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The spatial interaction between two or more classes (or species) hasimportant consequences in many fields and might cause multivariate clusteringpatterns such as segregation or association. The spatial pattern of segregationoccurs when members of a class tend to be found near members of the same class(i.e., conspecifics), while association occurs when members of a class tend tobe found near members of the other class or classes. These patterns can betested using a nearest neighbor contingency table (NNCT). The null hypothesisis randomness in the nearest neighbor (NN) structure, which may result from --among other patterns -- random labeling (RL) or complete spatial randomness(CSR) of points from two or more classes (which is called the CSR independence,henceforth). In this article, we consider Dixon's class-specific tests ofsegregation and introduce a new class-specific test, which is a newdecomposition of Dixon's overall chi-square segregation statistic. Wedemonstrate that the tests we consider provide information on different aspectsof the spatial interaction between the classes and they are conditional underthe CSR independence pattern, but not under the RL pattern. We analyze thedistributional properties and prove the consistency of these tests; compare theempirical significant levels (Type I error rates) and empirical power estimatesof the tests using Monte Carlo simulations. We demonstrate that the newclass-specific tests also have comparable performance with the currentlyavailable tests based on NNCTs in terms of Type I error and power estimates.For illustrative purposes, we use three example data sets. We also provideguidelines for using these tests.

Class-Specific Tests of Spatial Segregation Based on Nearest Neighbor Contingency Tables