By Mikhail Kanevski; Michel Maignan

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Mean and variance), the possibility of the monitoring network detecting the phenomena under study (spatial and dimensional resolution of the monitoring network), etc. For global estimations, recovery procedure is related to the declustering (weighting of raw data before statistical analysis). See examples below. In general, the spatial inhomogeneity of points can be characterised by di fferent geometrical/topological, statistical and fractal. indices. The general idea of a quantitative description of MN clustering is to compare given monitoring network with a regular or homogeneous networklpallern and to compute some measures ofsimilarity/dissimilarity between Jhe two.

Oedustered dm . -... ,-----: -:-� -- " " a JI 0 .. Q 0 0 ! Fig. 24 Histograms of raw and declu tercd data. Cell de<:lustcring algorilhms were used. 491 � Cs 137,sttp 4 � Cs 137, Sttp 3 576 661 831 491 661 Fig. 25 Sequential generation of homogeneous monitoring network based on original topology. 831 EXPLORATORY SPATIAL DATA Al ALYSIS. MO ITORING NETWORKS. 6 GEOSTAT OFFICE: MONJTORING NETWORKS AND DECLUSTERING The understanding of monitoring networks geometry/topology, statistical and fractal/multi fractal properties i s very important for the many reasons explained , .

When the cell size is very small only n; = l or n; = 0 points fall into the cell. Jn this case the Morishita index equals zero. When the cell size is very large and covers the entire region, the Morishita index is equal to l . High values of Morishita index reflect the fractality of monitoring networks. Detailed discussion the Morishita index and its connection with the fractal dimension is presented by Korvin ( 1992). The Morishita diagram for regularly distributed data starts from zero and monotonically reaches a value of I with some fluctuations.