Evaluating geo-environmental variables using a clustering based areal model

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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.authoridLloyd, Christopher/0000-0003-0575-7793
dc.authorwosidTutmez, Bulent/ABG-8630-2020
dc.authorwosidKaymak, Uzay/A-3364-2008
dc.authorwosidTERCAN, A.ERHAN/G-5921-2013
dc.contributor.authorTutmez, Bulent
dc.contributor.authorKaymak, Uzay
dc.contributor.authorTercan, A. Erhan
dc.contributor.authorLloyd, Christopher D.
dc.date.accessioned2024-08-04T20:35:50Z
dc.date.available2024-08-04T20:35:50Z
dc.date.issued2012
dc.departmentİnönü Üniversitesien_US
dc.description.abstractGlobal regression models do not accurately reflect the spatial heterogeneity which characterises most geo-environmental variables. In analysing the relationships between such variables, an approach is required which allows the model parameters to vary spatially. This paper proposes a new framework for exploring local relationships between geo-environmental variables. The method is based on extended objective function based fuzzy clustering with the environmental parameters estimated through on a locally weighted regression analysis. The case studies and prediction evaluations show that the fuzzy algorithm yields well-fitted models and accurate predictions. In addition to an increased accuracy of prediction relative to the widely-used geographically weighted regression (GWR), the proposed algorithm provides the search radius (bandwidth) and weights for local estimation directly from the data. The results suggest that the method could be employed effectively in tackling real world kernel-based modelling problems. (C) 2012 Elsevier Ltd. All rights reserved.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.doi10.1016/j.cageo.2012.02.019
dc.identifier.endpage41en_US
dc.identifier.issn0098-3004
dc.identifier.issn1873-7803
dc.identifier.scopus2-s2.0-84859412306en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage34en_US
dc.identifier.urihttps://doi.org/10.1016/j.cageo.2012.02.019
dc.identifier.urihttps://hdl.handle.net/11616/95609
dc.identifier.volume43en_US
dc.identifier.wosWOS:000305202500005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Geosciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSpatial relationshipen_US
dc.subjectGWRen_US
dc.subjectFuzzy clusteringen_US
dc.subjectLocal analysisen_US
dc.subjectGeo-environmentalen_US
dc.titleEvaluating geo-environmental variables using a clustering based areal modelen_US
dc.typeArticleen_US

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