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How Different Are Population Movements between Weekdays and Weekends: A Complex-Network-Based Analysis on 36 Major Chinese Cities

Author

Listed:
  • Chengyue Zhang

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China)

  • Minmin Li

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518060, China
    Technology Innovation Center of Territory & Spatial Big Data, MNR & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China)

  • Ding Ma

    (Research Institute for Smart Cities, Shenzhen University, Shenzhen 518000, China)

  • Renzhong Guo

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
    Research Institute for Smart Cities, Shenzhen University, Shenzhen 518000, China)

Abstract

With the rapid development of Information and Communications Technology (ICT) and transportation infrastructure, the flows of people between cities have become the cornerstone of shaping regional integration. Although research studies about the movement of people have aroused widespread interest in academia, research about the temporal and spatial dynamics of daily mobility between cities is sparse, which is called the temporal heterogeneity of mobility between cities. This research aims to study the temporal and spatial changes (Heterogeneity) of population mobility between cities, using big data obtained through China Unicom, in terms of mapping the spatial network of population mobility and complex network analysis, from which the following findings emerge: (1) On weekends, the gap between cities in the number of floating population flow and the capacity of transferring population has become smaller, indicating that there is better coordination between cities on weekends. (2) There are huge differences in population flow between cities, which reflects the imbalance of urban development, population is more concentrated in cities with higher level of development. (3) The heterogeneity of population flow between cities at weekdays and weekends is closely related to the city’s hierarchy, which can help us study the hierarchical structure of China’s cities from a dynamic perspective. The paper emphasizes the importance of researching heterogeneity issues, clarifies the difference between the heterogeneity of weekdays and weekends and the heterogeneity involved in previous population research fields in terms of population flow and deficiencies in research.

Suggested Citation

  • Chengyue Zhang & Minmin Li & Ding Ma & Renzhong Guo, 2021. "How Different Are Population Movements between Weekdays and Weekends: A Complex-Network-Based Analysis on 36 Major Chinese Cities," Land, MDPI, vol. 10(11), pages 1-14, October.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1160-:d:668553
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    References listed on IDEAS

    as
    1. Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    4. Jiwei Li & Qingqing Ye & Xuankai Deng & Yaolin Liu & Yanfang Liu, 2016. "Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
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