Gabby Lee

The overall objective of this study is to determine what neighbourhood and offender-related demographic characteristics impact crime rates in the City of Toronto. By doing so, quantitative and qualitative approaches were implemented in this study. This study includes both property and violent crime datasets from 2014-2016 and census related information from the 2011 Canadian Census. The advancing techniques of Geographical Information System (GIS) has been explored and applied to achieve a thorough understanding of crime occurrences and patterns in the city. Hotspot and Kernel Density mapping were applied to analyze the spatial distribution of crime occurrences and account for spatial autocorrelation. Findings revealed that property and violent crimes across the three years of study showed similar distribution of significant hotspots in the core, Northwest, and East end of the city. An Ordinary Least Square (OLS) regression was conducted to examine the ways in which individual and neighbourhood demographic characteristics predict the effects of crime occurrences. The OLS model was a good predictor for offender-related demographics as opposed to neighbourhood level demographics at the 0.05 significant level. These findings revealed that social disadvantaged neighbourhood characteristics such as low income, unemployment, low education, female lone parent were poor predictors of property crimes but good predictors for violent crimes.