On the Use of Nearest Neighbor Contingency Tables for Testing Spatial Segregation

TL;DR

This paper evaluates and compares various nearest neighbor contingency table (NNCT) tests for spatial segregation, introduces one-sided Pielou's tests, and extends their application to testing complete spatial randomness, with extensive simulations and real data examples.

Contribution

It compares NNCT-tests, introduces one-sided Pielou's tests, extends NNCT use for CSR testing, and assesses edge effects with correction methods.

Findings

Dixon's tests are suitable for CSR independence testing.

Pielou's tests are liberal for CSR and RL testing.

Edge correction methods impact test performance.

Abstract

For two or more classes (or types) of points, nearest neighbor contingency tables (NNCTs) are constructed using nearest neighbor (NN) frequencies and are used in testing spatial segregation of the classes. Pielou's test of independence, Dixon's cell-specific, class-specific, and overall tests are the tests based on NNCTs (i.e., they are NNCT-tests). These tests are designed and intended for use under the null pattern of random labeling (RL) of completely mapped data. However, it has been shown that Pielou's test is not appropriate for testing segregation against the RL pattern while Dixon's tests are. In this article, we compare Pielou's and Dixon's NNCT-tests; introduce the one-sided versions of Pielou's test; extend the use of NNCT-tests for testing complete spatial randomness (CSR) of points from two or more classes (which is called \emph{CSR independence}, henceforth). We assess the finite sample performance of the tests by an extensive Monte Carlo simulation study and demonstrate that Dixon's tests are also appropriate for testing CSR independence; but Pielou's test and the corresponding one-sided versions are liberal for testing CSR independence or RL. Furthermore, we show that Pielou's tests are only appropriate when the NNCT is based on a random sample of (base, NN) pairs. We also prove the consistency of the tests under their appropriate null hypotheses. Moreover, we investigate the edge (or boundary) effects on the NNCT-tests and compare the buffer zone and toroidal edge correction methods for these tests. We illustrate the tests on a real life and an artificial data set.