Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China
Eliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this...
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MDPI AG
2021-11-01
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author | Yanjun Wang Mengjie Wang Bo Huang Shaochun Li Yunhao Lin |
author_facet | Yanjun Wang Mengjie Wang Bo Huang Shaochun Li Yunhao Lin |
author_sort | Yanjun Wang |
collection | DOAJ |
description | Eliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this paper, a quantitative evaluation model is built and applied to all poverty-stricken counties in Hunan Province. First, based on the SDG global index framework and local index system of China, a local SDG index system for poverty-related goals is designed, and the weights of the indexes are derived using an entropy method. The scores obtained for counties and districts with data available are then taken as the true value for the poverty assessment. Second, using National Polar-orbiting Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light images and land use and digital elevation model data, six factors, including socioeconomic, land cover, terrain and traffic factors, are extracted. Third, we then construct multiple linear evaluation models of poverty targets defined by the SDGs and machine learning evaluation models, including regression trees, support vector machines, Gaussian process regressions and ensemble trees. Last, combined with statistical data of poverty-stricken counties in Hunan Province, model validation and accuracy evaluation are carried out. The results show that the R<sup>2</sup> and relative error of the localized, multiple linear evaluation model, including all six factors, are 0.76 and 19.12%, respectively. The poverty-stricken counties in Hunan Province were spatially aggregated and distributed mainly in the southeastern and northwestern regions. The proposed method for regional poverty assessment based on multisource geographic data provides an effective poverty monitoring reference scheme for the implementation of the poverty eradication goals in the 2030 agenda. |
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spelling | doaj.art-2f2ff06228324962926acf5010f4a6a12023-11-23T02:56:16ZengMDPI AGRemote Sensing2072-42922021-11-011323477810.3390/rs13234778Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, ChinaYanjun Wang0Mengjie Wang1Bo Huang2Shaochun Li3Yunhao Lin4Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, ChinaDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, ChinaHunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, ChinaEliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this paper, a quantitative evaluation model is built and applied to all poverty-stricken counties in Hunan Province. First, based on the SDG global index framework and local index system of China, a local SDG index system for poverty-related goals is designed, and the weights of the indexes are derived using an entropy method. The scores obtained for counties and districts with data available are then taken as the true value for the poverty assessment. Second, using National Polar-orbiting Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light images and land use and digital elevation model data, six factors, including socioeconomic, land cover, terrain and traffic factors, are extracted. Third, we then construct multiple linear evaluation models of poverty targets defined by the SDGs and machine learning evaluation models, including regression trees, support vector machines, Gaussian process regressions and ensemble trees. Last, combined with statistical data of poverty-stricken counties in Hunan Province, model validation and accuracy evaluation are carried out. The results show that the R<sup>2</sup> and relative error of the localized, multiple linear evaluation model, including all six factors, are 0.76 and 19.12%, respectively. The poverty-stricken counties in Hunan Province were spatially aggregated and distributed mainly in the southeastern and northwestern regions. The proposed method for regional poverty assessment based on multisource geographic data provides an effective poverty monitoring reference scheme for the implementation of the poverty eradication goals in the 2030 agenda.https://www.mdpi.com/2072-4292/13/23/4778sustainable development goalsmultisource geographic datapoverty index systemmultiple linear regression modelmachine learning |
spellingShingle | Yanjun Wang Mengjie Wang Bo Huang Shaochun Li Yunhao Lin Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China Remote Sensing sustainable development goals multisource geographic data poverty index system multiple linear regression model machine learning |
title | Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China |
title_full | Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China |
title_fullStr | Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China |
title_full_unstemmed | Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China |
title_short | Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China |
title_sort | evaluation and analysis of poverty stricken counties under the framework of the un sustainable development goals a case study of hunan province china |
topic | sustainable development goals multisource geographic data poverty index system multiple linear regression model machine learning |
url | https://www.mdpi.com/2072-4292/13/23/4778 |
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