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September 2013 Modeling and estimation of multi-source clustering in crime and security data
George Mohler
Ann. Appl. Stat. 7(3): 1525-1539 (September 2013). DOI: 10.1214/13-AOAS647

Abstract

While the presence of clustering in crime and security event data is well established, the mechanism(s) by which clustering arises is not fully understood. Both contagion models and history independent correlation models are applied, but not simultaneously. In an attempt to disentangle contagion from other types of correlation, we consider a Hawkes process with background rate driven by a log Gaussian Cox process. Our inference methodology is an efficient Metropolis adjusted Langevin algorithm for filtering of the intensity and estimation of the model parameters. We apply the methodology to property and violent crime data from Chicago, terrorist attack data from Northern Ireland and Israel, and civilian casualty data from Iraq. For each data set we quantify the uncertainty in the levels of contagion vs. history independent correlation.

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George Mohler. "Modeling and estimation of multi-source clustering in crime and security data." Ann. Appl. Stat. 7 (3) 1525 - 1539, September 2013. https://doi.org/10.1214/13-AOAS647

Information

Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 06237186
MathSciNet: MR3127957
Digital Object Identifier: 10.1214/13-AOAS647

Keywords: Cox process , crime , Hawkes process , Markov chain Monte Carlo , terrorism

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 3 • September 2013
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