By Tomislav Hengl
The aim of this consultant is to aid you in generating caliber maps by utilizing absolutely operational open resource software program programs.
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Additional info for A Practical Guide to Geostatistical Mapping
3) ✷✺ where z(si ) is the value of a target variable at some sampled location and z(si + h) is the value of the neighbor at distance si + h. Suppose that there are n point observations, this yields n · (n − 1)/2 pairs for which a semivariance can be calculated. We can then plot all semivariances versus their separation distances, which will produce a variogram cloud as shown in Fig. 9b. Such clouds are not easy to describe visually, so the values are commonly averaged for a standard distance called the “lag”.
Another approach to make predictions from polygon maps is to use multiple regression. In this case, the predictors (mapping units) are used as indicators: zˆ(s0 ) = ˆb1 · MU 1 (s0 ) + . . 23) are in fact equivalent. If, on the other hand, the residuals do show spatial auto-correlation, the predictions can be obtained by stratified kriging. , 1998). Note that the strata or sub-areas need to be known a priori and they should never be derived from the data used to generate spatial predictions. 4 Hybrid models Hybrid spatial prediction models comprise of a combination of the techniques listed previously.
This is because both approaches deal with different aspects of spatial variation: regression deals with the deterministic and kriging with the spatially-correlated stochastic part of variation. The biggest criticism of the pure regression approach to spatial prediction is that the position of points in geographical space is completely ignored, both during model fitting and prediction. Imagine if we are dealing with two point data sets where one data set is heavily clustered, while the other is well-spread over the area of interest — a sophistication of simple non-spatial regression is needed to account for the clustering of the points so that the model derived using the clustered points takes this property into account.
A Practical Guide to Geostatistical Mapping by Tomislav Hengl