Analyzing Hotspots of Crime Using aBayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in theGreaterTorontoArea

Abstract

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.

The Influence of On-Premise and Off-Premise Alcohol Outlets on Reported Violent Crime in the Region of Waterloo, Ontario: Applying Bayesian Spatial Modeling to Inform Land Use Planning and Policy

“…Figure 3 maps violent crime hotspots, which are identified based on the posterior probability of small-area violent crime risk being greater than 1.0. Areas with posterior probability greater than 0.95 are of particular interest for law enforcement and are considered as violent crime hotspots because they have 95 % probability of having relative risk greater than 1.0 (Law et al 2014). Hotspots are located in Waterloo near the city centre and universities, in and around downtown Kitchener, and in the city centre of Cambridge.…”

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

“…This is changing, however, as more researchers have become aware of the advantages of Bayesian spatial models. A small but growing body of research has adopted Bayesian spatial modeling approaches in crime analysis [10][11][12][13][14][15][16][17]. For example, researchers using Bayesian spatial models have investigated contextual influences on domestic violence [12,15,18], juvenile offenders [14] and property crime [13].…”

Exploring the Influence of Neighborhood Characteristics on Burglary Risks: A Bayesian Random Effects Modeling Approach

“…However, researchers are increasingly aware of the advantages of Bayesian spatial modeling in small-area crime analysis. The past few years have seen a small but growing body of literature applying Bayesian spatial approaches in crime researches [24][25][26][27][28][29][30][31][32][33][34][35]. For example, the relationship between alcohol outlets and domestic violence (intimate partner violence and child abuse) has been illustrated in a couple of ecological studies [25,29].…”