Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Глобальный Кокригинг× | Обычный кригинг× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1982 | 1963 |
| Автор метода≠ | Matheron (geostatistics framework); formalized for multivariate case by Myers (1982) | Georges Matheron (formalising D.G. Krige's empirical work) |
| Тип≠ | Multivariate geostatistical interpolation | Geostatistical interpolation |
| Основополагающий источник≠ | Myers, D. E. (1982). Matrix formulation of co-kriging. Journal of the International Association for Mathematical Geology, 14(3), 249–257. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ |
| Другие названия | global cokriging, co-kriging, cokriging, multivariate kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| Связанные | 4 | 4 |
| Сводка≠ | Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study domain and solves one global cokriging system for each prediction location. | 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 ↗ |
|
|