方法对比
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| 局部空间杜宾模型× | 局部空间回归× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002–2009 | 1996 |
| 提出者≠ | LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework | Brunsdon, Fotheringham & Charlton |
| 类型≠ | Spatially varying regression model | Spatially varying coefficient 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 | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression |
| 相关≠ | 5 | 6 |
| 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. | Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number. |
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