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| Bayesian Geographically Weighted Regression (BGWR)× | Geographisch gewichtete Regression (GWR)× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2007 | 2002 |
| Urheber≠ | Wheeler & Calder (2007); Finley (2011) | Fotheringham, Brunsdon & Charlton |
| 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. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Aliasnamen | BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (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. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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