A Statistical Threat Assessment

Criminal gangs, insurgent groups, and terror networks demonstrate observable preferences in selecting the sites where they commit their crimes. Accordingly, police departments, military organizations, and intelligence agencies seek to learn these preferences and identify locations with a high probability of experiencing the particular event of interest in the near future. Often, such agencies are keen not just to predict the spatial pattern of future events but even more importantly to conduct threat assessments of particular criminal gangs or insurgent groups. These threat assessments include identifying where each of the various groups presents the greatest threat to the community, what the most likely targets are for each criminal group, what makes one location more likely to experience an attack than another, and how to most efficiently allocate resources to address the specific threats to the community. Previous research has demonstrated that applying multivariate prediction models to relate features in an area to the occurrence of crimes offers an improvement in predictive performance over traditional methods of hot-spot analysis. This paper introduces the application of multilevel modeling to these multivariate spatial choice models, demonstrating that it is possible to significantly improve the predictive performance of the spatial choice models for individual groups and leverage that information to provide improved threat assessments of the criminal elements in a given geographic area.