Investigating a Bayesian Hierarchical Framework for Feature-Space Modeling of Criminal Site-Selection Problems

A significant amount of academic research in criminology focuses on spatial and temporal event analysis. Although several efforts have integrated spatial and temporal analyses, most previous work focuses on the space-time interaction and space-time clustering of criminal events. This research expands previous work in geostatistics and disease clustering by using a Bayesian hierarchical framework to model criminals’ spatial-temporal preferences for site-selection across a continuous time horizon. The development of this Bayesian hierarchical feature-space model (BHFSM) offers law enforcement personnel a method for accurate crime event forecasting while improving insight into criminal site-selection at the strategic level. We compare the BHFSM to other featurespace modeling techniques using both a long range and short range criminal event dataset collected from police reporting. While the BHFSM remains sufficiently accurate for event prediction, current computational requirements limit the applicability for “just-in-time” crime modeling.