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

Abstract

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 as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered.