Population-density estimation using regression and area-to-point residual kriging
Population-density estimation using regression and area-to-point residual kriging
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DOI：
10.1080/13658810701492225
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发表时间：
2008-01-01
影响因子：
5.7
通讯作者：
Goodchild, M. F.
中科院分区：
文献类型：
--
作者：
Liu, X. H.;Kyriakidis, P. C.;Goodchild, M. F.
人口普查数据与若干分析和制图问题有关。利用遥感协变量的回归模型已被用于估计城市人口密度，但效果可能不令人满意。本文描述了一种基于kriging的面插值方法，即area-to-point残差kriging，它可以对回归后的残差进行分解。与传统的共克里格法相比，面积到点的残差克里格法要简单得多，因为它只需要一个点残差的半变差模型，而不需要一组包含因变量和所有协变量的自和交叉半变差模型。此外，区域到点的残差克里格明确地解释了源数据和目标值之间的任何尺度差异。该方法通过将人口从普查单位分解到其中的土地使用区域来说明。采用和不采用区域到点残差克里格回归的对比结果表明，区域到点残差克里格可以显著提高插值精度。
Census population data are associated with several analytical and cartographic problems. Regression models using remote-sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging-based areal interpolation method, namely area-to-point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area-to-point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto- and cross-semivariogram models involving the dependent variable and all the covariates. In addition, area-to-point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land-use zones within them. Comparative results for regression with and without area-to-point residual kriging show that area-to-point residual kriging can substantially improve interpolation accuracy.