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Assessment of regression kriging for spatial interpolation - comparisons of seven GIS interpolation methods

机译：空间插值的回归克里格法评估-七种GIS插值方法的比较。

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摘要

As an important GIS function, spatial interpolation is one of the most often used geographic techniques for spatial query, spatial data visualization, and spatial decision-making processes in GIS and environmental science. However, less attention has been paid on the comparisons of available spatial interpolation methods, although a number of GIS models including inverse distance weighting, spline, radial basis functions, and the typical geostatistical models (i.e. ordinary kriging, universal kriging, and cokriging) are already incorporated in GIS software packages. In this research, the conceptual and methodological aspects of regression kriging and GIS built-in interpolation models and their interpolation performance are compared and evaluated. Regression kriging is the combination of multivariate regression and kriging. It takes into consideration the spatial autocorrelation of the variable of interest, the correlation between the variable of interest and auxiliary variables (e.g., remotely sensed images are often relatively easy to obtain as auxiliary variables), and the unbiased spatial estimation with minimized variance. To assess the efficiency of regression kriging and the difference between stochastic and deterministic interpolation methods, three case studies with strong, medium, and weak correlation between the response and auxiliary variables are compared to assess interpolation performances. Results indicate that regression kriging has the potential to significantly improve spatial prediction accuracy even when using a weakly correlated auxiliary variable.
机译：作为GIS的一项重要功能，空间插值是GIS和环境科学中用于空间查询，空间数据可视化以及空间决策过程的最常用地理技术之一。但是，尽管有许多GIS模型，包括反距离权重，样条曲线，径向基函数和典型的地统计模型（即普通克里金法，通用克里金法和共同克里金法），但对可用空间插值方法的比较却很少关注。已经包含在GIS软件包中。在这项研究中，比较和评估了回归克里金法和GIS内置插值模型的概念和方法方面以及它们的插值性能。回归克里金法是多元回归和克里金法的结合。它考虑了关注变量的空间自相关，关注变量与辅助变量之间的相关性（例如，相对容易获得遥感图像作为辅助变量）以及方差最小化的无偏空间估计。为了评估回归克里金法的效率以及随机和确定性插值方法之间的差异，比较了响应变量和辅助变量之间具有强，中和弱相关性的三个案例研究，以评估插值性能。结果表明，即使使用弱相关辅助变量，回归克里金法也有可能显着提高空间预测精度。

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