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SFU Thesis Template Files “On to the next one:” Using social network data to inform police target prioritization by Sadaf Hashimi B.A. (Honours with distinction, Criminology), Simon Fraser University, 2013 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Arts in the School of Criminology Faculty of Arts and Social Sciences Sadaf Hashimi 2015 SIMON FRASER UNIVERSITY Summer 2015 Approval Name: Sadaf Hashimi Degree: Master of Arts (Criminology) Title: “On to the next one:” Using social network data to inform police target prioritization Examining Committee: Chair: Sheri Fabian, PhD Senior Lecturer, School of Criminology Martin Bouchard, PhD Senior Supervisor Associate Professor School of Criminology Eric Beauregard, PhD Supervisor Professor School of Criminology Robin S. Engel, PhD External Examiner Professor School of Criminal Justice University of Cincinnati Date Defended: August 5, 2015 ii Abstract As part of the portfolio of strategies used to achieve crime reductions, law enforcement agencies routinely establish a list of offenders to be targeted as priorities. Rarely considered, however, is the fact that targets are embedded in larger social networks. These networks are a rich resource to be exploited as they facilitate: 1) efficient prioritization by understanding which offenders have access to more resources in the network, and 2) assessments of the impact of intervention strategies. Drawing from law enforcement data, the personal networks of two mutually connected police targets from a mid-size city in British Columbia, Canada were constructed. Results show that of the 101 associates in their combined network, 50 percent have a crime-affiliated attribute. The network further divides into seven distinct communities, ranging from four to 25 members. Membership to these communities suggests how opportunities, criminal and non-criminal, form and are more likely to occur within one’s immediate network of associates as opposed to the larger network. As such, seven key players that have the highest propensity to facilitate crime-like behaviours are identified via a measure of “network capital,” and located within the communities for informed target selection. Keywords: Social network analysis; intelligence-led policing; law enforcement priorities; key players; deterrence; community structure iii Dedication To my constants, my rocks, who have been through it all with me: the good, the bad, and the uncertainties. I am grateful for your support, patience, and unconditional love. Thank you for teaching me the importance of balance while allowing me to get lost in the process. You are my irreplaceables and I owe everything to you. Love and respect, always xxx iv Acknowledgements “Everyone you will ever meet knows something you don’t” – Bill Nye This journey, my journey, necessitates a million thanks (to say the least) to several “key players”. The first of it belonging to my senior supervisor, Dr. Martin Bouchard. MB, seven years and counting, you started it all! Thank you for the guidance, the patience (so much patience) and for sharing your genius. Your investment in this project, and our partnership was (is) unprecedented, and I can only hope to pay a fraction of it forward. Over the years, we haven’t always taken the easiest paths, but you continued to inspire, provided the opportunities and really trusted me (and my instincts) to be better than the day before. I could not have asked for a better mentor to have learned from, to have laughed at the craziness of it with and to have engaged in all the real talk – it truly was multiplex. Thank you for never being basic B, it was an honour to work with you - OTTNO? I would also like to thank my committee members, Professor Eric Beauregard, and Professor Robin Engel. Professor Beauregard - from my first semester in the program, you had a way of calming the waves of uncertainty, and making everything seem okay. Thank you for the continuous support and the conversations that went beyond my bubble. To my external examiner, Professor Robin Engel, thank you for your insightful comments and suggestions. CIRV was an inspiration for this project and your contribution helped bring this journey to a full circle. Finally, thank you Dr. Sheri Fabian for chairing my defence. You have been a constant source of support through the years. Thank you for the words of encouragement, and that boost of confidence when it was needed the most. To my favourite RCMP detachment: Without your trust in us and enthusiasm in the work, this project would not have materialized. To everyone I met during the last two years: Superintendent, S/Sgts, Operations Support Officers, general duty officers and the analysts, thank you for being so welcoming, you made it feel like my second home during those late (late) nights, and early mornings. Thank you for your support (even when it did not make sense right away), fascination in the work (“what do you do?”), for never being more than an email away, for always answering my questions and/or directing me in the right path. I will never forget those ride-alongs and the conversations in between. Thank you for the experience of it all, I look forward to the continued partnership. v My SFU community: SL, my academic soulmate! Thank you for always being there (open door policy, literally) and for reading my mind before I said anything. You were the best sounding board and source of reinforcement. MO, best office mate ever! Thank you for sharing the adventures (danger? never), and for going through the ups and the downs with me. Where to next? To my “cohort” (+/- a few years) thanks for the discussions, and the support. This journey would not have been nearly as enjoyable without seeing your beautiful faces all day, every day! To the faculty at SFU, my teachers, it was a privilege to learn from you during my undergraduate and graduate years. My family, my blood: There are no words to describe the love, appreciation and admiration I have for each and every one of you. I am blessed with the people in my life. Thank you for teaching me the importance of kindness, the value of relationships and for helping me become a better, stronger version of myself each and every day. Above all - thank you for showing me the purpose of life: be good, do good, and the rest will take care of itself. This research was supported by the Canada Graduate Scholarships – Master’s Program (CGS-M). vi Table of Contents Approval .......................................................................................................................... ii Abstract .......................................................................................................................... iii Dedication ...................................................................................................................... iv Acknowledgements ......................................................................................................... v Table of Contents .......................................................................................................... vii List of Tables .................................................................................................................. ix List of Figures.................................................................................................................. x Introduction ............................................................................................. 1 Theory and literature review ................................................................... 4 Embeddedness in social and criminal networks ...................................................... 4 The emergence of communities and groups within networks ..................... 8 Applying social networks to law enforcement data ................................................ 10 Target selection and network prioritization ............................................................ 15 Identifying key players ............................................................................. 15 From key players to network capital: Using multidimensional data to go beyond centrality ............................................................................. 18 The current study .................................................................................................. 19 Data and methods ................................................................................. 21 Data source .......................................................................................................... 21 Network extractions: Associates and ties .............................................................. 23 Associate attributes .............................................................................................. 27 Network measurements ........................................................................................ 27 Multiplexity and the duality of roles ....................................................................... 29 Determining key players: Measuring network capital ............................................ 32 Associate severity .................................................................................... 33 Associate connectivity ............................................................................. 34 Community analysis ............................................................................................. 35 Analytic strategy ..................................................................................................
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