Modeling and estimation for self-exciting spatio-temporal models of terrorist activity

dc.contributor.author Clark, Nicholas
dc.contributor.author Dixon, Philip
dc.contributor.author Dixon, Philip
dc.contributor.department Statistics
dc.date 2019-06-26T01:06:22.000
dc.date.accessioned 2020-07-02T06:56:54Z
dc.date.available 2020-07-02T06:56:54Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 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="http://dx.doi.org/10.1214/17-AOAS1112" target="_blank">10.1214/17-AOAS1112</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/161/
dc.identifier.articleid 1163
dc.identifier.contextkey 14403201
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/161
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90467
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/161/0-Policy_for_2018_ModelingEstimation.pdf|||Fri Jan 14 20:55:10 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/161/2018_Dixon_ModelingEstimation.pdf|||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
relation.isAuthorOfPublication 7b3eb8d2-a569-4aba-87a1-5d9c2d99fade
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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