The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

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.

Comparing Residents’ Fear of Crime with Recorded Crime Data—Case Study of Ostrava, Czech Republic

“…On the other hand, there has been a vast amount of research focusing purely on analysing and exploring patterns in recorded crime data [7][8][9][10][11][12][13][14][15][16][17][18][19] relating to the time of day and land use. This research combines the perceived crime data with recorded crime data in one analysis in order to explore the attributes of various parts of the selected city-Ostrava, Czech Republic.…”

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Estudo Bibliométrico Sobre O Uso De Geotecnologias Aplicadas Ao Planejamento De Cidades Médias

“…A utilização de várias fontes de dados para a fusão de imagens visando o mapeamento do solo urbano foi testada por Feng et al (2019), os quais obtiveram resultados promissores com o uso de redes neurais de aprendizagem profunda para classificar imagens e detectar objetos. Estes autores propuseram uma rede neural convolucional de dois ramos modificada para fundir imagens hiperespectrais (HSI) e dados de detecção ajustados à luz (LIDAR).…”

“…Da utilização de dados de várias fontes pode-se extrair pela fusão recursos das imagens, de acordo com Feng et al (2019), melhorando a precisão de mapeamento do uso urbano da terra.…”