方法对比
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| 面板网络空间计量分析× | 面板地理加权回归 (Panel GWR)× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 2000s–2010s | 2000s–2010s |
| 提出者≠ | Developed from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworks | Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature |
| 类型≠ | Panel spatial regression | Local spatial regression with panel structure |
| 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 | panel spatial network analysis, longitudinal network spatial analysis, panel network spatial econometrics, PNBSA | Panel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression |
| 相关≠ | 5 | 4 |
| 摘要≠ | Panel Network-Based Spatial Analysis extends standard spatial econometric models to repeated-measures (panel) data by representing spatial dependence through network connectivity rather than simple geographic proximity. It captures how units connected in a network influence each other's outcomes over time, while controlling for unit-level and time-level fixed effects. | 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. |
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