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Our goal is to predict the location of the next crime in a crime series,based on the identified previous offenses in the series. We build a predictivemodel called Next Hit Predictor (NHP) that finds the most likely location ofthe next serial crime via a carefully designed risk model. The risk modelfollows the paradigm of a self-exciting point process which consists of abackground crime risk and triggered risks stimulated by previous offenses inthe series. Thus, NHP creates a risk map for a crime series at hand. To trainthe risk model, we formulate a convex learning objective that considerspairwise rankings of locations and use stochastic gradient descent to learn theoptimal parameters. Next Hit Predictor incorporates both spatial-temporalfeatures and geographical characteristics of prior crime locations in theseries. Next Hit Predictor has demonstrated promising results on decades' worthof serial crime data collected by the Crime Analysis Unit of the CambridgePolice Department in Massachusetts, USA.

Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes