“Kriging: A Method of Interpolation For Geographical Information Systems,”

Geographical Information Systems 07/1990; 4(3):313-332. DOI: 10.1080/02693799008941549
Source: DBLP


Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities. Most traditional methods of interpolation are based on mathematical as distinct from stochastic models of spatial variation. Spatially distributed data behave more like random variables, however, and regionalized variable theory provides a set of stochastic methods for analysing them. Kriging is the method of interpolation deriving from regionalized variable theory. It depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated. We describe the procedures and the way we link them using standard operating systems. We illustrate them using examples from case studies, one involving the mapping and control of soil salinity in the Jordan Valley of Israel, the other in semi-arid Botswana where the herbaceous cover was estimated and mapped from aerial photographic survey.

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    • "To this effect, we computed Moran's I, an index of spatial autocorrelation (Moran, 1950), with an increasing neighborhood distance between observations (from 2 km to 30 km), in ArcGIS 10.0 (ESRI, Redlands, CA, USA). We applied a threshold criterion that the absolute index values were >0.3 as an indication of autocorrelation (e.g., Oliver and Webster, 1990; Hitziger and Ließ, 2014). "
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