Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes
This work addresses a domain-specific problem for law enforcement by providing a predictive tool for serial crime location forecasting, though it appears incremental as it builds on existing self-exciting point process paradigms.
The authors tackled the problem of predicting the next crime location in a serial crime series by developing the Next Hit Predictor (NHP), a self-exciting risk model that uses spatial-temporal and geographical features from prior offenses, and demonstrated promising results on decades of data from the Cambridge Police Department.
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 predictive model called Next Hit Predictor (NHP) that finds the most likely location of the next serial crime via a carefully designed risk model. The risk model follows the paradigm of a self-exciting point process which consists of a background crime risk and triggered risks stimulated by previous offenses in the series. Thus, NHP creates a risk map for a crime series at hand. To train the risk model, we formulate a convex learning objective that considers pairwise rankings of locations and use stochastic gradient descent to learn the optimal parameters. Next Hit Predictor incorporates both spatial-temporal features and geographical characteristics of prior crime locations in the series. Next Hit Predictor has demonstrated promising results on decades' worth of serial crime data collected by the Crime Analysis Unit of the Cambridge Police Department in Massachusetts, USA.