Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Co-Krigage Global× | Kriging Ordinaire× | |
|---|---|---|
| Domaine | Analyse spatiale | Analyse spatiale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1982 | 1963 |
| Auteur d'origine≠ | Matheron (geostatistics framework); formalized for multivariate case by Myers (1982) | Georges Matheron (formalising D.G. Krige's empirical work) |
| Type≠ | Multivariate geostatistical interpolation | Geostatistical interpolation |
| Source fondatrice≠ | Myers, D. E. (1982). Matrix formulation of co-kriging. Journal of the International Association for Mathematical Geology, 14(3), 249–257. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Alias | global cokriging, co-kriging, cokriging, multivariate kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Apparentées | 4 | 4 |
| Résumé≠ | Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study domain and solves one global cokriging system for each prediction location. | 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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