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dc.contributor.authorRakshit, Suman
dc.contributor.authorNair, G.
dc.contributor.authorBaddeley, Adrian
dc.date.accessioned2018-02-01T05:23:19Z
dc.date.available2018-02-01T05:23:19Z
dc.date.created2018-02-01T04:49:25Z
dc.date.issued2017
dc.identifier.citationRakshit, S. and Nair, G. and Baddeley, A. 2017. Second-order analysis of point patterns on a network using any distance metric. Spatial Statistics. 22: pp. 129-154.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/62361
dc.identifier.doi10.1016/j.spasta.2017.10.002
dc.description.abstract

© 2017 Elsevier B.V. The analysis of clustering and correlation between points on a linear network, such as traffic accident locations on a street network, depends crucially on how we measure the distance between points. Standard practice is to measure distance by the length of the shortest path. However, this may be inappropriate and even fallacious in some applications. Alternative distance metrics include Euclidean, least-cost, and resistance distances. This paper develops a general framework for the second-order analysis of point patterns on a linear network, using a broad class of distance metrics on the network. We examine the model assumptions that are implicit in choosing a particular distance metric; define appropriate analogues of the K-function and pair correlation function; develop estimators of these characteristics; and study their statistical performance. The methods are tested on several datasets, including a demonstration that different conclusions can be reached using different choices of metric.

dc.titleSecond-order analysis of point patterns on a network using any distance metric
dc.typeJournal Article
dcterms.source.volume22
dcterms.source.startPage129
dcterms.source.endPage154
dcterms.source.issn2211-6753
dcterms.source.titleSpatial Statistics
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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