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Research on Network Patterns and Influencing Factors of Population Flow and Migration in the Yangtze River Delta Urban Agglomeration, China

Author

Listed:
  • Xuewei Wang

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
    School of Geography and Planning, Cardiff University, Cardiff CF 10 3WA, UK)

  • Shuangli Ding

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China)

  • Weidong Cao

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China)

  • Dalong Fan

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China)

  • Bin Tang

    (School of Architecture, Southeast University, Nanjing 210096, China)

Abstract

Through the construction of a population flow and migration relationship matrix, this paper analyzes population flow and migration in the Yangtze River Delta urban agglomeration during the Spring Festival travel rush and daily period. This paper also studies the urban network spatial structure characteristics and the influencing factors from the perspective of inter-provincial population flow and migration. The results show the following: (1) as a central city, Shanghai has a significant siphon effect, with Suzhou, Nanjing, Hangzhou, Ningbo, Wuxi and Changzhou accumulating 86.95% of the incoming population. The Shanghai–Jiangsu cross-border floating population is active and accounts for 40.83% of the total mobility scale in the same period. The population flow and migration network in the Yangtze River Delta urban agglomeration shows obvious hierarchical characteristics. The secondary network relationship during the Spring Festival travel rush is the main migration path, while the first-level network relationship in the daily period is the main flow path. (2) Three indicators, namely, the network density, mean centrality, and control force based on the population flow and migration, consistently show that the Yangtze River Delta urban agglomeration network presents a strong connection state with the formation of a local cluster structure, highlighting that the city tightness in terms of population flow and migration also has dual attributes, which refers to “the restriction of the geographic space effect” and “overcoming the friction of space”. (3) Economic scale, political resources, industrial structure, and the historical basis are important factors influencing the formation of population flows and migration networks. Employment opportunities and labor wages are key guiding factors of the population migration direction, and spatial distance is a conditional factor influencing the formation of population flows and migration networks. The inter-provincial boundary, temporal distance, and transboundary frequency are the decisive factors for the formation of network patterns of population flow and migration.

Suggested Citation

  • Xuewei Wang & Shuangli Ding & Weidong Cao & Dalong Fan & Bin Tang, 2020. "Research on Network Patterns and Influencing Factors of Population Flow and Migration in the Yangtze River Delta Urban Agglomeration, China," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6803-:d:402338
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    2. Yuqi Chen & Zongyao Sun & Liangwa Cai, 2021. "Population Flow Mechanism Study of Beijing-Tianjin-Hebei Urban Agglomeration from Industrial Space Supply Perspective," Sustainability, MDPI, vol. 13(17), pages 1-15, September.
    3. Fei Ma & Yujie Zhu & Kum Fai Yuen & Qipeng Sun & Haonan He & Xiaobo Xu & Zhen Shang & Yan Xu, 2022. "Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective," IJERPH, MDPI, vol. 19(1), pages 1-25, January.
    4. Jianing Xu & Weidong Li, 2023. "High-Speed Rail and Industrial Agglomeration: Evidence from China’s Urban Agglomerations," Land, MDPI, vol. 12(8), pages 1-18, August.
    5. Cao, Yu & Hua, Zesu & Chen, Ting & Li, Xiaoying & Li, Heng & Tao, Dingtian, 2023. "Understanding population movement and the evolution of urban spatial patterns: An empirical study on social network fusion data," Land Use Policy, Elsevier, vol. 125(C).

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