The Spatio-Temporal Modeling for Criminal Incidents
Law enforcement agencies monitor criminal incidents. With additional geographic and demographic data, law enforcement analysts look for spatio-temporal patterns in these incidents in order to predict future criminal activity. When done correctly these predictions can inform actions that can improve security and reduce the impact of crime. Effective prediction requires the development of models that can find and incorporate the important associative and causative variables available in the data. This paper describes a new approach that uses Spatio-Temporal Generalized Additive Models (ST-GAMs) to discover underlying factors related to crimes and predict future incidents.