Comparer des méthodes

	Régression Géographiquement Pondérée Multiscale (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	A. Stewart Fotheringham, Wei Yang, and Wei Kang	Anselin (textbook formalisation); LeSage & Pace
Type	Local spatial 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	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Apparentées	5	5
Résumé	Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.	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.