Adaptive detection of statistically significant regional spatial co-location patterns. (March 2018)

• Record Type: Journal Article
• Authors: Cai, Jiannan; Liu, Qiliang; Deng, Min; Tang, Jianbo; He, Zhanjun
• Is Part Of: Computers, environment and urban systems. Volume 68(2018)
• Page Start: 53
• Page End: 63
• Publication Date: 2018-03
• Subjects: Spatial heterogeneity -- Regional spatial co-location patterns -- Significance test -- Non-parametric -- Adaptive spatial clustering; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2017.10.003
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Regional spatial co-location patterns refer to subsets of spatial features that often co-occur in close geographical proximity in certain localities of space. Discovering regional spatial co-location patterns is still very challenging because it is difficult to specify appropriate thresholds for prevalence measures without prior knowledge and to detect natural localities of regional spatial co-location patterns automatically. On that account, an adaptive method is proposed in this study. First, a non-parametric significance test is constructed to evaluate the prevalence of spatial co-location patterns. Then, an adaptive pattern clustering approach is developed to detect hotspots of each candidate regional spatial co-location pattern. Finally, all statistically significant regional spatial co-location patterns and their localities are detected by iteratively expanding these hotspots. Comparisons between this adaptive method and two state-of-the-art methods are carried out with both simulated and ecological datasets (i.e. a wetland species dataset in northeast China). Experiments show that the proposed adaptive method allows detecting regional spatial co-location patterns effectively and with less prior knowledge than the state-of-the-art methods. Highlights: The prevalence of regional co-location patterns is statistically evaluated. Localities of regional spatial co-location patterns are detected in an automatic way. The subjectivity in discovery of regional spatial co-location patterns is significantly reduced.