Workman Austin, Song Joon Jin
Department of Statistical Science, Baylor University, Waco, TX, USA.
J Appl Stat. 2023 Aug 29;51(10):1946-1960. doi: 10.1080/02664763.2023.2249636. eCollection 2024.
Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide geostatistical methods for spatial prediction, predictive performance measure for prediction assessment, and visualization for mapping SIVD. The proposed methods are illustrated with both simulated and real examples.
符号数据分析处理具有符号对象的复杂数据，如列表、直方图和区间。符号数据的空间分析相对较少被探索。为了填补这一空白，本文提出了一种用于空间区间值数据（SIVD）分析的统计框架。我们提供了用于空间预测的地质统计方法、用于预测评估的预测性能度量以及用于绘制SIVD的可视化方法。通过模拟和实际示例对所提出的方法进行了说明。
