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| Model Bayesański z Lagiem Przestrzennym× | Regresja geograficznie ważona (GWR)× | |
|---|---|---|
| Dziedzina | Analiza przestrzenna | Analiza przestrzenna |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1997 | 2002 |
| Twórca≠ | LeSage (1997); fully elaborated in LeSage & Pace (2009) | Fotheringham, Brunsdon & Charlton |
| Typ≠ | Bayesian spatial regression | Local spatial regression |
| Źródło pierwotne≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Inne nazwy | Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho. | 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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