Sampling and Kriging Spatial Means: Efficiency and ConditionsReport as inadecuate

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Institute of Geographic Sciences & Nature Resources Research, Chinese Academy of Sciences, Beijing, China


Department of Geography, San Diego State University, San Diego, CA, USA


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Abstract Sampling and estimation of geographical attributes that vary across space e.g., area temperature, urban pollution level, provincial cultivated land, regional population mortality and state agricultural production are common yet important constituents of many real-world applications. Spatial attribute estimation and the associated accuracy depend on the available sampling design and statistical inference modelling. In the present work, our concern is areal attribute estimation, in which the spatial sampling and Kriging means are compared in terms of mean values, variances of mean values, comparative efficiencies and underlying conditions. Both the theoretical analysis and the empirical study show that the mean Kriging technique outperforms other commonly-used techniques. Estimation techniques that account for spatial correlation dependence are more efficient than those that do not, whereas the comparative efficiencies of the various methods change with surface features. The mean Kriging technique can be applied to other spatially distributed attributes, as well. View Full-Text

Keywords: random field; mean Kriging; spatial dependence; GIS random field; mean Kriging; spatial dependence; GIS

Author: Jin-Feng Wang 1,* , Lian-Fa Li 1 and George Christakos 2



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