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| Régression Géographiquement Pondérée Multiscale (MGWR)× | Modèle de Durbin spatial (SDM)× | |
|---|---|---|
| Domaine | Analyse spatiale | Analyse spatiale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2017 | 2009 |
| Auteur d'origine≠ | A. Stewart Fotheringham, Wei Yang, and Wei Kang | LeSage & Pace |
| Type≠ | Local spatial regression | Spatial regression model |
| Source fondatrice≠ | 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 ↗ | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| Alias≠ | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR | SDM, spatial mixed model, uzamsal durbin modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. |
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