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Additional resources for Methods in Applied Soil Microbiology and Biochemistry
E. the structure of a set of mixed data can more appropriately be described by a superposition of t graphs, so-called "completely labelled multigraphs". Lastly, the subjective decisions necessary for clustering multivariate data become more transparent to the users. They are directly involved in the decisions required by the method as to which dissimilarity measure should be used for which dimensions, or whether single or k-linkage classification would be appropriate. Some of the other clustering methods tend to be a "black box" where data is fed into a computer program and the user is largely detached from the analysis.
Whether a sampling consists of redundant data or has a lack of samples should be determined. In this way the geostatistical analysis enables the sampling design to be optimized (see Fig. 7, Y4 and 73). 3. In a further step the analysis of spatial autocorrelation can be used for a spatial prediction of the data under investigation. That means that this technique can be used for predicting (respectively interpolating) maps with the soil data. Steps 1 and 2 can be done with a relatively simple morphological analysis of the empirical function of the spatial covariance, called variogram.
The location of the measured values is shown by a black point. The place of a predicted value is indicated by a white point. The predicted lead concentrations in soil (site E) according to the method of Kriging are shown in Fig. 16. The grid geometry and shift to the predicted grid is identical to the parameters in the previous figure, where IDW+LOGRAN prediction results are given. Two aspects become obvious: the strong smoothing effect of Kriging and the effect of constant areas of influence (here radius = range) that are used by Kriging for interpolation.