Exploring property crime and business locations: Using spatial analysis and firm count data to reveal correlations in Toronto, Ontario

This article presents an exploratory analysis of the relationship between the population, firm counts and average property crime from 2017 to 2020 across the Toronto census metropolitan area (CMA). It combines datasets from different domains—crime, business counts and population data—using 500 m by 500 m spatial grids to explore their relationships. At this scale, residential and business land use can be at least partially separated, allowing the independent association between residential populations, business counts and crime to be measured and mapped across the Toronto CMA. This analysis provides a picture of the spatial pattern of crimes across the CMA, explores and validate the data by establishing expected baseline relationships, and points towards areas for more in-depth analysis to determine the relationship between crime and business outcomes. After accounting for the population of grid squares, a positive association between business counts and crime was found, consistent with previous work. Furthermore, after considering population and firm counts, statistically significant spatial clusters of high (and low) crime rates were found. This work therefore sets the foundation for future analysis that would examine how variations in crime rates across space and time affect business outcomes (e.g., firm profitability and exit).