Analyzing Hotspots of Crime Using a Bayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the Greater Toronto Area

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.