November 2018
Beginner to intermediate
354 pages
6h 59m
English
Content preview from Hands-On Geospatial Analysis with R and QGIS
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Geostatistics
In geostatistics, observed variables are random, with the assumption that we observe them as outcomes of random processes. Usually, the data is observed in discrete locations of a spatially continuous regions, and we are interested in the prediction of values in non-observed locations in the given region. For example, soil salinity is measured in certain points in an area, and we are interested in predicting or interpolating soil salinity in every point under this area. Now, soil salinity in a point could depend on multiple factors: it could be due to its proximity to the saline-affected area, its spatial autocorrelation, the climate, or a random process. Other examples of geostatistical data include rainfall data, air quality, ...
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