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Associations Between Greenspace and Street Crimes in Toronto: Evidence from a Spatial Analysis Study at Dissemination Area Level

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Associations Between Greenspace and Street Crimes in Toronto: Evidence from a Spatial Analysis Study at Dissemination Area Level Associations between Greenspace and street Crimes in Toronto: Evidence from a spatial analysis study at dissemination area level by Odunayo Onifade A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Science in Geography Waterloo, Ontario, Canada, 2020 © Odunayo Onifade 2020 Author’s Declaration I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Introduction: Earlier criminologists have explored various factors generating or attracting crime in urban cities coupled with crime studies focusing on the influence of social, built and natural environments in urban centres. According to Statistics Canada (2019), the Crime severity index of Canada and Toronto has been on the rise since 2014, which found the violent crime severity index showing higher trends than non-violent crime severity. This study, first, examined the crime trends and seasonality in Toronto. Next, the association between greenspace variables and street crime rates across the city at the dissemination level using the spatial statistical methods were explored. Previous crime studies have also investigated the relationship between the crime rate (property and violent) and greenspace, albeit this study only focused on analyzing crime that usually occurs outsides, namely “street crimes.” There are two schools of thought concerning the association between crime rates and greenspace. The first belief suggests greenspace facilitates criminal activities because it conceals the offender from the victims/bystanders, while the second belief insists that greenspace deter criminal activities. Methods: Street crime considered for this research included assault, auto-theft and robbery crime. This study explored the association between greenspace variables and street crime rates across the City of Toronto. Crime data were extracted from the Toronto Police Service public safety data portal; the greenspace data were obtained from Toronto Open Data, and sociodemographic data were exported from the 2016 Census Data collected from Statistic Canada. Street crime trends and seasonality were first, and they were carried out using crime data from the year 2014 to 2018 to generate a line graph depicting yearly, monthly and seasonal crime trends in the study area. Greenspace variables (stem density; basal area density and tree density) were estimated from tree inventory data. The sociodemographic variables considered were median household income, lone iii parent, unemployment rate, high school degree holders, owner-occupied housing and renter- occupied housing. Spatial distribution maps for the dependent and independent variables were generated to show the geographical variation of the data. The Global Moran’s I and Local Indicator of Spatial Association (LISA) statistics were carried out on the street crime data to detect the spatial autocorrelation and clustering in the dependent variables. The spatial regression analyses were then carried out using the spatial lag model and the spatial error model on street crime rates, greenspace and sociodemographic variables. Results: There were changing crime trends and seasonal variation of the three-street crime occurrences. Consequently, the street crime rates indicated spatial clustering with the locations of hot and cold spots for assault and robbery crime rates similar. In contrast, auto-theft crime rates emerge in different locations across the City of Toronto. Results from the spatial regression analyses show that the stem density and tree density are negatively associated with street crime rates after controlling for specific sociodemographic factors. Also, the basal area density was not significant in the spatial regression analyses on street crime rates. The six sociodemographic indicators (median household income, unemployment rate, lone parent, high school degree holders, housing units occupied by owners and renters) were significantly associated with the three street crime rates in this current study. Conclusion: This thesis contributes to the existing literature by using a spatial-statistical approach to estimate greenspace variables and explored their relationship with street crime rates. This study draws attention to the use of specific sociodemographic factors with street crime types, and the influence parts of a tree (greenspace) could have on street crime rates across the City of Toronto. Limitations of the data were discussed, future studies concerning the recommendation of different tree species and the influence of weather on greenspace were discussed. iv Acknowledgement I would like to start by acknowledging my supportive supervisor Dr. Jane Law for her patience and encouragement throughout my master’s program at the University of Waterloo. Thank you so much for your guidance, time, dedication, resources, inspiration and understanding during my master’s program. Your words will forever echo in my ears that “thesis is like cooking; you have to gather all the ingredients before cooking the food.” I am grateful to Su-Yin Tan and Peter Deadman for willing to accept to serve on my research committee. My gratitude goes to my big brother Olufemi Onifade; you have been my rock and motivator since day one. Thank you, Egbon. I will like to appreciate my Aunt Foluke Ogunro and Cousin Ayodele Ogunro, for their support since I started my master’s program. Above all, I want to give glory to God for seeing me through my program. Thank you, God. I am also grateful to my friends, lecturers and Profs: Oladotun Alade, Pelumi Onarinde, Linda Iheme, Victor Agbo, Ngone Gaye, Babatunde Adubiobi, Mr & Mrs. Adesola Adesoji, Olukayode Awe, Oluwafemi Safe, Max Salman, Toluwalope Bashorun, Jaydeep Mistry, Neomi Jayaratne, Manpreet Singh, Ernest Agyemang Duah, Remi, Bernard, Adewumi Adepoju for being there through the good and hard times. I’m grateful to Prof. Brent Doberstein (for giving me my first winter jacket), Mike Belfry, Yu Huang, Scott MacFarlane, Olusola Popoola, Olumuyiwa Akinbamijo, Michael Oyinloye, the Geospatial Centre, the Department of Geography and Environmental Management, the Faculty of Environment, Toronto’s Urban forest management, Toronto Police Service and Statistics Canada for their support and the various roles they played in making my thesis a success. Thank you, all. v I want to appreciate my Uncle Barrister & Mrs. Gbadebo Olabode for believing in me even when I did not believe in myself. I’ll like to thank Dr. Amelia Clarke, and Catherine Bernard for your motherly support and assurance that everything is will be fine. I will like to thank Counselling Services (especially Emmanuel Ugwuegbula and Shelley Huisken) for kind words during the demise of my late mother. Big shout out to the Onifade, Olabode, Akintunde and Ajibawo’s Families for their moral support over the years. vi Dedication Dedicated to the memory of my Mother Modupe Anike Onifade (Nee Akintunde), who always believed in my ability to be successful in the academic arena. You are gone, but your belief in me has made this journey possible. vii Table of Contents Author’s Declaration .................................................................................................................................. ii Abstract ....................................................................................................................................................... iii Acknowledgement ....................................................................................................................................... v Dedication .................................................................................................................................................. vii List of Figures .............................................................................................................................................. x List of Tables ............................................................................................................................................. xii Chapter 1 Introduction ............................................................................................................................... 1 1.1 Introduction ................................................................................................................................. 1 1.2 Research Questions ......................................................................................................................... 10 1.3 Study Rationale ............................................................................................................................... 10 Chapter 2 Literature Review ................................................................................................................... 13 2.1 Crime Studies in Toronto ............................................................................................................... 13 2.2 Previous research on street crime in urban settings .................................................................... 16 2.3 Seasonality of Crime ....................................................................................................................... 17 2.4 Theories Associated with spatial analysis of Crime ..................................................................... 20 2.5 Crime and Greenspace
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