方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 面板地理加权回归 (Panel GWR)× | 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≠ | 2000s–2010s | 2017 |
| 提出者≠ | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| 类型≠ | Local spatial regression with panel structure | 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 | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| 相关≠ | 4 | 5 |
| 摘要≠ | Panel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatial estimation with panel-data controls for unit-specific heterogeneity. | 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. |
| ScholarGate数据集 ↗ |
|
|