Methoden vergleichen

	Bayesian Geographically Weighted Regression (BGWR)	Spatial-Lag-Modell (SAR / Spatial Autoregressive)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Regression model	Regression model
Entstehungsjahr	2007	1988
Urheber	Wheeler & Calder (2007); Finley (2011)	Anselin (textbook formalisation); LeSage & Pace
Typ	Bayesian spatially varying coefficient regression	Spatial autoregressive 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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Aliasnamen	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.