Confronta i metodi

	Regressione Geograficamente Ponderata Bayesiana (BGWR)	Regressiona Spaziale Locale
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	2007	1996
Ideatore	Wheeler & Calder (2007); Finley (2011)	Brunsdon, Fotheringham & Charlton
Tipo	Bayesian spatially varying coefficient regression	Spatially varying coefficient regression
Fonte seminale	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
Correlati	5	6
Sintesi	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.