Local spatial regression models: a comparative analysis on soil contamination

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dc.authoridKaymak, Uzay/0000-0002-4500-9098
dc.authoridTERCAN, A.ERHAN/0000-0002-0393-4656
dc.authoridTutmez, Bulent/0000-0002-2618-3285
dc.authorwosidKaymak, Uzay/A-3364-2008
dc.authorwosidTutmez, Bulent/ABG-8630-2020
dc.authorwosidTERCAN, A.ERHAN/G-5921-2013
dc.contributor.authorTutmez, Bulent
dc.contributor.authorKaymak, Uzay
dc.contributor.authorTercan, A. Erhan
dc.date.accessioned2024-08-04T20:36:10Z
dc.date.available2024-08-04T20:36:10Z
dc.date.issued2012
dc.departmentİnönü Üniversitesien_US
dc.description.abstractSpatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [108M393]; COST (European Cooperation in Science and Technology) Action [IC0702]en_US
dc.description.sponsorshipThis research was supported by the Scientific and Technological Research Council of Turkey (TUBITAK Project: 108M393) and COST (European Cooperation in Science and Technology) Action IC0702.en_US
dc.identifier.doi10.1007/s00477-011-0532-2
dc.identifier.endpage1023en_US
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-84866459953en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1013en_US
dc.identifier.urihttps://doi.org/10.1007/s00477-011-0532-2
dc.identifier.urihttps://hdl.handle.net/11616/95803
dc.identifier.volume26en_US
dc.identifier.wosWOS:000308812500010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofStochastic Environmental Research and Risk Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLocal regression modellingen_US
dc.subjectGWRen_US
dc.subjectFuzzy clusteringen_US
dc.subjectRegional dependence functionen_US
dc.titleLocal spatial regression models: a comparative analysis on soil contaminationen_US
dc.typeArticleen_US

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