Identification of crime and collision hotspots for law enforcement: case study for the City of Regina

Author(s): Emmanuel Takyi, Peter Park
Student Paper Competition: 3rd Place

Slidedeck Presentation - not available


Traditionally, police enforcement tactics are reactive: a location is identified as a problem area before law enforcement measures are taken. Proactive approaches have been shown to be far more effective. Proactive enforcement systems include sophisticated intelligence-led policing, predictive policing, selective policing, and smart enforcement.
Data-Driven Approaches to Crime and Traffic Safety (DDACTS) have introduced many police departments in North America to more effective ways to control crimes, traffic violations, and traffic collisions. DDACTS is a law enforcement operational model that integrates location-based crime and traffic data to identify problem areas, inform enforcement decisions, and maximize the effectiveness and efficiency of available resources. It is a proactive and effective tool for reducing crime and preventing future crimes, while minimizing traffic collisions.
In this research, the study area is the City of Regina which has the highest crime severity index and highest crime rate in Canada. The analysis used R-language and ESRI’s ArcGIS to create a sets of maps that show spatio-temporal patterns in crimes and collisions. These patterns reveal hotspots where the overlap between crimes and collisions suggest that certain locations (and certain times) should be targeted for police tactics and enforcement.