Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā parastā krigēšana× | Parastā krigēšana× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970s–1990s | 1963 |
| Autors≠ | Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tips≠ | Geostatistical interpolation (local/moving-window variant) | Geostatistical interpolation |
| Pirmavots≠ | Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley. ISBN: 978-0471083153 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Citi nosaukumi | moving window kriging, local kriging, neighborhood kriging, LOK | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Local Ordinary Kriging (LOK) is a geostatistical interpolation method that estimates values at unsampled locations using only a spatially defined moving neighborhood of nearby observations. By restricting each prediction to a local data window rather than the full dataset, LOK accommodates spatial non-stationarity, reduces computational cost, and often yields more accurate local predictions than global ordinary kriging. | 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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