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Erschienen in:
2013 | OriginalPaper | Buchkapitel
8. Interpolation and Geostatistics
verfasst von : Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio
Erschienen in: Applied Spatial Data Analysis with R
Verlag: Springer New York
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Abstract
Geostatistical data are data that could in principle be measured anywhere, but that typically come as measurements at a limited number of observation locations: think of gold grades in an ore body or particulate matter in air samples. The pattern of observation locations is usually not of primary interest, as it often results from considerations ranging from economical and physical constraints to being ‘representative’ or random sampling varieties. The interest is usually in inference of aspects of the variable that have not been measured such as maps of the estimated values, exceedance probabilities or estimates of aggregates over given regions, or inference of the process that generated the data. Other problems include monitoring network optimisation: where should new observations be located or which observation locations should be removed such that the operational value of the monitoring network is maximised.