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Bayesiansk Geographically Weighted Regression (BGWR)×Spatial Lag Model (SAR / Spatial Autoregressive)×
ÄmnesområdeRumslig analysRumslig analys
FamiljRegression modelRegression model
Ursprungsår20071988
UpphovspersonWheeler & Calder (2007); Finley (2011)Anselin (textbook formalisation); LeSage & Pace
TypBayesian spatially varying coefficient regressionSpatial autoregressive regression
UrsprungskällaFinley, 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. DOI ↗Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗
AliasBGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regressionSAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Närliggande55
SammanfattningBayesian 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.
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ScholarGateJämför metoder: Bayesian Geographically Weighted Regression · Spatial Lag Model. Hämtad 2026-06-17 från https://scholargate.app/sv/compare