On the trend detection of time-ordered intensity images of point processes on linear networks
On the trend detection of time-ordered intensity images of point processes on linear networks
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.
1318-1330
Chaudhuri, Somnath
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Moradi, Mehdi
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Mateu, Jorge
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9 February 2021
Chaudhuri, Somnath
ae0507e0-f920-4438-bc9f-ecdd5ac8967a
Moradi, Mehdi
9ca36ca9-e4b3-468d-a30a-03e93c907343
Mateu, Jorge
522f2e31-3f2f-4a7e-b671-94255ae74e48
Chaudhuri, Somnath, Moradi, Mehdi and Mateu, Jorge
(2021)
On the trend detection of time-ordered intensity images of point processes on linear networks.
Communications in Statistics - Simulation and Computation, 52 (4), .
(doi:10.1080/03610918.2021.1881116).
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.
Text
On the trend detection of time-ordered intensity images of point processes on linear networks
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Accepted/In Press date: 20 January 2021
Published date: 9 February 2021
Identifiers
Local EPrints ID: 502622
URI: http://eprints.soton.ac.uk/id/eprint/502622
ISSN: 0361-0918
PURE UUID: d1de83f4-5bbe-4a69-a820-5391fb06e0a9
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Date deposited: 02 Jul 2025 16:32
Last modified: 22 Aug 2025 02:43
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Contributors
Author:
Somnath Chaudhuri
Author:
Mehdi Moradi
Author:
Jorge Mateu
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