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On the trend detection of time-ordered intensity images of point processes on linear networks
dc.contributor.author | Chaudhuri, Somnath | |
dc.contributor.author | Moradi, Mehdi | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2021-04-30T13:44:32Z | |
dc.date.available | 2021-04-30T13:44:32Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Somnath Chaudhuri, Mehdi Moradi & Jorge Mateu (2023) On the trend detection of time-ordered intensity images of point processes on linear networks, Communications in Statistics - Simulation and Computation, 52:4, 1318-1330, DOI: 10.1080/03610918.2021.1881116 | ca_CA |
dc.identifier.issn | 0361-0918 | |
dc.identifier.issn | 1532-4141 | |
dc.identifier.uri | http://hdl.handle.net/10234/192965 | |
dc.description.abstract | Spatial point processes on linear networks are increasingly getting attention in different disciplines such as traffic accidents and street crime analysis. Dealing with a set of time-ordered point patterns on a linear network over a period, helps in obtaining a time series of estimated intensity images. In this article, we combine the problem of estimating the intensity and relative risk of point patterns on linear networks with trend detection in time-ordered observations. Taking the temporal autocorrelation between consecutive time-ordered intensity and relative risk images into account, we make use of the Mann–Kendall trend test to look for potential locations in the network where the estimated intensity and/or relative risk show evidence of a monotonic trend. The monthly time-ordered spatial point patterns of fatal traffic accidents and street crimes in the city of London, UK, in the period of January 2013 to December 2017, are used as an application. | ca_CA |
dc.format.extent | 13 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Taylor & Francis | ca_CA |
dc.relation.isPartOf | Communications in Statistics - Simulation and Computation, 52:4 (2023). | ca_CA |
dc.rights | © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Mann–Kendall trend test | ca_CA |
dc.subject | relative risk | ca_CA |
dc.subject | separability | ca_CA |
dc.subject | spatio-temporal data | ca_CA |
dc.subject | street crime | ca_CA |
dc.subject | traffic accident | ca_CA |
dc.title | On the trend detection of time-ordered intensity images of point processes on linear networks | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1080/03610918.2021.1881116 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.tandfonline.com/doi/abs/10.1080/03610918.2021.1881116 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación | ca_CA |
oaire.awardNumber | UJI-B2018-04 | ca_CA |
oaire.awardNumber | AICO/2019/198 | ca_CA |
oaire.awardNumber | MTM2016-78917-R | ca_CA |
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Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way