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The Factors Influencing China’s Population Distribution and Spatial Heterogeneity: Based on Multi-source Remote Sensing Data

Author

Listed:
  • Shasha Huang

    (Jiangsu University of Science and Technology)

  • Jiandong Chen

    (Southwestern University of Finance and Economics)

  • Ming Gao

    (Southwestern University of Finance and Economics)

  • Mengjiao Yuan

    (Southwestern University of Finance and Economics)

  • Zunhong Zhu

    (Nanjing Audit University)

  • Xueli Chen

    (Chinese Academy of Social Sciences)

  • Malin Song

    (Anhui University of Finance and Economics
    Anhui University of Finance and Economics
    Lebanese American University)

Abstract

Examining the factors that influence population distribution enables us to gain insights into the patterns and evolutionary trends of distribution over time. Based on the Spatial Durbin Model (SDM) with satellite data of nighttime light, net primary productivity (NPP), and the digital evaluation model (DEM), this study examines the population distribution of 303 prefecture-level cities in China between 2007 and 2017 in terms of three dimensions—economic development, ecological environment, and topography. The empirical results reveal that, firstly, the above-mentioned multiple factors have caused the current population distribution in Chinese cities. Economic development emerges as a potent force driving population concentration within local regions while simultaneously exerting a draining influence on surrounding urban centers. Secondly, the environment has a significant agglomeration effect on local regions and surrounding areas, while the average altitude can inhibit population aggregation. It is worth noting, however, that in eastern China, average altitude surprisingly contributes to population concentration within the local area.

Suggested Citation

  • Shasha Huang & Jiandong Chen & Ming Gao & Mengjiao Yuan & Zunhong Zhu & Xueli Chen & Malin Song, 2024. "The Factors Influencing China’s Population Distribution and Spatial Heterogeneity: Based on Multi-source Remote Sensing Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2179-2203, October.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:4:d:10.1007_s10614-023-10515-y
    DOI: 10.1007/s10614-023-10515-y
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    References listed on IDEAS

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