Exploring the Spatial Patterns of Income Inequality and Its Impacts on Major Crime Rates in the City of Toronto

Keywords: income inequality, crime, spatial data analysis, spatial regression, modifiable areal unit problem
Session Type: Virtual Paper
Day: Friday
Session Start / End Time: 4/9/2021 08:00 AM (Pacific Time (US & Canada)) - 4/9/2021 09:15 AM (Pacific Time (US & Canada))
Room: Virtual 9

Abstract

Income inequality is a major component of social inequality and it refers to the uneven distribution of income in a population. Existing literature suggests that income inequality has been continually increasing in most parts of the world and it can be the cause of many social problems, including crime. This study investigates the spatial patterns of income inequality and its association with different types of crimes in the City of Toronto. An exploratory spatial data analysis approach is used to analyze and map the spatial distributions of several economic metrics, and spatial regression models are employed to explore the impacts of these economic metrics on five types of major crimes (assault, break and enter, auto theft, robbery, and theft). Results of this research identify areas experiencing severe income inequality and explore the significant associations between income inequality and different types of crime in Toronto. This study also assesses two spatial units of analysis, census tracts and dissemination areas that were adopted in this study. Differences in statistical results at these two spatial scales further demonstrate the effects of the modifiable areal unit problem, where varying spatial data aggregation units lead to different analysis results. This study provides policymakers with insights into the spatial patterns of income inequality and socioeconomic status associated with crime in Toronto, which can better inform measures taken to reduce social inequality and to control neighbourhood crime.