Methoden vergleichen

	Bayesian Geographically Weighted Regression (BGWR)	Multiskalige Geographisch Gewichtete Regression (MGWR)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Regression model	Regression model
Entstehungsjahr	2007	2017
Urheber	Wheeler & Calder (2007); Finley (2011)	A. Stewart Fotheringham, Wei Yang, and Wei Kang
Typ	Bayesian spatially varying coefficient regression	Local spatial regression
Wegweisende Quelle	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., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
Aliasnamen	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR
Verwandt	5	5
Zusammenfassung	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.	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.