方法对比
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| 局部空间回归× | 空间杜宾模型 (SDM)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1996 | 2009 |
| 提出者≠ | Brunsdon, Fotheringham & Charlton | LeSage & Pace |
| 类型≠ | Spatially varying coefficient regression | Spatial regression model |
| 开创性文献≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| 别名≠ | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | SDM, spatial mixed model, uzamsal durbin modeli |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. |
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