Rethinking the null hypothesis in significant colocation pattern mining of spatial flows

Mengjie Zhou (), Mengjie Yang, Tinghua Ai, Jiannan Cai and Zhe Chen
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Mengjie Zhou: Hunan Normal University
Mengjie Yang: Hunan Normal University
Tinghua Ai: Wuhan University
Jiannan Cai: The Chinese University of Hong Kong
Zhe Chen: Hunan Normal University

Journal of Geographical Systems, 2024, vol. 26, issue 3, No 5, 375-405

Abstract: Abstract Spatial flows represent spatial interactions or movements. Mining colocation patterns of different types of flows may uncover the spatial dependences and associations among flows. Previous studies proposed a flow colocation pattern mining method and established a significance test under the null hypothesis of independence for the results. In fact, the definition of the null hypothesis is crucial in significance testing. Choosing an inappropriate null hypothesis may lead to misunderstandings about the spatial interactions between flows. In practice, the overall distribution patterns of different types of flows may be clustered. In these cases, the null hypothesis of independence will result in unconvincing results. Thus, considering the overall spatial pattern of flows, in this study, we changed the null hypothesis to random labeling to establish the statistical significance of flow colocation patterns. Furthermore, we compared and analyzed the impacts of different null hypotheses on flow colocation pattern mining through synthetic data tests with different preset patterns and situations. Additionally, we used empirical data from ride-hailing trips to show the practicality of the method.

Keywords: Colocation pattern; Spatial flow; Statistical significance test; Null hypothesis; Random labeling (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10109-024-00439-y

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