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Geochemical mapping of risk element concentrations in soils is performed incountries around the world. It results in large datasets of high analyticalquality, which can be used to identify soils that violate individuallegislative limits for safe food production. However, there is a lack ofadvanced data mining tools that would be suitable for sensitive exploratorydata analysis of big data while respecting the natural variability of soilcomposition. To distinguish anthropogenic contamination from natural variation,the analysis of the entire data distributions for smaller sub-areas is key. Inthis article, we propose a new data mining method for geochemical mapping databased on functional data analysis of probability density functions in theframework of Bayes spaces after post-stratification of a big dataset to smallerdistricts. Proposed tools allow us to analyse the entire distribution, goingbeyond a superficial detection of extreme concentration anomalies. Weillustrate the proposed methodology on a dataset gathered according to theCzech national legislation (1990--2009). Taking into account specificproperties of probability density functions and recent results for orthogonaldecomposition of multivariate densities enabled us to reveal real contaminationpatterns that were so far only suspected in Czech agricultural soils. Weprocess the above Czech soil composition dataset by first compartmentalising itinto spatial units, in particular the districts, and by subsequently clusteringthese districts according to diagnostic features of their uni- and multivariatedistributions at high concentration ends. Comparison between compartments iskey to the reliable distinction of diffuse contamination. In this work, we usedsoil contamination by Cu-bearing pesticides as an example for empirical testingof the proposed data mining approach.

Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements