Abstract
Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.
Highlights
Crime activities are often unevenly distributed over space in a municipality or geographic region [1], and the occurrence of crime tends to concentrate in particular neighbourhoods or settings [2,3,4,5]
Ordinary Least Squares (OLS) regression is employed to explore the statistical association between crime rates and the independent variables measuring offenders’ characteristics and neighbourhood socioeconomic context
An increase in proportions of the 18-to-34 age group among offenders in neighbourhoods positively relates to an increase in violent crime rates, but negatively to property crime rates
Summary
Crime activities are often unevenly distributed over space in a municipality or geographic region [1], and the occurrence of crime tends to concentrate in particular neighbourhoods or settings [2,3,4,5]. Studies that focus on the influence of the neighbourhood (or city) context on crime often use contextual variables from census-based measures of deprivation or measures of other attributes such as alcohol outlet density, at a neighbourhood level, in order to identify the association between the neighbourhood environment and crime rates [5,16,22]. These studies feature a prominent spatial dimension, as criminal activities often cluster in socioeconomically marginalised or deprived areas. The neighbourhood is deemed an appropriate unit in the spatial analysis of crime in Toronto given its relatively homogenous socioeconomic characteristics and the confidential nature of crime data, which limits data access at finer spatial scales, including an individual level
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