A Geographic Understanding of Burglary Hotspots in San Antonio, Texas

Abstract

Increasing crime rates in expanding urban areas demand immediate attention. Geographic Information Systems (GIS) are used as a device to identify concentrations of crimes, but could prove beneficial as a tool to prevent crime. Routine Activity Theory maintains that to understand why crimes occur there must be an understanding of where criminals target, who is being targeted, and who is committing the come. This paper presents a method to detect differences between crime hotspots and non-hotspots of burglaries, based on physical and demographic characteristics of census block groups in two police substations, Prue and Central, in San Antonio, Texas. By employing Spatial and Temporal Analysis of Crime (STAC), areas associated with high burglary incidents were identified. These areas were used to conduct a logistic regression based on components created in a factor analysis. This analysis could lead to a better understanding of the characteristics of high crime areas. The logistic regression for classifying characteristics inside and outside hotspot areas was significant. Comparison of the similarities and differences between the equations created for the Prue and Central substation offered insight into the importance of Routine Activity Theory and geography in crime hotspot identification.