Abstract
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.
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- 1.
The classification is based on Industrial classification for national economic activities, GB/T 4754-2002, which is published by General Administration of Quality Supervision, Inspection and Quarantine of The People’s Republic of China in 2007.
- 2.
Due to the absence of firms of Manufacture of coke, refined petroleum products and nuclear fuel(C25), Manufacture of chemical fiber (C28) and Recycling (C43) in the original data, these three industries are omitted because the number of dimensions-above firms of the three industries in 2009 was only 12, 5, 1, respectively, according to the 2010 statistical yearbook of Shenzhen.
- 3.
The network cross K-function method is included in SANET. SANET stands for “Spatial Analysis along Networks”. It is a software package developed by Atsu Okabe and his group. Details about SANET can be found at http://sanet.csis.u-tokyo.ac.jp/.
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Acknowledgments
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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Tian, J., Xiong, Fq., Yan, F. (2015). Mining Co-location Patterns Between Network Spatial Phenomena. In: Harvey, F., Leung, Y. (eds) Advances in Spatial Data Handling and Analysis. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-19950-4_8
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DOI: https://doi.org/10.1007/978-3-319-19950-4_8
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