Exploring the relationship between income inequality and crime in Toronto using frequentist and Bayesian models: Examining different crime types and spatial scales

Income inequality, which refers to the uneven distribution of income in a population, has been linked to many societal problems, including crime. Although environmental criminology theories, such as rational choice theory, suggest a positive association between income inequality and crime, previous empirical studies have reported divergent results based on different crime types, statistical models, and spatial units of analysis. This study employs non-spatial and spatial regression models using frequentist and Bayesian modelling frameworks to explore the impacts of within-area and across-area income inequality on five types of major crimes in the City of Toronto at the census tract and dissemination area scales. The use of spatial regression models improves the model fit in both frequentist and Bayesian frameworks. The Bayesian shared component model, which accounts for the interactions between different types of crimes, further enhances model performance. Results obtained from the best-fitting frequentist and Bayesian models are inconsistent but do not conflict in terms of the relationship between crime and income inequality, where within-area income inequality generally increases major crime rates, while across-area income inequality has varying effects dependent on crime type and spatial scale. The discrepancies between spatial scales are a manifestation of the modifiable areal unit problem (MAUP).