Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Exploiting autoregressive properties to develop prospective urban arson forecasts by target

Published

Author(s)

Jeffrey P. Prestemon, David Butry, Douglas Thomas

Abstract

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.
Citation
Applied Geography
Volume
44

Citation

Prestemon, J. , Butry, D. and Thomas, D. (2013), Exploiting autoregressive properties to develop prospective urban arson forecasts by target, Applied Geography (Accessed May 30, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 30, 2013, Updated October 12, 2021