Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальный кригинг (кригинг в скользящем окне)× | Обычный кригинг× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990 | 1963 |
| Автор метода≠ | Haas, T. C. | Georges Matheron (formalising D.G. Krige's empirical work) |
| Тип≠ | Spatial interpolation (local variant) | Geostatistical interpolation |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | moving-window kriging, local kriging interpolation, windowed kriging, neighborhood kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Связанные≠ | 3 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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