Problems of the spatial interpolation of physical geographical elements

Abstract

The spatial interpolation of some physical geographical elements is becoming increasingly important nowadays in resources management, disaster control, environment improvement and the research of global change. The core of the spatial interpolation of those elements is to seek the functions that can reveal their characteristics of spatial distribution. But, as for specified regions and sample data, there are many functions in the list for choice. And the best choice is difficult to make because of the complex effects from theoretical foundation, algorithm, temporal spatial scale, and attributes of sample data. By referring to the major achievements in the interpolation research field, this paper comes to the point that the accuracy of certain spatial interpolation depends on its capability of reflecting the element's spatial variance and spatial correlation. Models using other elements as variables, when regression variable has high correlation with interpolation variable, will give more accurate results than others, because they have better reflection of spatial variance. Models without other element variables change in accuracy according to their consideration of the anisotropic characteristics or not. With spatial temporal scales' variance, the disposed spatial variance and correlations will be different, which affects the interpolation accuracy. The density, spatial distribution, data extent of sample points also makes the interpolation results different for the same reason. As for applications, the optimal interpolation method should be picked out after the analysis of those spatial characteristics embedded in the sample dataset.