Sample data visualisation with the help of Voronoi polygons is presented in Fig. The points co-ordinates were preliminary transformed from geographic system to local Lambert projection. Histogram of Voronoi polygons squares describes homogeneity of the network. For uniform network Morisita index increases to 1 with the growth of cell size. Fractal dimension and spatial resolution of the monitoring network should be enough to detect the considered spatial phenomena.
Fractal dimension is calculated with sand-box or box counting methods.
For 90 Sr monitoring network in the part of Briansk region fractal dimension equals 1.6. Declustering procedure allows to obtain correct summary statistics. The dependence of weighted mean from the cell size for cell declustering is presented in Fig. 5. It completes the monitoring network to make it more homogeneous honouring the initial sampling. The generator is based on triangulation a point is added into triangle of initial samples if its sides do not accede a critical length.