Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā universālā kriginga metode× | Parastā krigēšana× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1969/1997 | 1963 |
| Autors≠ | Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tips≠ | Spatial interpolation model | Geostatistical interpolation |
| Pirmavots≠ | Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press. ISBN: 9780195115383 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Citi nosaukumi | local UK, local kriging with trend, local KED, local kriging with external drift | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | 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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