A genetic approach to detecting clusters in point data sets

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dc.contributor.author Conley, J en
dc.contributor.author Gahegan, Mark en
dc.contributor.author Macgill, J en
dc.date.accessioned 2011-08-10T22:17:23Z en
dc.date.issued 2005-07-01 en
dc.identifier.citation Geographical Analysis 37(3):286-314 2005 en
dc.identifier.issn 0016-7363 en
dc.identifier.uri http://hdl.handle.net/2292/7270 en
dc.description.abstract Spatial analysis techniques are widely used throughout geography. However, as the size of geographic data sets increases exponentially, limitations to the traditional methods of spatial analysis become apparent. To overcome some of these limitations, many algorithms for exploratory spatial analysis have been developed. This article presents both a new cluster detection method based on a genetic algorithm, and Programs for Cluster Detection, a toolkit application containing the new method as well as implementations of three established methods: Openshaw’s Geographical Analysis Machine (GAM), case point-centered searching (proposed by Besag and Newell), and randomized GAM (proposed by Fotheringham and Zhan). We compare the effectiveness of cluster detection and the runtime performance of these four methods and Kulldorf’s spatial scan statistic on a synthetic point data set simulating incidence of a rare disease among a spatially variable background population. The proposed method has faster average running times than the other methods and significantly reduces overreporting of the underlying clusters, thus reducing the user’s postprocessing burden. Therefore, the proposed method improves upon previous methods for automated cluster detection. The results of our method are also compared with those of Map Explorer (MAPEX), a previous attempt to develop a genetic algorithm for cluster detection. The results of these comparisons indicate that our method overcomes many of the problems faced by MAPEX, thus, we believe, establishing that genetic algorithms can indeed offer a viable approach to cluster detection. en
dc.language English en
dc.publisher WILEY-BLACKWELL en
dc.relation.ispartofseries GEOGRAPHICAL ANALYSIS en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0016-7363// en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.subject Social Sciences en
dc.subject Geography en
dc.subject UPSTATE NEW-YORK en
dc.subject WILD GOOSE en
dc.subject SPACE-TIME en
dc.subject DISEASE en
dc.subject LEUKEMIA en
dc.subject ALGORITHMS en
dc.subject PATTERNS en
dc.title A genetic approach to detecting clusters in point data sets en
dc.type Journal Article en
dc.identifier.doi 10.1111/j.1538-4632.2005.00617.x en
pubs.issue 3 en
pubs.begin-page 286 en
pubs.volume 37 en
dc.rights.holder Copyright: 2005 The Ohio State University en
pubs.author-url http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000230422600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d en
pubs.end-page 314 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 194872 en
pubs.org-id Science en
pubs.org-id School of Computer Science en
pubs.record-created-at-source-date 2013-06-05 en

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