Modeling and estimation for self-exciting spatio-temporal models of terrorist activity Clark, Nicholas Dixon, Philip Dixon, Philip
dc.contributor.department Statistics 2019-06-26T01:06:22.000 2020-07-02T06:56:54Z 2020-07-02T06:56:54Z Mon Jan 01 00:00:00 UTC 2018 2018-01-01
dc.description.abstract <p>Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely. The spatio-temporal diffusion is placed, as a matter of convenience, in the process model allowing for straightforward estimation of the diffusion parameters through Bayesian techniques. However, this method of modeling does not allow for the existence of self-excitation, or a temporal data model dependency, that has been shown to exist in criminal and terrorism data. In this manuscript we will use existing theories on how violence spreads to create models that allow for both spatio-temporal diffusion in the process model as well as temporal diffusion, or self-excitation, in the data model. We will further demonstrate how Laplace approximations similar to their use in Integrated Nested Laplace Approximation can be used to quickly and accurately conduct inference of self-exciting spatio-temporal models allowing practitioners a new way of fitting and comparing multiple process models. We will illustrate this approach by fitting a self-exciting spatio-temporal model to terrorism data in Iraq and demonstrate how choice of process model leads to differing conclusions on the existence of self-excitation in the data and differing conclusions on how violence spread spatially-temporally in that country from 2003–2010.</p>
dc.description.comments <p>This article is published as Clark, Nicholas J., and Philip M. Dixon. "Modeling and estimation for self-exciting spatio-temporal models of terrorist activity." <em>The Annals of Applied Statistics</em> 12, no. 1 (2018): 633-653. doi: <a href="" target="_blank">10.1214/17-AOAS1112</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1163
dc.identifier.contextkey 14403201
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/161
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 20:55:10 UTC 2022
dc.source.bitstream archive/|||Fri Jan 14 20:55:12 UTC 2022
dc.source.uri 10.1214/17-AOAS1112
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Criminology and Criminal Justice
dc.subject.disciplines Spatial Science
dc.subject.disciplines Statistical Models
dc.title Modeling and estimation for self-exciting spatio-temporal models of terrorist activity
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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