By Hengl, Tomislav
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Additional resources for A Practical Guide to Geostatistical Mapping of Environmental Variables
Multiple simulations can be generated by increasing the number set for this argument. g. g. radius=1000 or nmax=60. 2 Universal kriging, kriging with external drift The geostatistical literature uses many different terms for what are essentially the same or at least very similar techniques. This confuses the users and distracts them from using the right technique for their mapping projects. In this section, we will show that both universal kriging, kriging with external drift and regregression-kriging are basically the same technique.
C(sn , s1 ) · · · C(sn , sn ) 1 C(s0 , sn ) 1 ··· 1 0 1 w1 (s0 ) .. . 5) ϕ where ϕ is the so-called Langrange multiplier . In addition to estimation of values at new locations, a statistical spatial prediction technique offers a measure of associated uncertainty of making these estimations by using a given model. e. the estimated variance of the prediction error. 5). As you can notice, outputs from any statistical prediction model are always two maps: (1) predictions and (2) prediction variance.
Some disadvantages of regression trees, on the other hand, is that they require large datasets and completely ignore spatial position of the input points. A common regression-based approach to spatial prediction is the multiple linear regression (Draper and Smith, 1998). 2)), this time by averaging the predictors: p zˆOLS (s0 ) = ˆb0 + ˆb1 · q1 (s0 ) + . . 15) where q is the matrix of predictors (n×p+1) and z is the vector of sampled observations. 16) To avoid confusion with geographical coordinates, we use the symbol q, instead of the more common x, to denote a predictor.