Comparer des méthodes

	Pondération par distance inverse (IDW)	Cokrigage	Régression Pondérée Géographiquement (GWR)
Domaine	Analyse spatiale	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model	Regression model
Année d'origine	1968	1963	2002
Auteur d'origine	Donald Shepard	Georges Matheron (geostatistics); multivariate extension	Fotheringham, Brunsdon & Charlton
Type	Deterministic spatial interpolation	Multivariate geostatistical interpolation	Local spatial regression
Source fondatrice	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.	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	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	co-kriging, multivariate kriging, ortak kriging	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Apparentées	3	3	5
Résumé	Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.	Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.	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.