A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting

Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important, since most hot spot forecasting models do not provide their underlying mechanisms. The current study first used a population heterogeneity framework to find places that were consistent hot spots. Second, it used a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm was implemented in Excel, making it simple to apply and completely transparent. The forecasting models had high accuracy and high efficiency in hotspot forecasting in both Portland and Cincinnati. (publisher abstract modified)