Comparer des méthodes

	Régression Pondérée Géographiquement (GWR)	Krigage universel (Krigage avec une tendance)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2002	1969
Auteur d'origine	Fotheringham, Brunsdon & Charlton	Georges Matheron
Type	Local spatial regression	Geostatistical interpolation with spatial trend
Source fondatrice	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Alias	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Apparentées	5	3
Résumé	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.