基于karhunen - lo展开和最小生成树的监督空间区划

Ranadeep Daw, C. Wikle
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引用次数: 2

摘要

本文提出了一种空间域数据监督区域化的方法。在多个尺度上定义空间过程会导致著名的生态谬误问题。在这里,我们使用生态谬误作为最小化标准的基础,以获得预期区域。空间过程的karhunen - lo展开维持了多个分辨率实现之间的关系。具体来说,我们使用karhunen - lo展开式来定义区划误差,从而使生态谬误最小化。利用空间位置和数据形成的最小生成树实现连续区域化。然后,区域化变得类似于从最小生成树修剪边。通过模拟和实际数据实例对该方法进行了论证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervised Spatial Regionalization using the Karhunen-Loève Expansion and Minimum Spanning Trees
The article presents a methodology for supervised regionalization of data on a spatial domain. Defining a spatial process at multiple scales leads to the famous ecological fallacy problem. Here, we use the ecological fallacy as the basis for a minimization criterion to obtain the intended regions. The Karhunen-Loève Expansion of the spatial process maintains the relationship between the realizations from multiple resolutions. Specifically, we use the Karhunen-Loève Expansion to define the regionalization error so that the ecological fallacy is minimized. The contiguous regionalization is done using the minimum spanning tree formed from the spatial locations and the data. Then, regionalization becomes similar to pruning edges from the minimum spanning tree. The methodology is demonstrated using simulated and real data examples.
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