Discovering Spatial Interdependencies from Mobile Phone Data and Transportation Data: Evidence from Guizhou Province

F. J. Long (longfj@tsinghua.edu.cn), L. F. Zheng (zhenglf17@mails.tsinghua.edu.cn) and L. Shi
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F. J. Long: Guizhou Institute of Technology
L. F. Zheng: Tsinghua University
L. Shi: Tsinghua University

A chapter in Proceedings of the 23rd International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 1246-1255 from Springer

Abstract: Abstract With the continuous urbanization of China, more and more attention has been paid to the balanced development of different regions at all levels, which demands that the government clarify spatial interdependencies before making plan. Previous studies focused more on information or physical space, and few studied the interaction between them. From a new perspective, this paper involves mobile phone data and transportation data in discovering interdependencies between different regions in the context of information space and physical space. First, we establish two networks composed of 81 counties in Guizhou province. Next, both the physical connection and the information connection between these counties are clarified by measuring the call flow and traffic flow in each network. The 81 counties are then divided into several communities according to the complex network theory. The results break through the administrative division and truly reflect the hierarchy and spatial structure of the urban network system of Guizhou province. The correlation between traffic and information connection is also presented, and we confirm that the attenuation with distance of information flow is significantly smaller than the traffic flow. As an attempt to analyze the interdependencies between different regions with big data, the general framework of our research can be extended to a larger scope if the data are available.

Keywords: Spatial interdependencies; Mobile phone data; Transportation data; Urban network system; Complex network theory (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-981-15-3977-0_96

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