Comparer des méthodes

	Krigage universel (Krigage avec une tendance)	Régression Pondérée Géographiquement (GWR)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1969	2002
Auteur d'origine	Georges Matheron	Fotheringham, Brunsdon & Charlton
Type	Geostatistical interpolation with spatial trend	Local spatial regression
Source fondatrice	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	kriging with a trend, kriging with drift, trend kriging, evrensel kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Apparentées	3	5
Résumé	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.	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.