Partially-supervised spatiotemporal clustering for burglary crime series identification

Statistical clustering of criminal events can be used by crime analysts to create of lists of potential suspects for an unsolved crime, identify groups of crimes that may have been committed by the same individuals or group of individuals, for offender profiling, and for predicting future events. In this paper, we propose a Bayesian model-based clustering approach for criminal events. Our approach is semi-supervised because the offender is known for a subset of the events, and utilizes spatiotemporal crime locations as well as crime features describing the offender’s modus operandi. The hierarchical model naturally handles complex features often seen in crime data, including missing data, interval censored event times, and a mix of discrete and continuous variables. In addition, our Bayesian model produces posterior clustering probabilities which allow analysts to act on model output only as warranted. We illustrate the approach using a large data set of burglaries in 2009-2010 in Baltimore County, Maryland.