A Practical Guide to Geostatistical Mapping by Tomislav Hengl

By Tomislav Hengl

The aim of this consultant is to help you in generating caliber maps through the use of totally operational open resource software program applications.

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Common explanatory environmental predictors used to map environmental variables are land surface parameters, remotely sensed images, and geological, soil and land-use maps (McKenzie and Ryan, 1999). , 2007a). Functional relations between environmental variables and factors are in general unknown and the correlation coefficients can differ for different study areas, different seasons and different scales. However, in 22 In practice, often >95% of weights will be explained by the nearest 30–50 points.

15) ✼ ✽ ✾ where q is the matrix of predictors (n × p + 1) and z is the vector of sampled observations. 17) n−2 ✶✷ ✶✸ ✶✹ and q0 is the vector of predictors at new, unvisited location. 18) i=1 ✶✺ ✶✻ ✶✼ ✶✽ where v is the curvature of the confidence band around the regression line. 570). It can be seen from Eq. ) ‘Distance’ of the new observation from the centre of the feature space q(s0 ) − q¯ . ✷✷ ✷✸ ✷✹ q(si ) − q¯ 2 ; So in general, if the model is linear, we can decrease the prediction variance if we increase the spreading of the points in feature space.

E. an array with rows×columns number of grid nodes over the domain of interest (Fig. 8): z = z(s j ), j = 1, . . 3) ✶✺ ✶✻ ✶✼ ✶✽ ✶✾ ✷✵ ✷✶ ✷✷ ✷✸ ✷✹ ✷✺ ✷✻ ✷✼ ✷✽ ✷✾ where z is the data array, z(s j ) is the value at the grid m node s j , and m is the total number of grid nodes. Fig. 8: Spatial prediction implies application of a prediction Note that there is a difference between predicting algorithm to an array of grid nodes (point á point spatial prevalues at grid node (punctual) and prediction val- diction).

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