Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā kriginga metode (kustīgā loga kriginga metode)× | Parastā krigēšana× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990 | 1963 |
| Autors≠ | Haas, T. C. | Georges Matheron (formalising D.G. Krige's empirical work) |
| Tips≠ | Spatial interpolation (local variant) | Geostatistical interpolation |
| Pirmavots≠ | Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment, 24(7), 1759-1769. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Citi nosaukumi | moving-window kriging, local kriging interpolation, windowed kriging, neighborhood kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | Local Kriging is a spatially adaptive geostatistical interpolation method that restricts each prediction to a moving neighborhood of nearby observations, fitting a variogram model locally within that window. This allows spatial covariance structure to vary across the study region rather than imposing a single global variogram, making it better suited to large or non-stationary spatial fields. | 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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