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A non‐parametric statistical test method to detect significant cross‐outliers in spatial points

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In spatial points that describe geographical events, outliers that deviate significantly from the global or local distribution indicate extraordinary geographical phenomena. Existing outlier detection methods cannot statistically identify significant outliers by considering the co‐occurrences of multiple categories of spatial points. Therefore, this study develops a non‐parametric statistical test method to detect significant cross‐outliers from two categories of spatial points divided into primary and reference ones. Firstly, the cross K function is employed to test the significance of co‐occurrences of the two categories of points, and the co‐occurrence intensity of each primary point is quantitatively defined as the number of reference points in its circular buffer. Then, the edge‐constrained Delaunay triangulation is utilized to construct a reasonable spatial neighborhood for each primary point. On this basis, the Monte Carlo method is utilized to simulate the distribution of reference points in the support domain of each primary point delineated using the α‐shape algorithm, and those primary points with significantly different local co‐occurrence intensity are determined as spatial cross‐outliers. Finally, those cross‐outliers obtained using different buffers are evaluated further by analysis of living intervals. Experiments on both simulated and real‐life datasets demonstrate the effectiveness and practicability of the proposed method.
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Document Type: Research Article

Publication date: December 1, 2018

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