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Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Author

Listed:
  • Bin Guo

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Yi Bian

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Lin Pei

    (School of Exercise and Health Sciences, Xi’an Physical Education University, Xi’an 710068, China
    School of Public Health, Xi’an Jiaotong University, Xi’an 710043, China)

  • Xiaowei Zhu

    (Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA)

  • Dingming Zhang

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Wencai Zhang

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Xianan Guo

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Qiuji Chen

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R 2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization.

Suggested Citation

  • Bin Guo & Yi Bian & Lin Pei & Xiaowei Zhu & Dingming Zhang & Wencai Zhang & Xianan Guo & Qiuji Chen, 2022. "Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9815-:d:883849
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    References listed on IDEAS

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    1. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    2. Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    3. Jimin Lee & Kyo Suh, 2021. "A New Index to Assess Vulnerability to Regional Shrinkage (Hollowing out) Due to the Changing Age Structure and Population Density," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    4. Ying Liu & Liqiong Shu & Li Peng, 2021. "The Hollowing Process of Rural Communities in China: Considering the Regional Characteristic," Land, MDPI, vol. 10(9), pages 1-17, August.
    5. Yudan Zhang & Yuanqing Li & Yanan Chen & Shirao Liu & Qingyuan Yang, 2022. "Spatiotemporal Heterogeneity of Urban Land Expansion and Urban Population Growth under New Urbanization: A Case Study of Chongqing," IJERPH, MDPI, vol. 19(13), pages 1-25, June.
    6. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    7. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.
    8. Sheng Liu & Ming Bai & Min Yao & Ke Huang, 2021. "Identifying the natural and anthropogenic factors influencing the spatial disparity of population hollowing in traditional villages within a prefecture-level city," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-21, April.
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    2. Qingqing Yang & Yanhui Gao & Xiaoshu Cao & Jie Yang, 2023. "Contributions and Resistances to Vulnerability of Rural Human Settlements System in Agricultural Areas of Chinese Loess Plateau since 1980," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
    3. Geng, Yuqing & Liu, Liwen & Chen, Lingyan, 2023. "Rural revitalization of China: A new framework, measurement and forecast," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    4. Jiaying Zhang & Yi Chen & Xuhong Yang & Wenyi Qiao & Danyang Wang, 2022. "The Demarcation of Urban Development Boundary Based on the Maxent-CA Model: A Case Study of Wuxi in China," Sustainability, MDPI, vol. 14(18), pages 1-21, September.

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