Modelling Crime: A Spatial Microsimulation Approach Charatdao Kongmuang Submitted in accordance with the requirements for the degree of Doctor of Philosophy The University of Leeds School of Geography September 2006 The candidate confirms that the work submitted is her own and that appropriate credit has been given where reference has been made to the work of others. This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. i Academic Acknowledgements a. The 2001 Census statistics used in this thesis are Crown Copyright produced by the Office for National Statistics (ONS). Licensed for academic use by the ESRC/JISC Census Programme, which funded access to the data for researchers in the UK, free at the point of use. The ESRC/JISC Census Programme funds the Data Support Units which provide access to UK Census Data. The 2001 Census Area Statistics are provided by the Census Dissemination Unit (CDU) through the Manchester Information and Associated Services (MIMAS) of Manchester Computing, University of Manchester through an interface called CASWEB. b. All maps are based on data provided by the United Kingdom Boundary Outline and Reference Database for Education and Research Study (UKBORDERS) via Edinburgh University Data Library (EDINA) with the support of the Economic and Social Research Council (ESRC) and the Joint Information Systems Committee (JISC) and boundary material which is copyright of the Crown, Post Office and the EDLINE consortium. c. The 2001/2002 British Crime Survey, material from Crown Copyright records made available through the Home Office and the UK Data Archive has been used by permission of the Controller of Her Majesty’s Stationery Office and the Queen’s Printer for Scotland. d. The recorded crime datasets (2000/01, 2001/02, 2002/03, and 2003/04) and known offender dataset (2000-2004) are provided by West Yorkshire Police. e. Gipton and Harehills NRAs boundaries are in dBase and Shapefiles formats provided by Leeds Statistics, Leeds City Council’s Neighbourhood and Housing Department. All Neighbourhood Renewal Areas maps are based upon Ordnance Survey material with permission of Ordnance Survey on behalf of the Controller of Her Majesty's Stationery Office. ii Acknowledgements This research would not have been possible without the help, support and advice of many people whom I am extremely grateful. First and foremost, I would like to express my gratitude to my supervisors, Prof. Graham Clarke and Dr. Andy Evans for working so hard on reading my thesis and appreciating what this thesis is all about. Their advice, guidance, and comments have been invaluable and helped me to complete this research. Thanks to my research support group, Dr. Dimitris Ballas and Dr. Debbie Phillips for their advice. In particular, thanks to Dimitris for helping me to build on his spatial microsimulation work and other suggestions at various stages. I would also like to express my gratitude to Prof. Phil Rees not only for examining this work, but also for his kindness, comments, and suggestions which were also of great value. I would like to acknowledge the funding that was provided by Naresuan University, Thailand. Without such generous financial support my PhD would not have been possible. Thanks P’Bid, Assoc. Prof. Kanchalee Jetiyanon, for her long-distance support and caring. Thanks Nikki for sharing lots of experiences when we were in the UK. It is very hard to individually acknowledge everyone who has helped me to reach this point. However, I would like to thank all the people who have directly or indirectly helped me in completing this thesis. Firstly, thanks Pete, Dan, Alison, Dianna, and Andy Turner for your very helpful comments at various stages. Thanks Jin for sharing your programming expertise. Secondly, thank to those with whom I have shared the last four years: Dear Thai friends, you are too many to list here, but you know who you are. Thank you my other friends at the School of Geography for providing an inspiring and friendly atmosphere. A further special thanks to Jin, my very best friend. Your help, care, support, company and patience over the past two and a half years has been wonderful. Our friendship will never be forgotten I promise. Last, but not least, I would like to thank my family for their love, support, and encouragement that create whom I am. They have always been there when I needed them. People always ask me why I am doing a research on crime. My dad, Police Major General Sanam Kongmuang, is my inspiration. This thesis is the result of a four-year long journey in Leeds, a chapter of my life which I will always remember with a smile. iii Abstract Spatial microsimulation offers a potentially powerful framework for modelling crime at small area levels. It is more powerful than traditional crime analysis in that it can make policy-centred predictions. This thesis presents SimCrime; a spatial microsimulation model for modelling crime and an analysis using this software at the ward level in Leeds. The model is based on the UK 2001 Census and the 2001/2002 British Crime Survey. The model effectively adds ‘geography’ to the British Crime Survey data (which is not currently released below the national level). Adding geo-references into the British Crime Survey makes it more valuable, with the spatial aspect of the data enabling an analysis of the geographical variations of factors of interest to policy-makers at a range of scales. Within this innovative framework, victims, offenders and locations are examined. The results show the geographical distribution of the likelihood of being a victim of burglary dwelling, the risks for different types of household and the reporting rate of each crime type. The addition of a spatial interaction model allows for the analysis and prediction of offender flows, and in combination with the microsimulation of victims, the complete framework thus provides a predictive capacity which can be used to inform policy making. iv Table of Contents Academic Acknowledgements………………………………………………………………..i Acknowledgements…………………………………………………………………………...ii Abstract………………………………………………………………………………………iii Table of Contents…………………………………………………………………………….iv List of Figures……………………………………………………………………………….vii List of Tables…………………………………………………………………………………x Abbreviations………………………………………………………………………………..xii Chapter 1: Introduction…….…………………………………………………………….... 1 1.1 Background………………………………………………………………………......... 1 1.2 Aims and Objectives of the Research…………………………………………………. 3 1.3 Thesis Structure ……………………………………………………………………... 4 Chapter 2: Geography of Crime and Modelling Crime: A Literature Review …………8 2.1 Introduction……………………………………………………….……………………. 8 2.2 The Geographical Approaches to Crime………………………………………………. 8 2.3 Crime Attributes……………………………………………………………………… 11 2.3.1 Demographic Characteristics………………………………………………... 11 2.3.2 Socio-economic Characteristics……………………………………………... 12 2.3.2.1 Employment………………………………………………………… 12 2.3.2.2 Tenure Type……………………………………………………….... 13 2.3.2.3 Poverty and Deprivation………………………………………….… 13 2.3.3 Neighbourhood/Area Characteristics and Offence Locations...…………...... 15 2.4 Movement of Offenders: Journey to Crime………………………………………….. 17 2.5 Crime Victimisation………………………………………………………………….. 18 2.6 Modelling Crime……………………………………………………………………... 20 2.6.1 Multivariate Regression Model…………………………………………….... 20 2.6.2 Poisson and Negative Binomial Regression Model…………………………. 25 2.6.3 Logistic Regression Model………………………………………………..…. 26 2.7 Concluding Comments………………………………………………………...…....... 29 Chapter 3: Microsimulation Modelling: A Literature Review……………………….… 31 3.1 Microsimulation: An Introduction……………………………………………....…… 31 3.2 Types of Microsimulation…………………………………………………………..... 31 3.2.1 Static and Dynamic……………………………………………………….…. 32 3.2.2 Spatial and Aspatial………………………………………………….……… 34 3.3 A Review of Selected Microsimulation Models…………………………………….. 35 3.4 Advantage and Disadvantages………………………………………..………….…... 40 3.5 The Creation of Synthetic Microdata……………………………………………..….. 41 3.5.1 Synthetic Reconstruction…………………………………………………..... 41 3.5.2 Combinatorial Optimisation…………………………………….…………... 42 3.6 Combinatorial Optimisation using Simulated Annealing Method…………………… 45 3.7 Concluding Comments…………………………………………………….……….… 48 Chapter 4: Modelling Crime: Data Sources and Issues…………………………...……. 49 4.1 Introduction……………………………………………………………..…….….…... 49 4.2 Data Sources and Issues……………………………………………………..…….…. 49 4.2.1 The 2001 Census………………………………………………………..…… 49 4.2.2 The 2001/2002 British Crime Survey…………………….………...………... 55 4.2.3 Police Recorded Crime Datasets……………………………………..……....60 4.2.4 Offender Dataset………………………………………………………….…. 63 4.3 Concluding Comments……………………………………………………………….. 64 v Chapter 5: Geography and Determinants of Crime in Leeds………………………….. 66 5.1 Introduction…………………………………………………………………..….…… 66 5.2 Leeds Crime Figures and Trends…………………………………………….….….. 67 5.3 Geographical Variations………………………………………………………………73 5.3.1 Police Divisions………………………………………………………...…… 73 5.3.2 Wards…………………………………………………………………...…… 78 5.4 Findings by Crime Type……………………………………………………………… 80 5.4.1 Burglary Dwelling……………………………………………………………80 5.4.2 Burglary Elsewhere………………………………………………………….. 81 5.4.3 Criminal Damage……………………………………………………………. 81 5.4.4 Drug Offences……………………………………………………………….. 82 5.4.5 Fraud and Forgery…………………………………………………………… 82 5.4.6 Handling……………………………………………………………………... 83 5.4.7 Homicide…………………………………………………………………….