Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Obyčejný kriging× | Prostorová autokorelace× | |
|---|---|---|
| Obor | Prostorová analýza | Prostorová analýza |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1963 | 1950 |
| Tvůrce≠ | Georges Matheron (formalising D.G. Krige's empirical work) | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Typ≠ | Geostatistical interpolation | Spatial statistic / exploratory spatial data analysis |
| Původní zdroj≠ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Další názvy | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Příbuzné≠ | 4 | 5 |
| Shrnutí≠ | 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. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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