This study discusses the preliminary results of a crime forecasting model developed in collaboration with the police department of a United States city in the Northeast.
The authors first discuss their approach to architecting datasets from original crime records. The datasets contain aggregated counts of crime and crime-related events categorized by the police department. The location and time of these events is embedded in the data. Additional spatial and temporal features are harvested from the raw data set. Second, an ensemble of data mining classification techniques is employed to perform the crime forecasting. They analyze a variety of classification methods to determine which is best for predicting crime "hotspots". The authors also investigate classification on increase or emergence. Last, they propose the best forecasting approach to achieve the most stable outcomes. The result of the authors’ research is a model that takes advantage of implicit and explicit spatial and temporal data to make reliable crime predictions. (Published abstract provided)