Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Krigage Universel Local× | Régression Pondérée Géographiquement (GWR)× | |
|---|---|---|
| Domaine | Analyse spatiale | Analyse spatiale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1969/1997 | 2002 |
| Auteur d'origine≠ | Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice | Fotheringham, Brunsdon & Charlton |
| Type≠ | Spatial interpolation model | Local spatial regression |
| Source fondatrice≠ | Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press. ISBN: 9780195115383 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Alias | local UK, local kriging with trend, local KED, local kriging with external drift | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Apparentées | 5 | 5 |
| Résumé≠ | Local Universal Kriging is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual, estimated using only nearby observations within a defined search neighborhood. It generalizes local ordinary kriging by explicitly modeling and removing a polynomial or covariate-driven drift before interpolating the residual surface. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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