方法对比
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| 贝叶斯地理加权回归 (BGWR)× | 空间滞后模型（SAR / 空间自回归）× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2007 | 1988 |
| 提出者≠ | Wheeler & Calder (2007); Finley (2011) | Anselin (textbook formalisation); LeSage & Pace |
| 类型≠ | Bayesian spatially varying coefficient regression | Spatial autoregressive regression |
| 开创性文献≠ | 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 ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| 别名 | BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
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