Comparer des méthodes

	Régression Géographiquement Pondérée Bayésienne Multi-Échelle	Régression Spatiale Locale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2017-2020	1996
Auteur d'origine	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Brunsdon, Fotheringham & Charlton
Type	Spatially varying coefficient regression	Spatially varying coefficient 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.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Apparentées	6	6
Résumé	Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface.	Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.