Park, example, 108f physical disorder, 107–109 social disorder, 109–110 types of physical disorder, 107t types of social disorder, 110t fear-of-crime survey and analysis cognitive mapping of avoidance behaviour, 105–106 combinatory process to collate individual avoidance grids, 106f factors known to influence, 104 GIS-based technique, collating cognitive maps of avoidance, 106–107 questions on emotional levels and protective behaviour, 105 vignettes, emotional levels of fear in hypothetical situations, 105

282 Wollongong study, methods (cont.) spatial analysis of crime data, 110–112 types of venues with social disorder, 110t typical view, Crown and Keira streets, 112f typical view, Crown Street Mall area, 111, 111f Wollongong study, research setting Central Business District (CBD), 99–101, 99f public seating area in Crown Street Mall, 100f–101f structure plan, 100 crime and fear of crime, 101–103, 102t crime hotspots at LGA Level in NSW (2002), 102–103 crime trends in Illawarra Region (2001), 101–102 discussion of spatial outputs, see Wollongong study, spatial outputs integrating findings with police/community initiatives, 145–149 CCTV program – action table, 148, 148f Crime Prevention and Community Safety Plan, 146 Don’t stall in the mall – action table, 147, 148f spatiotemporal overlap between broken windows thesis and institutional involvement, 146t logic behind study site selection, 98 high rates of crime, 98 techniques and approaches, assessments, 149–151 Wollongong study, results, 115–133 distribution of collective avoidance concentrations, 119–122 areas of CBD avoided in relation to fear of crime, 121f–122f respondents adopting avoidance behaviour in neighbourhoods/CBD area, 120t responses to question regarding police, 120f overlap between areas of avoidance and crime hotspots, 133f and social disorder hotspots for weekdays/weekends, 135f–136f and weighted physical disorder hotspots, 134f

Index overlap between collective avoidance concentrations, physical and social disorder, 129–133, 134f overlap with elements of disorder and crime, 136t sample characteristics, 115–119, 115f answers to global measure of fear amongst respondents, 119f experience of victimization among respondents, 118f income distribution of respondents, 116f media perception of crime-related issues among respondents, 117f overlap between collective avoidance concentrations, physical/social disorder, 129–133 safety in relation to vignettes, 119t spatiotemporal distribution of physical/social disorder/crime, 122–129 areas of CBD avoided after 19:00, 123f crime hotspots within CBD, 132f hotspots of social disorder on weekdays/weekends, 127f–128f hotspots of social disorder on weekends, 128f number of clubs/bars/nightclubs in CBD area, 131f physical disorder hotspots, based upon weighted data, 124f ranking of different types of disorder, 124f social disorder hotspots, bars/clubs/adult entertainment stores, 131f social disorder on weekdays at day/night, 125f–126f social disorder on weekends at day/night, 126f–127f types of social disorder on weekdays/weekends, 129f–130f Wollongong study, spatial outputs activity diary analysis, 143–145 categories from classification procedure, 140t coding system for protective behaviours, 141t data preparation, 139–141 diary data, 140t emotional levels of fear/ protective behaviour, 140–141, 142t mandatory activities, 145

Index results of, 143 structural constraints and role obligations, 144 constraints on social interaction from collective avoidance behaviour Crown Street Mall Area, 138–139 MacCabe Park Area, 135–138 Piccadilly Area, 133–135 Women’s fear of crime, 59 Wood, T. H. J. J., 201 Wright, S. M., 256 Wu, Y. N., 255 X Xu, Y., 12, 51, 53

283 Y Yarwood, R., 82, 87 Yigitcanlar, T., 261 Yin, P. P., 67 Yokohari, M., 41 Young, M., 258, 263 Z Zadel, S., 161 Zero tolerance policing, 51, 55, 146 community oriented policing, 51–53, 56 problem oriented policing, 51–53, 156 reassurance policing, 53, 221 Zhang, L. N., 3, 260 Zhu, X. A., 261 Zumbo, B. D., 143