By Mikhail Kanevski; Michel Maignan
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Extra resources for Analysis and Modelling of Environmental Data
T fig. 6 137Cs data. Inverse distance squared interpolations. Search radii R=IO km (left) and R = 100 (right). In general the search radius and the power "p" can be estimated using cross val idation (leave-one-out method). 5. From this representation it is evident, that using the same and even simple method but changing the model dependent parameters it is possible to obtain qualitatively different results. 6. Because of the small search radius (I 0 km) there are some underestimated regions corresponding to white zones.
A decreasing trend from south to north can be detected. ANALYSIS AND MODELLlNG OF SPATIAL ENVIRONMENTAL AND POLLUTION DATA 26 The following step in the spatial aspect of data is to split the entire region into subregions and to carry out statistical analysis within subregions: moving window statistics analysis. Such analysis gives an overview of spatial distributions of basic statistical parameters: mean vaJue, variance, skewness, kurtosis. The spatial variability of mean value is also a good test for analysis of stationarities.
2. 14 Voronoi polygons of Briansk mes data and distribution of their areas. 2 Statistical description of monitoring networks. Morishita index There are many statistical indices used to characterise clustering of points [Cressie, 1993]. Here the Morishita index of dispersion is used to characterise quantitatively the clustering (non-homogeneity) of the monitoring network [Korvin, 1 992]. Morishita index ( M I) is implemented in Geostat Office. Gooynghted rra 1al 34 ANALYSIS AND MODELLING OF SPATIAL ENVIRONMENTAL AND POLLUTION DATA In order to compute the M I the region, in this case the entire region, is covered by a regular grid of equally sized cells and the following index is computed: I £ n,(n1 - l) ' ' "' - Q N (N - 1 ) where n; (i = l , 2 ...
Analysis and Modelling of Environmental Data by Mikhail Kanevski; Michel Maignan