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| Panel MGWR (Panel Multiscale Geographically Weighted Regression)× | Regressione Geograficamente Ponderata Locale (GWR)× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2017-2020 | 1996 |
| Ideatore≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | Brunsdon, Fotheringham & Charlton |
| Tipo≠ | Spatially varying coefficient panel regression | Spatially varying coefficient regression |
| Fonte seminale≠ | 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 ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Alias | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model |
| Correlati | 5 | 5 |
| Sintesi≠ | Panel MGWR extends Multiscale Geographically Weighted Regression to repeated-observations (panel) data, allowing each predictor to operate at its own spatial bandwidth while controlling for unit-specific or time-specific fixed effects. It is used when both spatial heterogeneity and temporal structure matter simultaneously. | Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data. |
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