方法对比
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| 局部空间杜宾模型× | 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≠ | 2002–2009 | 2017 |
| 提出者≠ | LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| 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., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ |
| 别名 | local SDM, geographically weighted Spatial Durbin Model, GW-SDM, spatially varying Durbin model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth 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. | 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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