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Testing a Geospatial Predictive Policing Strategy

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Testing a Geospatial Predictive Policing Strategy TESTING A GEOSPATIAL PREDICTIVE POLICING STRATEGY: APPLICATION OF ARCGIS 3D ANALYST TOOLS FOR FORECASTING COMMISSION OF RESIDENTIAL BURGLARIES By SOLMAZ AMIRI A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF DESIGN WASHINGTON STATE UNIVERSITY School of Design and Construction DECEMBER 2014 © Copyright by SOLMAZ AMIRI, 2014 All Rights Reserved © Copyright by SOLMAZ AMIRI, 2014 All Rights Reserved To the Faculty of Washington State University: The members of the Committee appointed to examine the dissertation/thesis of SOLMAZ AMIRI find it satisfactory and recommend that it be accepted. ___________________________________ Kerry R Brooks, Ph.D., Chair ___________________________________ Bryan Vila, Ph.D. ___________________________________ Kenn Daratha, Ph.D. ___________________________________ David Wang, Ph.D. ii ACKNOWLEDGMENTS I would like to thank my committee for their guidance, understanding, patience and support during my studies at Washington State University. I would like to gratefully and sincerely thank Dr. Kerry Brooks for accepting to direct this dissertation. Dr. Brooks introduced me to the fields of geographic information systems and scientific studies of cities, and helped me with every aspect of my research. He asked me questions to help me think harder, spent endless time reviewing and proofreading my papers and supported me during the difficult times in my research. Without his encouragements, continuous guidance and insight, I could not have finished this dissertation. I am grateful to Dr. Bryan Vila for joining my committee. Dr. Vila put a great deal of time to help me understand ecology of crime and environmental criminology. I showed up at his office without having scheduled a prior appointment, and he was always willing to help. He also informed and helped me apply for the Graduate Research Fellowship Program of the National Institute of Justice. I would like to thank Dr. Kenn Daratha for serving on my committee. Dr. Daratha helped me expand my knowledge on univariate and multivariate statistics. Dr. Daratha also contributed valuable insight to my research. I am grateful to Dr. David Wang for being on my committee since the early stages of this project and encouraging me to conduct research on a subject that is significant and interests me. I iii would also like to thank Dr. Janetta McCoy, Dr. Bob Scarfo and Dr. Celestina Barbosa-Leiker for supporting this research and my studies at WSU. This dissertation was partially funded by the Graduate Research Fellowship Program of the National Institute of Justice (Grant No. 2013-IJ-CX-0044). I also received additional support from NIJ to present my research at the International Association of Crime Analysts Conference (IACA) in years 2013 and 2014. I am grateful to NIJ for supporting my research. I would also like to thank Ms. Natasha Kenon and Mr. Joel Hunt for their help with administrative and technical requirements of this grant. This research would have not been feasible without Spokane Police Command Staff granting me access to the Spokane Police Department burglary reports. I would like to thank Assist. Chief Stephens, Major Scalise and Capt. Carl for granting this permission. I would also like to sincerely thank Mr. Ryan Shaw, Mr. Thomas Michaud and Dr. Benjamin Krauss in SPD for their help with retrieving and understanding burglary crime data. I am grateful to Mr. Ian von Essen, the Spokane County GIS Manager, for granting me access to Pictometry Imagery. I would also like to thank Ms. Shawna Ernst and Mr. Joel Edgar for helping me access and utilize Pictometry for my research. I would like to thank Prof. Blossom for offering me graduate assistantship at the Interdisciplinary Design Institute and School of Design and Construction for three years. I would also like to thank Dr. Julie Johnson, Ms. Pam Medley, Ms. Kristie Clark-brink, Ms. Jaime Rice, Ms. Liz West and Ms. Cheryl Scott for their enormous help during my studies at WSU. I also iv received enormous support from Mr. Bob Pringle, Ms. Mary Wood and Ms. Dee Rodgers of the WSU Spokane library for conducting this research. I would like to acknowledge the help and support of my classmates and friends at Washington State University and beyond. I would specifically like to thank Ms. Tammy Parker and Dr. Amber Joplin for supporting me complete this work. Special thanks goes to Ms. Leslie Johnson, Mr. Steve Johnson and Mr. Butch Johnson for their enormous help and support since I step foot in Spokane. I would like to thank my parents, my brothers, my grandmothers and family for their unconditional love and support. Without their encouragement, I could have never achieved what I have. I am grateful to have you all in my life. … I believe spatial design and configurations influence health and safety. Thus, I would like to thank “institutions, agencies and organizations that initiate, support and value design research programs.” v TESTING A GEOSPATIAL PREDICTIVE POLICING STRATEGY: APPLICATION OF ARCGIS 3D ANALYST TOOLS FOR FORECASTING COMMISSION OF RESIDENTIAL BURGLARIES ABSTRACT by Solmaz Amiri, D.Des. Washington State University December 2014 Chair: Kerry Brooks Classical placed-based crime prevention theories suggest existence of a relationship between certain characteristics of spatial design and configuration and crime occurrence. This study explored the relationship between natural surveillance – one of the least studied and understood principles of crime prevention through environmental design (CPTED) – and burglary commissions in three-dimensions. Natural surveillance has been claimed to differ when seen by neighbors, pedestrian passersby or individuals in vehicles, and to be influenced by viewing distance. Thus, the notion of natural surveillance was quantified to three categories of occupant, road and pedestrian surveillability. In addition, length of sightlines were restricted by the distance at which human eye is considered effective to eyewitness and interpret events. Employing a mixed methods research design, qualitative data (sketches made from oblique aerial imagery, field observations of architectural and landscape features, burglary crime reports and field observations of crime sites) were embedded and provided a supportive role for vi the quantitative data (georeferenced spatial and crime data) and quantitative analysis (univariate and multivariate statistical analysis). Firstly, ArcGIS geospatial tools were utilized for processing spatial and crime data in three-dimensions. Then, ESRI ModelBuilder was employed for automating the procedure of enumerating natural surveillance intensity. Spearman’s rank correlation, Mann-Whitney U and binary logistic regression were employed to investigate the univariate and multivariate association between natural surveillance and burglary commissions or burglary occurrence. The results at the building opening level revealed that the log of the odds of burglary commission was negatively related to occupant surveillability and positively related to road surveillability. Findings at the building level showed that the log of the odds of residential burglary occurrence was positively related to road surveillability. This research shed light on the importance of the notion of “eyes upon the street” (Jacobs, 1961) even in a low socioeconomic-high criminogenic area. It has implications for developing proactive design and planning policies to help design crime out at the early stages of planning and development. It also demonstrates how law enforcement can further leverage societal investments in geospatial data to benefit public safety more effectively. KEYWORDS: Eyes upon the Street, Geographic Information Systems (GIS), Natural Surveillance, Residential Burglary, 3D, Line-of-sight. vii AppendixTABLE OF A CONTENTS ACKNOWLEDGMENTS ............................................................................................................. iii ABSTRACT ................................................................................................................................... vi LIST OF FIGURES ................................................................................................................... xviii LIST OF TABLES ..................................................................................................................... xxiv 1 INTRODUCTION ........................................................................................................................1 1.1 Introduction ........................................................................................................................1 1.2 Statement of the Problem ...................................................................................................3 1.3 Statement of Purpose ..........................................................................................................4 1.4 Research Questions ............................................................................................................6 1.5 Hypotheses .........................................................................................................................6 1.6 Expected Outcomes ............................................................................................................7 1.7 Summary of Chapters .........................................................................................................8 2 LITERATURE REVIEW ...........................................................................................................10
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