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| Regressione Geograficamente Ponderata Locale (GWR)× | Regressione Pesata Geograficamente Multiscala (MGWR)× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1996 | 2017 |
| Ideatore≠ | Brunsdon, Fotheringham & Charlton | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tipo≠ | Spatially varying coefficient regression | Local spatial regression |
| Fonte seminale≠ | 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 ↗ |
| Alias | GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | 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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