Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China
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 rel...
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MDPI AG
2024-09-01
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| author | Xiping Zhang Jianbin Xu Saiying Zhong Ziheng Wang |
| author_facet | Xiping Zhang Jianbin Xu Saiying Zhong Ziheng Wang |
| author_sort | Xiping Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| language | English |
| publishDate | 2024-09-01 |
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| series | Land |
| spelling | doaj-art-bcde5f2fca1d4d739c3dd9d8ce04be8e2025-08-20T01:55:37ZengMDPI AGLand2073-445X2024-09-01139152410.3390/land13091524Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in ChinaXiping Zhang0Jianbin Xu1Saiying Zhong2Ziheng Wang3College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, ChinaCollege of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, ChinaCollege of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, ChinaCollege of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, ChinaUneven 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.https://www.mdpi.com/2073-445X/13/9/1524nighttime light (NTL) satellite dataimproved human development index (improved HDI)uneven regional developmentuneven regional development |
| spellingShingle | Xiping Zhang Jianbin Xu Saiying Zhong Ziheng Wang Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China Land nighttime light (NTL) satellite data improved human development index (improved HDI) uneven regional development uneven regional development |
| title | Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China |
| title_full | Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China |
| title_fullStr | Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China |
| title_full_unstemmed | Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China |
| title_short | Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China |
| title_sort | assessing uneven regional development using nighttime light satellite data and machine learning methods evidence from county level improved hdi in china |
| topic | nighttime light (NTL) satellite data improved human development index (improved HDI) uneven regional development uneven regional development |
| url | https://www.mdpi.com/2073-445X/13/9/1524 |
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