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| I di Moran multiscalare× | Regressione Pesata Geograficamente Multiscala (MGWR)× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1950 (base); multiscale variant 1980s-1990s | 2017 |
| Ideatore≠ | P. A. P. Moran (base statistic, 1950); multiscale extension developed through spatial ecology and geography literature | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tipo≠ | Spatial autocorrelation statistic | Local spatial regression |
| Fonte seminale≠ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17-23. DOI ↗ | 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 | multi-scale Moran's I, spatial correlogram Moran, Moran correlogram, multiscale spatial autocorrelation | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Multiscale Moran's I extends the classic global Moran's I statistic by computing spatial autocorrelation across a series of distance lags or spatial scales. The resulting spatial correlogram reveals at which geographic scales clusters or dispersions of a variable exist, offering richer insight than a single summary statistic. | 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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