Many techniques have been developed to quantify different conceptualizations of self-interaction and patterns within spatial data. We propose a new metric and related algorithm that describes the geometric spatial disorder of geographic point sets, the “Index of Disorder” (IoD). The IoD algorithm was applied to synthetic and natural datasets and was shown to be able to differentiate between areas of high spatial disorder (randomly placed points) and low spatial disorder (e.g., curvilinear grids, wallpaper groups, and other repeating patterns). Because the IoD is a quantitative metric, it can be used on its own as an aid for identifying areas of unusually high or low spatial disorder or as enrichment for machine learning classification algorithms.

已经开发出许多技术来量化空间数据中自我交互和模式的不同概念。我们提出了一种新的度量标准和相关算法，该算法描述了地理特征集的几何空间无序性，即“失调指数”（IoD）。IoD算法已应用于合成数据集和自然数据集，并被证明能够区分高空间无序（随机放置的点）和低空间无序（例如曲线网格，墙纸组和其他重复图案）的区域。由于IoD是定量指标，因此它可以单独用作识别异常高或低空间无序区域的辅助工具，也可以用作机器学习分类算法的补充。