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Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China

Author

Listed:
  • Xiping Zhang

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Jianbin Xu

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Saiying Zhong

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Ziheng Wang

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

Abstract

Uneven regional development has long been a focal issue for both academia and policymakers, with numerous studies over the past decades actively engaging in discussions on measuring regional development disparities. Generally, most existing studies measure the Human Development Index (HDI) using relatively simple indicators, with a focus on national and provincial scales. As a crucial component of regional development, counties can directly reflect the regional characteristics of socio-economic progress. This study employs a multi-dimensional approach to develop an improved Human Development Index (improved HDI) system, using machine learning techniques to establish the relationship between nighttime light (NTL) data and the improved HDI. Subsequently, NTL data are utilized to infer the spatial distribution characteristics of the improved HDI across China’s county-level regions. The improved HDI for county-level areas in the Ningxia Hui Autonomous Region was validated using a machine learning model, resulting in a Pearson correlation coefficient of 0.93. The adjusted R-squared value for the linear fit was 0.86, and the residuals were relatively balanced, ensuring the accuracy of the simulations. This study reveals that 1439 county-level units, representing 50% of all county-level units in China, have development levels at or above the medium level. At the provincial and national levels, the improved HDI shows significant clustering, characterized by a multi-center pattern with declining diffusion. The spatial distribution of the improved Human Development Index remains closely associated with the natural geographic background and socio-economic development levels of the county regions. Lower HDI values are predominantly found in the inland areas of central and western China, often in ecologically sensitive areas, inter-provincial border zones, and mountainous regions of mainland China, sometimes forming contiguous distribution patterns. This underscores the need for the government and society to focus more on these specific geographic development areas, promoting continuous improvements in health, education, and living standards to achieve coordinated regional development.

Suggested Citation

  • Xiping Zhang & Jianbin Xu & Saiying Zhong & Ziheng Wang, 2024. "Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China," Land, MDPI, vol. 13(9), pages 1-19, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1524-:d:1481609
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    References listed on IDEAS

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    1. Fleisher, Belton & Li, Haizheng & Zhao, Min Qiang, 2010. "Human capital, economic growth, and regional inequality in China," Journal of Development Economics, Elsevier, vol. 92(2), pages 215-231, July.
    2. Doll, Christopher N.H. & Muller, Jan-Peter & Morley, Jeremy G., 2006. "Mapping regional economic activity from night-time light satellite imagery," Ecological Economics, Elsevier, vol. 57(1), pages 75-92, April.
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    5. Brock, Gregory, 2019. "A remote sensing look at the economy of a Russian region (Rostov) adjacent to the Ukrainian crisis," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 416-431.
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