By Topic

The spatio-temporal generalized additive model for criminal incidents

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xiaofeng Wang ; Department of Systems and Information Engineering, University of Virginia, Charlottesville, 22904, USA ; Donald E. Brown

Law enforcement agencies need to model spatio-temporal patterns of criminal incidents. With well developed models, they can study the causality of crimes and predict future criminal incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual criminal incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future criminal incidents.

Published in:

Intelligence and Security Informatics (ISI), 2011 IEEE International Conference on

Date of Conference:

10-12 July 2011