Practical spatial statisics for areal interpolation
Practical spatial statisics for areal interpolation
复制标题
面积插值的实用空间统计
DOI：
10.1068/b38034t
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Tsutsumi Morito
中科院分区：
文献类型：
--
作者：
Murakami Daisuke;Tsutsumi Morito
空间数据之间空间单位的差异往往使分析复杂化。空间单位转换，称为面积插值，通常用于解决这个问题。在众多的面插值方法中，很少考虑空间数据的一般属性空间自相关性。本文将空间过程模型、空间统计学中的原始模型与基于线性回归的面插值方法相结合，构造了一种面插值方法。我们的方法的主要优点是双重的：它考虑了空间自相关和体积保持属性，它是比其他基于空间统计的面积插值方法更实用。最后，以企业员工数密度的面积插值为例，验证了该方法的有效性。该案例研究表明，我们的方法成功地提高了预测精度。此外，区域插值结果表明，我们的方法，它提供了一个光滑的插值地图，是适当的空间聚集数据的底层过程建模。这些结果表明，空间自相关的考虑是重要的面积插值。
Differences in spatial units among spatial data often complicate analyses. Spatial unit conversion, called areal interpolation, is often applied to address this problem. Of the many proposed areal interpolation methods, few consider spatial autocorrelation, which is the general property of spatial data. In this paper an areal interpolation method is constructed by combining a spatial process model, a primal model in spatial statistics, and the linear-regression-based areal interpolation method. The primal advantages of our methods are twofold: It considers both spatial autocorrelation and the volume-preserving property; it is more practical than other spatial-statistics-based areal interpolation methods. A case study on the areal interpolation of the density of employee numbers is provided to check the properties of our method. This case study shows that our method succeeds in improving predictive accuracy. Furthermore, the areal interpolation result indicates that our method, which provides a smooth interpolation map, is appropriate to model the underlying process of spatially aggregated data. These results indicate that the consideration of spatial autocorrelation is important for areal interpolation.