Geographically weighted regression in geospatial analysis

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creativework.keywordsFirst law; Geo-spatial analysis; Geographically weighted regression; Parameters estimates; Spatial non-stationarity; Spatial relationships; Statistical techniques
dc.contributor.authorThapa, Rajesh Bahadur,
dc.contributor.authorEstoque, Ronald C.
dc.date.accessioned2026-09-01T03:28:47Z
dc.date.available2026-09-01T03:28:47Z
dc.date.issued2012
dc.descriptionFull text.
dc.description.abstractGeographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance. This is very close to Tobler's first law of geography-everything is related to everything else, but near things are more related than distant things (Tobler 1970). GWR was developed on the basis of the traditional regression framework which incorporates local spatial relationships into the framework in an intuitive and explicit manner (Brunsdon et al. 1996; Fotheringham and Brunsdon 1999; Fotheringham et al. 2002). © 2012 Springer Japan. All rights reserved.
dc.identifier.citationThapa, R. B., & Estoque, R. C. (2012). Geographically weighted regression in geospatial analysis. Progress in Geospatial Analysis, 9784431540007, 85 - 96. https://www.researchgate.net/profile/Rajesh-Thapa-4/publication/261296287_Progress_in_Geospatial_Analysis/links/5cc930e492851c8d22106171/Progress-in-Geospatial-Analysis.pdf
dc.identifier.doi10.1007/978-4-431-54000-7_6
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2429
dc.language.isoen
dc.publisherSpringer Japan
dc.sdgSDG 9
dc.subjectGeospatial analysis
dc.subjectGeographically weighted regression
dc.subjectSpatial statistics
dc.subjectSpatial relationships
dc.subject.ddc519.5
dc.subject.lcshGeographic information systems
dc.subject.lcshSpatial analysis (Statistics)
dc.subject.lcshRegression analysis
dc.subject.lcshSpatial statistics
dc.titleGeographically weighted regression in geospatial analysis
dc.typeBook chapter
oaire.citation.endPage96
oaire.citation.startPage85
oaire.citation.volume9784431540007
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