  Spatial interaction between two or more classes or species has important
implications in various fields and causes multivariate patterns such as
segregation or association. Segregation occurs when members of a class or
species are more likely to be found near members of the same class or
conspecifics; while association occurs when members of a class or species are
more likely to be found near members of another class or species. The null
patterns considered are random labeling (RL) and complete spatial randomness
(CSR) of points from two or more classes, which is called \emph{CSR
independence}, henceforth. The clustering tests based on nearest neighbor
contingency tables (NNCTs) that are in use in literature are two-sided tests.
In this article, we consider the directional (i.e., one-sided) versions of the
cell-specific NNCT-tests and introduce new directional NNCT-tests for the
two-class case. We analyze the distributional properties; compare the empirical
significant levels and empirical power estimates of the tests using extensive
Monte Carlo simulations. We demonstrate that the new directional tests have
comparable performance with the currently available NNCT-tests in terms of
empirical size and power. We use four example data sets for illustrative
purposes and provide guidelines for using these NNCT-tests.
