Comparer des méthodes

	Modèle de Durbin Spatial Local	Régression Géographiquement Pondérée Multiscale (MGWR)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2002–2009	2017
Auteur d'origine	LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Type	Spatially varying regression model	Local spatial regression
Source fondatrice	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Alias	local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Apparentées	5	5
Résumé	The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects.	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.