Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. Because of this, geostatistical techniques not only have the capability of producing a prediction surface but also provide some measure of the certainty or accuracy of the predictions. A second family of interpolation methods consists of geostatistical methods, such as kriging, which are based on statistical models that include autocorrelation-that is, the statistical relationships among the measured points. The IDW (inverse distance weighted) and Spline interpolation tools are referred to as deterministic interpolation methods because they are directly based on the surrounding measured values or on specified mathematical formulas that determine the smoothness of the resulting surface. Unlike other interpolation methods in the Interpolation toolset, to use the Kriging tool effectively involves an interactive investigation of the spatial behavior of the phenomenon represented by the z-values before you select the best estimation method for generating the output surface. Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. Understanding a semivariogram-Range, sill, and nugget.Fitting a model to the empirical semivariogram. Creating a prediction surface map with kriging.
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