Analyzing car thefts and recoveries with connections to modeling origin–destination point patterns
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comunitat-uji-handle2:10234/7037
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https://doi.org/10.1016/j.spasta.2020.100440 |
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Title
Analyzing car thefts and recoveries with connections to modeling origin–destination point patternsDate
2020Publisher
ElsevierISSN
2211-6753Bibliographic citation
SHIROTA, Shinichiro; GELFAND, Alan E.; MATEU, Jorge. Analyzing car thefts and recoveries with connections to modeling origin-destination point patterns. Spatial Statistics, 2020, p. 100440Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S2211675320300348Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
For a given region, we have a dataset composed of car theft loca-tionsalongwithalinkeddatasetofrecoverylocationswhich,dueto partial recovery, is a relatively small subset of the set of theftlocations. For an investigator ... [+]
For a given region, we have a dataset composed of car theft loca-tionsalongwithalinkeddatasetofrecoverylocationswhich,dueto partial recovery, is a relatively small subset of the set of theftlocations. For an investigator seeking to understand the behaviorof car thefts and recoveries in the region, several questions areaddressed. Viewing the set of theft locations as a point pattern,can we propose useful models to explain the pattern? Whattypes of predictive models can be built to learn about recoverylocation given theft location? Can the dependence between thepoint pattern of theft locations and the point pattern of recoverylocations be formalized? Can theflowbetween theft sites andrecovery sites be captured?Origin–destination modeling offers a natural framework forsuch problems. However, here the data is not for areal unitsbut rather is a pair of dependent point patterns, with the re-covery point pattern only partially observed. We offer modelingapproaches for investigating the questions above and apply theapproaches to two datasets. One is small from the state of Nezain Mexico with areal covariate information regarding populationfeatures and crime type. The second, a much larger one, is fromBelo Horizonte in Brazil but lacks potential predictors.For a given region, we have a dataset composed of car theft loca-tionsalongwithalinkeddatasetofrecoverylocationswhich,dueto partial recovery, is a relatively small subset of the set of theftlocations. For an investigator seeking to understand the behaviorof car thefts and recoveries in the region, several questions areaddressed. Viewing the set of theft locations as a point pattern,can we propose useful models to explain the pattern? Whattypes of predictive models can be built to learn about recoverylocation given theft location? Can the dependence between thepoint pattern of theft locations and the point pattern of recoverylocations be formalized? Can theflowbetween theft sites andrecovery sites be captured?Origin–destination modeling offers a natural framework forsuch problems. However, here the data is not for areal unitsbut rather is a pair of dependent point patterns, with the re-covery point pattern only partially observed. We offer modelingapproaches for investigating the questions above and apply theapproaches to two datasets. One is small from the state of Nezain Mexico with areal covariate information regarding populationfeatures and crime type. The second, a much larger one, is fromBelo Horizonte in Brazil but lacks potential predictors. [-]
Is part of
Spatial Statistics, 2020, p. 100440Investigation project
This work of the second author was partially funded by Grant MTM2016-78917-R from the Spanish Ministry of Science and Education, and Grant P1-1B2015-40 from University Jaume I, Spain. The work of the first author was supported in part by the Nakajima Foundation, Spain.Rights
© 2020 Elsevier B.V. All rights reserved.
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- INIT_Articles [754]
- MAT_Articles [766]