Studying of two Spatial Data Prediction Methods Performances

Document Type : Scientific Research

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Abstract

Correlation structure of spatial data is related to their positions and distances. Then spatial data analysis has various applications in applied areas.
In this research, we study two spatial interpolation methods, that is Ordinary Kriging and Universal Kriging. In this purpose, data sets are simulated and the performance of each of the methods is studied under the considered conditions. Besides, different sample sizes and variograms is considered to assess the effects of various sample sizes and different vaiogram functions. Furthermore, for every set of simulated data, cross validation criteria is applied to compare the methods. As an application of the results, the methods are applied for data of a mine.

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References

Alimohammadi, R. (2009). Comparison of Spline with Kriging in an Epidemiological Problem. Jp Journal of Biostatistics 3 (3), 187 – 193.
Cressie, N. (1993). Statistics for Spatial Data. Wiley. New York.
Krige, D.G. (1951). A Statistical Approach to Some Basic Mine Valuation Problem on the Witwatersand. Journal of the Chemical, Metallurgical and Mining Society of South Africa. 52(6). 119-139.
Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8). 1246-1266.
Matkan, A., Shakiba, A., Mirbagheri, B. & Tavoosi, H. (2010), A comparison between Kriging, Cokriging and Geographically Weighted Regression models for estimating rainfall over north west of Iran. EMS Annual Meeting Abstracts. Sep 2010. Zurich. Switzerland.
Stein, M.L. (2005). Statistical Methods for Regular Monitoring Data. Journal of Royal Statistical Society B, 67, 667-687.

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