Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Krigeage ordinaire global× | Kriging Ordinaire× | |
|---|---|---|
| Domaine | Analyse spatiale | Analyse spatiale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1951–1963 | 1963 |
| Auteur d'origine≠ | Danie G. Krige; formalized by Georges Matheron | Georges Matheron (formalising D.G. Krige's empirical work) |
| Type | Geostatistical interpolation | Geostatistical interpolation |
| Source fondatrice≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley. ISBN: 978-0471002550 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Alias | ordinary kriging, OK, global kriging, stationary ordinary kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Global Ordinary Kriging (GOK) is the canonical geostatistical interpolation method that estimates values at unsampled locations as a weighted linear combination of nearby observations. It fits a single variogram model to the entire dataset, enforcing a global stationarity assumption, and produces optimal unbiased predictions along with quantified prediction uncertainty at every interpolated point. | Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point. |
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