Authors:
Amera Najem Obaid,Mohammed Jasim Mohammed,DOI NO:
https://doi.org/10.26782/jmcms.2020.04.00009Keywords:
Geostatistics ,Deconvolution,Change the support,Interpolatio,Abstract
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 countryRefference:
I. A. Soares et al. (eds.), geo ENV VI – Geostatistics for Environmental Applications, 3-22 , Springer Science& Business Media,vol .15 ,2008.
II. A .Minh, Muhajarine N, Janus M, Brownell M, Guhn M. A review of neighborhood effects and early child development: how, where, and for whom, do neighborhoods matter? Health & place;46:155 -74, 2017.
III. Atkinson, Peter, Jingxiong Zhang, and Michael F. Goodchild. Scale in spatial information and analysis. CRC Press, 2014.
IV. B .Ghosh, Random distances within a rectangle and between two rectangles.Bull. Calcutta Math. Soc. 43, 17- 24, 1951.
V. CA .Gotway, Young LJ , Combining incompatible spatial data. J Am Stat Assoc 97(459):632-648,2002.
VI. Duan, Qingyun, Soroosh Sorooshian, and Vijai Gupta. “Effective and efficient global optimization for conceptual rainfall-runoff models.” Water resources research 28.4: 1015-1031, 1992.
VII. Gotway Crawford C.A., Young L.J. , Change of support: an inter-disciplinary challenge. In: Geostatistics for Environmental Applications. Springer, Berlin, Heidelberg, 2005.
VIII. Gottschalk, Lars, Etienne Leblois, and Jon Olav Skøien. “Distance measures for hydrological data having a support.” Journal of hydrology 402.3-4 ,415-421,2011.
IX. G. Journel, A.., Huijbregts, C.J., Mining Geostatistics. Academic Press, London, UK, 1978.
X. H .Kupfersberger, Deutsch CV, Journel AG Deriving constraints on small-scale variograms due to variograms of large-scale data. Math Geol 30(7):837-852, 1998.
XI. Kyriakidis, P., A, geostatistical framework for area-to-point spatial interpolation. Geograph. Anal. 36 (3), 259 -289,2004 .
XII. N.Cressie, Statistics for Spatial Data. Wiley, a Wiley-Interscience-Publication, New York, NY, 1991.
XIII. O. Skøien, J., Merz, R., Blöschl, G., Top-kriging – geostatistics on stream network s. Hydrol. Earth Syst. Sci. 10, 277- 287, 2006 .
XIV. O .Skoien, J, Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., Viglione, A., Rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67, 2014.
XV. P .Goovaerts,, “Kriging and semivariogram deconvolution in the presence of irregular geographical units”. Math. Geosci. 40 (1), 101 – 128 , 2008.
XVI. www.unicef.org