Clustering constrained on linear networks
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Altres documents de l'autoria: Martínez, Fabian; Chaudhuri, Somnath; Díaz-Avalos, Carlos; Juan, Pablo; Mateu, Jorge; Mena, Ramsés H.
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/173364
comunitat-uji-handle3:10234/173369
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Títol
Clustering constrained on linear networksAutoria
Data de publicació
2023Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliogràfica
MARTÍNEZ, Asael Fabian, et al. Clustering constrained on linear networks. Stochastic Environmental Research and Risk Assessment, 2023, p. 1-13Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s00477-022-02376-yVersió
info:eu-repo/semantics/acceptedVersionParaules clau / Matèries
Resum
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 ... [+]
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. [-]
Publicat a
Stochastic Environmental Research and Risk Assessment, 2023, p. 1-13Codi del projecte o subvenció
IG100221 | PID2019-107392RB-I00/AEI/10.13039/501100011033
Títol del projecte o subvenció
PAPIIT
Drets d'accés
"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
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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