自然地理要素空间插值的几个问题

Problems of the spatial interpolation of physical geographical elements

资源管理、灾害管理、生态环境治理以及全球变化研究的需要强化了部分自然地理要素空间插值研究的重要性。这些要素空间插值的核心是建立充分逼近要素空间分布特征的函数方程。对于给定的区域与要素样本值 ,插值函数可以有多种模型形式。各类模型的精度受其理论基础、模型算法、时空尺度效应、样本数据属性等因素的综合影响。通过对国际主要插值研究成果进行分析 ,文章认为各类模型插值精度的差异缘于模型对插值要素空间变异性与空间相关性的反映 ,具体应用中 ,只有对已知样本数据进行变异性与相关性分析才能选出适当的插值方法。

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.