Comparer des méthodes

	Régression pondérée géographiquement multi-échelle (MGWR)	Modèle de retard spatial (SAR / Autoregressive Spatial)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2017	1988
Auteur d'origine	Fotheringham, Yang & Kang	Anselin (textbook formalisation); LeSage & Pace
Type	Spatially varying coefficient regression	Spatial autoregressive regression
Source fondatrice	Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR)	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Apparentées	5	5
Résumé	Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.