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Clustering constrained on linear networks
dc.contributor.author | Martínez, Fabian | |
dc.contributor.author | Chaudhuri, Somnath | |
dc.contributor.author | Díaz-Avalos, Carlos | |
dc.contributor.author | Juan, Pablo | |
dc.contributor.author | Mateu, Jorge | |
dc.contributor.author | Mena, Ramsés H. | |
dc.date.accessioned | 2023-04-21T14:43:40Z | |
dc.date.available | 2023-04-21T14:43:40Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | MARTÍNEZ, Asael Fabian, et al. Clustering constrained on linear networks. Stochastic Environmental Research and Risk Assessment, 2023, p. 1-13 | ca_CA |
dc.identifier.issn | 1436-3240 | |
dc.identifier.issn | 1436-3259 | |
dc.identifier.uri | http://hdl.handle.net/10234/202244 | |
dc.description.abstract | An unsupervised classification method for point events occurring on a geometric network is proposed. The idea relies on the distributional flexibility and practicality of random partition models to discover the clustering structure featuring observations from a particular phenomenon taking place on a given set of edges. By incorporating the spatial effect in the random partition distribution, induced by a Dirichlet process, one is able to control the distance between edges and events, thus leading to an appealing clustering method. A Gibbs sampler algorithm is proposed and evaluated with a sensitivity analysis. The proposal is motivated and illustrated by the analysis of crime and violence patterns in Mexico City. | ca_CA |
dc.format.extent | 13 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation | PAPIIT | ca_CA |
dc.relation.isPartOf | Stochastic Environmental Research and Risk Assessment, 2023, p. 1-13 | ca_CA |
dc.rights | "This is a post-peer-review, pre-copyedit version of an article published in Stochastic Environmental Research and Risk Assessment. The final authenticated version is available online at: https://doi.org/10.1007/s00477-022-02376-y | ca_CA |
dc.rights.uri | ca_CA | |
dc.subject | bayesian nonparametrics | ca_CA |
dc.subject | penalty function | ca_CA |
dc.subject | random partition model | ca_CA |
dc.subject | spatial clustering | ca_CA |
dc.title | Clustering constrained on linear networks | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s00477-022-02376-y | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s00477-022-02376-y | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
oaire.awardNumber | IG100221 | ca_CA |
oaire.awardNumber | PID2019-107392RB-I00/AEI/10.13039/501100011033 | ca_CA |
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