Exploiting autoregressive properties to develop prospective urban arson forecasts by target
Jeffrey P. Prestemon, David Butry, Douglas Thomas
Municipal fire departments responded to approximately 53 000 intentionally-set fires annually from 2003 to 2007, according to NFPA figures. A disproportionate amount of these fires occur in spatio-temporal clusters, making them predictable and, perhaps, preventable. The objective of this research is to evaluate how the aggregation of data across space and target types (residential, non-residential, vehicle, outdoor and other) affects arson forecast skill for several target types of arson, all specified at the daily time step. To do this, we estimate, for the city of Detroit, Michigan, competing statistical models that either recognize or do not recognize potential temporal autoregressivity in the arson counts. Spatial units vary from Census tracts, police precincts, to citywide.