Journal Article10.1080/02693799008941549
Kriging: a method of interpolation for geographical information systems
M. A. Oliver,Richard Webster +1 more
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TL;DR: Kriging is the method of interpolation deriving from regionalized variable theory that depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated.
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Abstract: 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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Citations
Anomaly identification in soil geochemistry using multifractal interpolation: A case study using the distribution of Cu and Au in soils from the Tongling mining district, Yangtze metallogenic belt, Anhui province, China
TL;DR: In this paper, the authors compared Kriging and multifractal Krige interpolation methods for the mature Cu and Au Tongling mining district, in Anhui province, eastern China.
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Optimal sampling strategies for raster-based geographical information systems
Peter M. Atkinson
- 01 Jul 1996
TL;DR: In this paper, an optimal technique for linear unbiased estimation is used to design an optimal strategy to sample the property of interest in the field and subsequently to map the property with the map.
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Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting
Gustavo Willam Pereira,Domingos Sárvio Magalhães Valente,Daniel Marçal de Queiroz,Nerilson Terra Santos,Elpídio Inácio Fernandes-Filho +4 more
TL;DR: With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than traditional interpolation methods, IDW and Ordinary Kriging (OK).
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Geospatial Analysis on the Distributions of Tobacco Smoking and Alcohol Drinking in India
TL;DR: Results suggest that local public health action on smoking might also help to reduce alcohol consumption, and vice versa, and Surveys that properly represent tobacco and alcohol consumptions at the district level are recommended.
Quantifying soil variability in GIS applications: II Spatial distribution of soil properties
TL;DR: An overlay method was proposed that combines measured and published delineations of soil properties into an overlay with the properties of both and makes it possible to incorporate variability into a GIS analysis using data available from published soil surveys.
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References
•Book
Principles of Geographical Information Systems for Land Resources Assessment
Peter A. Burrough
- 21 Aug 1986
TL;DR: Geographical information systems Data structures for thematic maps Digital elevation models Data input, verification, storage, and output Methods of data analysis and spatial modelling Data quality, errors, and natural variation: sources of error Errors arising through processing.
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