An index for quantifying geometric point disorder in geospatial applications

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. •The locational structure in data is an additional dimension of spatial analysis.•Anthropogenic structures are ordered compared to organically occurring structures.•The Index of Disorder (IoD) quantifies this so-called geometric disorder.•The IoD can differentiate anthrophonic and naturally occurring structures. The spatial disorder of any arbitrary point in a set of points can be quantified by comparing the relative positions of that point's neighbors to the relative positions of its neighbors' neighbors.