Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gewone Kriging× | Ruimtelijke Autocorrelatie× | |
|---|---|---|
| Vakgebied | Ruimtelijke analyse | Ruimtelijke analyse |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1963 | 1950 |
| Grondlegger≠ | 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) |
| Type≠ | Geostatistical interpolation | Spatial statistic / exploratory spatial data analysis |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | 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. |
| ScholarGateGegevensset ↗ |
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