Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information

Personal and property crimes create large economic losses within the United States. To prevent crimes, law enforcement agencies model the spatio-temporal pattern of criminal incidents. In this paper, we present a new modeling process that combines two of our recently developed approaches for modeling criminal incidents. The first component of the process is the spatio-temporal generalized additive model (STGAM), which predicts the probability of criminal activity at a given location and time using a feature-based approach. The second component involves textual analysis. In our experiments, we automatically analyzed Twitter posts, which provide a rich, event-based context for criminal incidents. In addition, we describe a new feature selection method to identify important features. We applied our new model to actual criminal incidents in Charlottesville, Virginia. Our results indicate that the STGAM/Twitter model outperforms our previous STGAM model, which did not use Twitter information. The STGAM/Twitter model can be generalized to other applications of event modeling where unstructured text is available.