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Article: Urban simulation using principal components analysis and cellular automata for land-use planning

TitleUrban simulation using principal components analysis and cellular automata for land-use planning
Authors
Issue Date2002
PublisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/pers
Citation
Photogrammetric Engineering And Remote Sensing, 2002, v. 68 n. 4, p. 341-351 How to Cite?
AbstractThis paper discusses the integration of cellular automata (CA), principal components analysis, and GIS techniques in simulating alternative urban growth patterns for land-use planning. The simulation of actual cities usually involves multicriteria evaluation (MCE) in tackling the problems of complex spatial factors. Spatial factors often exhibit a high degree of correlation which is considered an undesirable property for MCE. It is difficult to determine the weights when many spatial variables are involved. This study uses principal components analysis (PCA) to remove data redundancy among a large set of spatial variables and determine the "ideal point" for land development. The simulation is based on transition rules that are related to the neighborhood function and similarity between cells and the "ideal point." Principal components analysis helps to deal with a large data set of spatial variables for the implementation of the CA model.
Persistent Identifierhttp://hdl.handle.net/10722/89848
ISSN
2015 Impact Factor: 1.288
2015 SCImago Journal Rankings: 0.830
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_HK
dc.contributor.authorYeh, AGOen_HK
dc.date.accessioned2010-09-06T10:02:33Z-
dc.date.available2010-09-06T10:02:33Z-
dc.date.issued2002en_HK
dc.identifier.citationPhotogrammetric Engineering And Remote Sensing, 2002, v. 68 n. 4, p. 341-351en_HK
dc.identifier.issn0099-1112en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89848-
dc.description.abstractThis paper discusses the integration of cellular automata (CA), principal components analysis, and GIS techniques in simulating alternative urban growth patterns for land-use planning. The simulation of actual cities usually involves multicriteria evaluation (MCE) in tackling the problems of complex spatial factors. Spatial factors often exhibit a high degree of correlation which is considered an undesirable property for MCE. It is difficult to determine the weights when many spatial variables are involved. This study uses principal components analysis (PCA) to remove data redundancy among a large set of spatial variables and determine the "ideal point" for land development. The simulation is based on transition rules that are related to the neighborhood function and similarity between cells and the "ideal point." Principal components analysis helps to deal with a large data set of spatial variables for the implementation of the CA model.en_HK
dc.languageengen_HK
dc.publisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/persen_HK
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensingen_HK
dc.titleUrban simulation using principal components analysis and cellular automata for land-use planningen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0099-1112&volume=68&issue=4&spage=341&epage=351&date=2002&atitle=Urban+Simulation+Using+Principal+Components+Analysis+and+Cellular+Automata+for+Land+Use+Planningen_HK
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0036219267en_HK
dc.identifier.hkuros73974en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036219267&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume68en_HK
dc.identifier.issue4en_HK
dc.identifier.spage341en_HK
dc.identifier.epage351en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLi, X=34872691500en_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK

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