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Using multiple linear regression and random forests to identify spatial poverty determinants in rural China

文献类型：期刊论文

作者Liu, Mengxiao2,3; Hu, Shan2; Ge, Yong2,3; Heuvelink, Gerard B. M.1,4; Ren, Zhoupeng2; Huang, Xiaoran2
刊名SPATIAL STATISTICS
出版日期2021-04-01
卷号42页码:19
关键词Poverty Spatial Determinants LMG Random forest Variable importance
ISSN号2211-6753
DOI10.1016/j.spasta.2020.100461
通讯作者Ge, Yong(gey@lreis.ac.cn)
英文摘要Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random forest (RF) machine learning. A case study was conducted in Yunyang, a poverty-stricken county in China, to evaluate the performances of the two methods for identifying village-level poverty determinants. The results indicated that: (1) MLR and RF had similar explanation accuracy; (2) LMG and RF were consistent in the three main determinants of poverty; (3) LMG better identified the importance of variables that were highly related to poverty but correlated with other variables, while RF better identified the non-linear relationships between poverty and explanatory variables; (4) accessibility metrics are the most important variables influencing poverty in Yunyang and have a linear relationship with poverty. (C) 2020 Elsevier B.V. All rights reserved.
资助项目Key Technologies Research and Development Program of China[2012BAH33B01]
WOS研究方向Geology ; Mathematics ; Remote Sensing
语种英语
WOS记录号WOS:000635278600002
出版者ELSEVIER SCI LTD
资助机构Key Technologies Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/161902]  
专题中国科学院地理科学与资源研究所
通讯作者Ge, Yong
作者单位1.ISRIC World Soil Informat, POB 353, NL-6700 AJ Wageningen, Netherlands
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands
推荐引用方式
GB/T 7714		 Liu, Mengxiao,Hu, Shan,Ge, Yong,et al. Using multiple linear regression and random forests to identify spatial poverty determinants in rural China[J]. SPATIAL STATISTICS,2021,42:19.
APA Liu, Mengxiao,Hu, Shan,Ge, Yong,Heuvelink, Gerard B. M.,Ren, Zhoupeng,&Huang, Xiaoran.(2021).Using multiple linear regression and random forests to identify spatial poverty determinants in rural China.SPATIAL STATISTICS,42,19.
MLA Liu, Mengxiao,et al."Using multiple linear regression and random forests to identify spatial poverty determinants in rural China".SPATIAL STATISTICS 42(2021):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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