An Investigation of the Relationship between Crime and Reported Incidents and the Built and Natural Environment in the Region of Waterloo, Ontario

Abstract

In the study of crime and geography, many studies have investigated the spatial relationship between crime and the built and natural environment. However, these studies usually focus on specific environmental characteristics, such as alcohol serving businesses or the presence of vegetation. This study conducts a comprehensive analysis of the spatial relationship between crime and features of the built and natural environment in the sister cities of Kitchener and Waterloo, Ontario, taking into account many factors that may potentially affect crime and reported incidents. This includes built environment features, such as residential buildings, commercial buildings, drinking establishments, and bus stops. Natural environment features, such as parks and the presence of green vegetation were also considered. The measure of crime in this study was a geospatial record (aggregated to the nearest street intersection) of crime and reported incidents where police were called (e.g., emergency call and response) recorded by the Waterloo Regional Police Service (WRPS). Relationships between built and natural environment characteristics with crime and reported incidents were studied using linear regression and logistic regression modelling techniques based on three datasets. The first dataset involved creating a buffer around each street intersection and deriving the proportion of each building type and count of bus stops, streetlights, and alcohol licenses within a static or adaptive radius, which was subsequently compared with the number or presence of crime and reported incidents at each intersection. The second involved developing Adaptive Kernel Density Estimation (AKDE) rasters of each environmental feature and then conducting a regression analysis by comparing the number or presence of crime and reported incidents at each street intersection to its corresponding pixel values. The third involved using buffers to summarize the levels of vegetation cover detected from remote sensing imagery surrounding each street intersection, which was subsequently compared with the number of crime and reported incidents at each intersection. The results of this study identified overall low r-squared values for tested regression models, which suggests that important variables may be missing, such as socio-economic variables that may have a significant role in predicting crime incidents. The model also found that bus stops and alcohol licences were the most important urban environment factors in predicting crime and reported incidents in Kitchener-Waterloo.