Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Kriging global× | Krigingul Ordinar× | |
|---|---|---|
| Domeniu | Analiză spațială | Analiză spațială |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1960s–1993 | 1963 |
| Autorul original≠ | Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tip | Geostatistical interpolation | Geostatistical interpolation |
| Sursa seminală≠ | Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience. ISBN: 978-0471002550 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Denumiri alternative | global-neighborhood kriging, full-data kriging, exhaustive kriging, non-local kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset. | 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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