Comparer des méthodes

	Régression Bayésienne Pondérée Géographiquement (BGWR)	Régression Spatiale Locale
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2007	1996
Auteur d'origine	Wheeler & Calder (2007); Finley (2011)	Brunsdon, Fotheringham & Charlton
Type	Bayesian spatially varying coefficient regression	Spatially varying coefficient regression
Source fondatrice	Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Apparentées	5	6
Résumé	Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates.	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.