首页|Research from Shanxi University of Finance and Economics Yields New Data on Mach ine Learning (Assessing Uneven Regional Development Using Nighttime Light Satell ite Data and Machine Learning Methods: Evidence from County-Level Improved HDI i n ...)
Research from Shanxi University of Finance and Economics Yields New Data on Mach ine Learning (Assessing Uneven Regional Development Using Nighttime Light Satell ite Data and Machine Learning Methods: Evidence from County-Level Improved HDI i n ...)
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Data detailed on artificial intelligen ce have been presented. According to news reporting out of Taiyuan, People's Rep ublic of China, by NewsRx editors, research stated, "Uneven regional development has long been a focal issue for both academia and policymakers, with numerous s tudies over the past decades actively engaging in discussions on measuring regio nal development disparities. Generally, most existing studies measure the Human Development Index (HDI) using relatively simple indicators, with a focus on nati onal and provincial scales." Funders for this research include National Natural Science Foundation of China; Humanities And Social Science Fund of Ministry of Education of China; Shanxi Pro vincial Applied Basic Research Program. The news reporters obtained a quote from the research from Shanxi University of Finance and Economics: "As a crucial component of regional development, counties can directly reflect the regional characteristics of socio-economic progress. T his study employs a multi-dimensional approach to develop an improved Human Deve lopment Index (improved HDI) system, using machine learning techniques to establ ish the relationship between nighttime light (NTL) data and the improved HDI. Su bsequently, NTL data are utilized to infer the spatial distribution characterist ics of the improved HDI across China's county-level regions. The improved HDI fo r 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 Rsquared value for the linear fit was 0.86, and the residuals were relatively balanced, ensuring the accuracy of the simulations. This study revea ls that 1439 county-level units, representing 50% of all county-le vel 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."