方法对比
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| 局部空间杜宾模型× | 地理加权回归 (GWR)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002–2009 | 2002 |
| 提出者≠ | LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework | Fotheringham, Brunsdon & Charlton |
| 类型≠ | Spatially varying regression model | Local spatial regression |
| 开创性文献≠ | 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 |
| 别名 | local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 相关 | 5 | 5 |
| 摘要≠ | The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects. | 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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