方法对比
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| 局部空间回归× | 多尺度地理加权回归 (MGWR)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1996 | 2017 |
| 提出者≠ | Brunsdon, Fotheringham & Charlton | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| 类型≠ | Spatially varying coefficient regression | Local spatial regression |
| 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 | Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ |
| 别名 | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| 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. | Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply. |
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