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A computationally efficient method for delineating irregularly shaped spatial clusters

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dc.citation.journalTitleJournal of Geographical Systems
dc.contributor.authorDuque, Juan C.
dc.contributor.authorAldstadt, Jared
dc.contributor.authorVelasquez, Ermilson
dc.contributor.authorFranco, Jose L.
dc.contributor.authorBetancourt, Alejandro
dc.contributor.departmentUniversidad EAFIT. Departamento de Economía y Finanzasspa
dc.contributor.researchgroupResearch in Spatial Economics (RISE)eng
dc.date.accessioned2021-04-12T14:26:14Z
dc.date.available2021-04-12T14:26:14Z
dc.date.issued2011-12-01
dc.description.abstractIn this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327-343, 2006). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana. © 2010 Springer-Verlag.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1579
dc.identifier.doi10.1111/j.1538-4632.2010.00810.x
dc.identifier.issn14355930
dc.identifier.issn14355949
dc.identifier.otherWOS;000285875700006
dc.identifier.otherSCOPUS;2-s2.0-78650793094
dc.identifier.urihttps://hdl.handle.net/10784/28025
dc.language.isoeng
dc.publisherSpringer Berlin Heidelberg
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-80855130145&doi=10.1007%2fs10109-010-0137-1&partnerID=40&md5=0193eb5d477b06d3ca96b43f84618c8a
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1435-5930
dc.sourceJournal of Geographical Systems
dc.subject.keywordalgorithmeng
dc.subject.keywordautocorrelationeng
dc.subject.keywordcluster analysiseng
dc.subject.keywordempirical analysiseng
dc.subject.keywordspatial analysiseng
dc.subject.keywordstatistical analysiseng
dc.subject.keywordAccraeng
dc.subject.keywordGhanaeng
dc.subject.keywordGreater Accraeng
dc.titleA computationally efficient method for delineating irregularly shaped spatial clusters
dc.typeinfo:eu-repo/semantics/article
dc.type.coarversionhttp://purl.org/coar/resource_type/c_6501
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREF
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication

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