A New Family of Random Graphs for Testing Spatial Segregation

TL;DR

This paper introduces a novel graph-based statistical test using proximity catch digraphs to detect spatial segregation or association among points, with proven asymptotic properties and validated through simulations.

Contribution

It develops a new family of random graphs, the proximity catch digraphs, for testing spatial point pattern randomness against segregation and association, including multi-dimensional data.

Findings

The relative density of PCDs follows a $U$-distribution asymptotically.

Monte Carlo simulations demonstrate good finite sample performance.

The method is applicable to multi-dimensional spatial data.

Abstract

We discuss a graph-based approach for testing spatial point patterns. This approach falls under the category of data-random graphs, which have been introduced and used for statistical pattern recognition in recent years. Our goal is to test complete spatial randomness against segregation and association between two or more classes of points. To attain this goal, we use a particular type of parametrized random digraph called proximity catch digraph (PCD) which is based based on relative positions of the data points from various classes. The statistic we employ is the relative density of the PCD. When scaled properly, the relative density of the PCD is a $U$-statistic. We derive the asymptotic distribution of the relative density, using the standard central limit theory of $U$-statistics. The finite sample performance of the test statistic is evaluated by Monte Carlo simulations, and the asymptotic performance is assessed via Pitman's asymptotic efficiency, thereby yielding the optimal parameters for testing. Furthermore, the methodology discussed in this article is also valid for data in multiple dimensions.