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| Panel Spatial Regression× | Multiskalige Geographisch Gewichtete Regression (MGWR)× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1988-2014 | 2017 |
| Urheber≠ | Anselin, Elhorst, and colleagues in spatial econometrics | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Typ≠ | Spatial panel regression | Local spatial regression |
| Wegweisende Quelle≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 | 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 ↗ |
| Aliasnamen | spatial panel model, panel spatial econometrics, spatial panel data regression, PSR | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and efficient estimates when observations are spatially correlated across units. | 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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