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

Xiping Zhang, Jianbin Xu (), Saiying Zhong and Ziheng Wang
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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

Land, 2024, vol. 13, issue 9, 1-19

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.

Keywords: nighttime light (NTL) satellite data; improved human development index (improved HDI); uneven regional development; uneven regional development (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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