Development of Macro-Level Crime and Collision Prediction Models to Support Data-Driven Approach to Crime and Traffic Safety (DDACTS)

Abstract

The number of violent crimes and fatal-injury collisions concerns many jurisdictions. Traditional enforcement tactics are often reactive, relying on historical crime and collision data to select locations for law enforcement. Advanced law enforcement tactics take a proactive approach. Such tactics include Data-Driven Approaches to Crime and Traffic Safety (DDACTS), which uses predicted numbers of crimes and collisions to identify locations for law enforcement. This DDACTS study was conducted in Regina, Saskatchewan, Canada. The research developed macro-level prediction models to predict violent crimes and collisions in each traffic analysis zone (TAZ) in Regina. The zonal nature of the analysis is important for overcoming confidentiality and privacy issues associated with violent crimes and fatal-injury collisions. Fifty-four input variables were used to describe each TAZ’s crimes, collisions, socio-demographic, road inventory, traffic, and land use characteristics. The analysis used negative binomial regression coupled with the empirical Bayes method (a popular approach in transportation, but relatively new to crime mapping) to develop two statistical models that predict the long-term mean value for the number of violent crimes/collisions per zone. Cumulative residual plots were used as the main goodness-of-fit test. The findings are summarized on a map showing the top ten hotzones for violent crimes, the top ten hotzones for fatal-injury collisions, and the zones where the crime and collisions zones overlap. The overlapping zones are the DDACTS zones. By focusing law enforcement in the DDACTS zones, it may be possible to reduce violent crimes and fatal-injury collisions simultaneously and use limited resources more cost effectively.