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New Tests of Spatial Segregation Based on Nearest Neighbour Contingency Tables

机译:基于最近邻居列联表的空间隔离新测试

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The spatial clustering of points from two or more classes (or species) has important implications in many fields and may cause segregation or association, which are two major types of spatial patterns between the classes. These patterns can be studied using a nearest neighbour contingency table (NNCT) which is constructed using the frequencies of nearest neighbour types. Three new multivariate clustering tests are proposed based on NNCTs using the appropriate sampling distribution of the cell counts in a NNCT. The null patterns considered are random labelling (RL) and complete spatial randomness (CSR) of points from two or more classes. The finite sample performance of these tests are compared with other tests in terms of empirical size and power. It is demonstrated that the newly proposed NNCT tests perform relatively well compared with their competitors and the tests are illustrated using two example data sets.
机译:来自两个或多个类别(或物种)的点的空间聚类在许多领域都具有重要意义,并且可能导致隔离或关联,这是类别之间的两种主要空间格局。可以使用最近邻居偶发表(NNCT)研究这些模式,该表使用最近邻居类型的频率构建。基于NNCT,使用NNCT中细胞计数的适当采样分布,提出了三种新的多元聚类测试。所考虑的空模式是来自两个或更多类的点的随机标记(RL)和完全空间随机性(CSR)。这些测试的有限样本性能在经验大小和功效方面与其他测试进行了比较。事实证明,新提出的NNCT测试与竞争对手相比性能相对较好,并且使用两个示例数据集对测试进行了说明。

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