Mining Co-location Patterns Between Network Spatial Phenomena
The mining of co-location patterns is a popular issue in the field of spatial data mining. However, little attention has been paid to the co-location patterns between network spatial phenomena. This paper addresses this issue by extending an existing method to mining the co-location patterns between network spatial phenomena. The approach consists of two stages: (1) defining a co-location model on a network space based on skeleton partitioning of a road network to have co-occurrence relationships; (2) computing statistical diagnostics for these co-occurrence relationships. Our method was then applied to a case study regarding the mining of co-location patterns of manufacturing firms in Shenzhen City, China. These co-location patterns were also analyzed qualitatively according to the three mechanisms derived from agglomeration economies. Our method was compared with the existing method and the differences were verified by the network cross K-function.
KeywordsNetwork spatial phenomena Co-location patterns Manufacturing firms Agglomeration Network cross K-function
The authors greatly appreciate the helpful comments of two anonymous reviewers. The authors also want to thank Dr. Okabe and his group for providing the program package, SANET, which allowed the calculation of the network cross K-function in this research. The work presented in this paper was supported by National Science Foundation for Fostering Talents in Basic Research of the National Natural Science Foundation of China (Grant No. J1103409) and by Innovation and Entrepreneurship Training Project for College Students of Wuhan University (Grant No.S2014438).
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