A Nonparametric Test Method for Identifying Significant Cross-outliers in Spatial Point Dataset

In the field of geography,a spatial outlier is an object whose non-spatial attribute value is significantly different from the values of its spatial neighbors. Detection of spatial outliers will be helpful to uncover special geographical phenomenon,so it has become an important branch of spatial data mining.Although existing methods are able to measure spatial outlier factor,the significance of these outliers can not be evaluated in an objective way. Furthermore,the existing methods are mainly designed for single class dataset,without taking into account the interaction between different categories of dataset.In this study,a nonparametric test was developed to identify the significant cross-outliers in spatial point dataset.Firstly,a reasonable and stable spatial neighborhood is constructed for the primary dataset entitys using the constraint Delaunay triangulation.Then,using the number of reference dataset entitys falling in the spatial reference neighbor radius to measure the initial outlier factor.Constructed the support domain by α-Shape method,the null model is constructed based on spatial randomness process,and the significant spatial cross-outliers are identified by statistical test.Finally,the stability of the spatial cross-outlliers are evaluated by the living distance.Experimentson on both simulated and real-world datasets show that the proposed permutation test is effective for determining significant spatial cross-outliers in spatial point datasets