A Practical Guide to Geostatistical Mapping of Environmental by Hengl, Tomislav

By Hengl, Tomislav

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Although equivalent, KED and RK differ in the computational steps used. Let us zoom into the two variants of regression-kriging. e. 1 The Best Linear Unbiased Predictor of spatial data 33 vector of n observations at primary locations. 11) = CKED−1 · cKED 0 where λKED is the vector of solved weights, ϕp are the Lagrange multipliers, CKED is the 0 extended covariance matrix of residuals and cKED is the extended vector of covariances 0 at new location. 183):  C(s1 , s1 ) ..   . CKED ··· C(s1 , sn ) ..

Id is the resulting raster map (see Fig. 11 for an example). ts = krige(ev∼x+y+x*y+x*x+y*y, data=points, newdata=mapgrid) which can be converted to the moving surface fitting by adding a search window (Fig. ok = krige(ev∼1, data=points, newdata=mapgrid, model=vgm(psill=5, "Exp", range=1000, nugget=1)) 1 Sometimes an information that we are completely uncertain about a feature is better than a colorful but completely unreliable map. 1 The Best Linear Unbiased Predictor of spatial data Is the physical model known?

This way, the calculation of the new map can be significantly speed up. 5). 1 that the importance of points (in the case of ordinary kriging and assuming a standard initial variogram model) exponentially decreases with their distance from the point of interest. Typically, geostatisticians suggest that already first 30–60 closest points will be good enough to obtain stable predictions. 3 Almost all geostatistical packages implement the KED algorithm because it is mathematically more elegant and hence easier to program.

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