Evaluation of county-level poverty alleviation progress by deep learning and satellite observations

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-10-12 DOI:10.1080/20964471.2021.1967259
Yanxiao Jiang, Liqiang Zhang, Yang Li, Jintai Lin, Jingwen Li, Guoqing Zhou, Su-hong Liu, Jingxiu Cao, Zhiqiang Xiao
{"title":"Evaluation of county-level poverty alleviation progress by deep learning and satellite observations","authors":"Yanxiao Jiang, Liqiang Zhang, Yang Li, Jintai Lin, Jingwen Li, Guoqing Zhou, Su-hong Liu, Jingxiu Cao, Zhiqiang Xiao","doi":"10.1080/20964471.2021.1967259","DOIUrl":null,"url":null,"abstract":"ABSTRACT Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"20 1","pages":"576 - 592"},"PeriodicalIF":4.2000,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Earth Data","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/20964471.2021.1967259","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 3

Abstract

ABSTRACT Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度学习和卫星观测的县级扶贫进展评价
减贫是中低收入国家面临的最大挑战之一。中国是世界上农村贫困人口最多的国家,特别是2014年实施精准扶贫政策以来,中国在扶贫方面做出了巨大努力,到2020年,全国贫困县全部实现脱贫。本研究将深度学习与多个卫星数据集相结合,对2008 - 2019年的县域经济发展进行了估算,并对592个国家级贫困县(县、省、县)的TPA政策效果进行了评估。人均国内生产总值(GDP)用来衡量富裕水平。2014 - 2019年,592个全国人大人均国内生产总值平均增速为7.6%±0.4%,高于毗邻的310个非全国人大县(7.3%±0.4%)和全国平均增速(6.3%)。我们还揭示了42个近期增长疲软的县,2019年全国人大的平均富裕水平仍远低于全国或全省平均水平。本文提出的方法廉价、及时、准确,可应用于其他低收入和中等收入国家的富裕程度评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
期刊最新文献
A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020 An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea A mediation system for continuous spatial queries on a unified schema using Apache Spark
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1