Amera Najem Obaid,Mohammed Jasim Mohammed,




Geostatistics ,Deconvolution,Change the support,Interpolatio,


The incidences of diseases (morbidities) vary across geographic areas. Spatial statistical analysis concerning spread  and  direction  is useful to study  such diseases in the neighborhood. This helps the health provenance for reducing this disease and control spatially it. Many spatial interpolations have employed for predicting the risky diseases based on observed values. In this paper, two methods of the spatial interpolation have studied based on unmeasured values from the same characteristic of spatial data, area-to-point kriging and topological kriging. These methods exploit variogram structure to predict the unmeasured values, then they fit this variogram by one of the parametric variograms. The de-regularization or deconvolution method is iterative and search model of area that reduces the variation between the theoretical semivariogram model and the fitted model for irregular area data. However, it is an approximate method for different regions based on the concept of average distance between irregular areas. Then, area to point kriging method has used using back calculation for approximated irregular areas in topological kriging (top kriging) .The prediction results for top kriging is better than other method. Disease krige map explaining the embedding risk of effective disease from observed frequencies are summarizes and their performances have compared .The goal of this paper is  mapping and exploring the spatial variation and hot spots of district- level disease cases in Iraq country


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