Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Regression Geograficamente Ponderata Multiscala (MGWR)× | Modello a Lag Spaziale (SAR / Autoregressivo Spaziale)× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2017 | 1988 |
| Ideatore≠ | Fotheringham, Yang & Kang | Anselin (textbook formalisation); LeSage & Pace |
| Tipo≠ | Spatially varying coefficient regression | Spatial autoregressive 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 ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Alias≠ | multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR) | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| Correlati | 5 | 5 |
| Sintesi≠ | Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
| ScholarGateInsieme di dati ↗ |
|
|