Relative Edge Density of the Underlying Graphs Based on Proportional-Edge Proximity Catch Digraphs for Testing Bivariate Spatial Patterns (Technical Report)

TL;DR

This paper introduces a new graph-based statistical method using relative edge density of underlying graphs from proximity catch digraphs to test for bivariate spatial patterns, with proven asymptotic properties and application to clustering analysis.

Contribution

It proposes a novel approach based on relative edge density of underlying graphs for spatial pattern testing, extending previous digraph-based methods and analyzing its efficiency and asymptotic behavior.

Findings

AND-underlying graphs perform better for segregation detection.

OR-underlying graphs are more effective for association detection.

The method's asymptotic distribution facilitates efficient parameter selection.

Abstract

The use of data-random graphs in statistical testing of spatial patterns is introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for pattern testing. The approach used in this article is based on underlying graphs of a family of data-random digraphs which is determined by a family of parameterized proximity maps. The relative edge density of the AND- and OR-underlying graphs is used as the summary statistic, providing an alternative to the relative arc density and domination number of the digraph employed previously. Properly scaled, relative edge density of the underlying graphs is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of bivariate spatial clustering patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman asymptotic efficiency, hence selection of the proximity map parameter to optimize efficiency. Asymptotic efficiency and Monte Carlo simulation analysis indicate that the AND-underlying version is better (in terms of power and efficiency) for the segregation alternative, while the OR-underlying version is better for the association alternative. The approach presented here is also valid for data in higher dimensions.