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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 286-298     DOI: 10.6046/zrzyyg.2021386
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The multidimensional measure and spatial-temporal evolution analysis of poverty in southwestern China based on nighttime light data
ZHANG Qiongyi1(), LI Kun2, YONG Zhiwei3, XIONG Junnan1,4(), CHENG Weiming4, XIAO Kunhong5, LIU Dongli6
1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
2. Sichuan Electric Power Design and Consulting Co. Ltd., Chengdu 610041, China
3. School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5. Sichuan Province Coalfield Surverying and Mapping Engineering Institute, Chengdu 610072, China
6. The Sixth Topographic Survey Team of the Ministry of Natural Resources, Chengdu 610500, China
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Abstract  

The overall regional poverty in China was eliminated in 2020, but the relative poverty in the country will still exist for a long time. Therefore, it is necessary to conduct a long-term measurement and development analysis of poverty in poverty-stricken areas. However, conventional measurement methods based on socio-economic data have severe limitations. With four provinces (municipalities) in southwestern China as a case study, this study built a back propagation (BP) neural network model based on the particle swarm optimization algorithm and a nighttime light (NTL) dataset of long time series from 2000 to 2019 first. Then, this study constructed the multi-dimensional poverty indices based on socio-economic and geographical data to reflect the poverty in counties. Finally, this study established a poverty measure model by combining the long-time-series NTL data with the multidimensional poverty indices and produced the nighttime light multidimensional poverty index (NLMPI). Based on the NLMPI, the measure and spatial-temporal evolution analysis of poverty in counties were carried out. The study results are as follows. The NLMPI indicates that the four provinces (municipalities) in southwestern China had significantly differentiated multidimensional poverty in 2000. However, the proportion of counties at extremely low and low levels decreased while that of moderate-level counties increased owing to the national poverty alleviation efforts. From 2000 to 2019, the NLMPI of counties in southwestern China showed a positive spatial autocorrelation and the Moran’s I index showed a downward and then an upward trend. These results indicate that poverty aggregation weakened from 2000 to 2010 and poverty alleviation dispersed thereafter in the four provinces (municipalities) in southwestern China. The local spatial autocorrelation results indicate that the multi-dimensional poverty pattern in southwestern China was alleviated but unbalanced. This pattern was reflected in the high NLMPI values surrounded by high NLMPI values (the H-H aggregation type) in Chengdu-Chongqing, Kunming, and Guiyang and in the low NLMPI values surrounded by low NLMPI values (the L-L aggregation type) in northwestern Sichuan and western Yunnan. This study highlights the application of NTL data in research on regional poverty.
Keywords nighttime light data      multi-dimensional poverty      poverty measure      spatial-temporal evolution     
ZTFLH:  TP79  
Issue Date: 27 December 2022
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Qiongyi ZHANG
Kun LI
Zhiwei YONG
Junnan XIONG
Weiming CHENG
Kunhong XIAO
Dongli LIU
Cite this article:   
Qiongyi ZHANG,Kun LI,Zhiwei YONG, et al. The multidimensional measure and spatial-temporal evolution analysis of poverty in southwestern China based on nighttime light data[J]. Remote Sensing for Natural Resources, 2022, 34(4): 286-298.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021386     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/286
Fig.1  Location of the study area
Fig.2  Population and GDP distribution of the study area in 2019
数据名称 数据
格式		 数据周期 空间
分辨率		 来源
DMSP-OLS .png 2000—2013年 30弧秒 科罗拉多矿业大学地球观测小组
NPP-VIIRS .png 2012—2019年 15弧秒 科罗拉多矿业大学地球观测小组
县级行政区划 .shp 2015年 1:100万 资源环境科学与数据中心
统计数据 .csv 2000—2019年 — 各级统计局
SRTMDEM .png — 90 m 地理空间数据云平台
国家贫困县名单 .csv — — 国家乡村振兴局
Tab.1  Description of each data sources used in this study
维度 指标 属性 权重分配
经济维度 人均GDP 正 0.130 8
农民人均纯收入 正 0.081 8
人均公共财政预算收入 正 0.153 2
社会维度 卫生机构数 正 0.094 1
每千人床位数 正 0.086 8
人均全社会固定资产投资 正 0.131 9
人均社会消费品零售总额 正 0.177 9
在校小学生比重 正 0.051 5
在校中学生比重 正 0.033 7
自然维度 坡度大于15°的面积比 负 0.036 0
平均海拔 负 0.021 6
Tab.2  Evaluation indices and weight distribution of multidimensional poverty
方面 编号 描述
集中特征 F1 县域内所有像素值的平均值
F2 县域内所有亮元像素值的平均值
分散程度 F3 县域内所有像素值的方差
F4 县域内所有像素值的标准差
F5 县域内所有像素值的离差平方和
分布特征 F6 县域内所有像素值的总和
F7 县域内的像素数
F8 县域内大于0的像素数
F9 县域内所有像素的最大值
F10 县域内所有像素的最小值
F11 县域内所有像素的值域
空间特征 F12 基于F3的县域局部自相关
Tab.3  Feature variables of regional NTL and its description
Fig.3  Spatial patterns of MPI in Chongqing in 2019
Fig.4  MPI of various county in Chongqing
Fig.5  Spatial distribution of 12 lighting indices in Chongqing in 2019
编号 相关系数 编号 相关系数
F1 0.550 **① F7 -0.373 **
F2 0.594 ** F8 0.340 **
F3 0.307 ** F9 0.534 **
F4 0.409 ** F10 0.371 **
F5 0.372 ** F11 0.267 **
F6 0.604 ** F12 0.129 **
Tab.4  Correlation analysis between MPI and NTL feature variables
Fig.6  Spatial patterns of NLMPI and national poverty counties in southwest China
Fig.7  NLMPI of southwest China in different years
类型 2015年 2019年
国家划定
贫困县数		 所占比例/% 国家划定
贫困县数		 所占比例/%
极低 52 92.9 29 38.2
较低 109 60.6 15 9.0
中等 21 19.6 3 2.3
较高 5 6.7 0 0
极高 1 5.3 0 0
Tab.5  Number and proportion of national poverty counties in the counties of each grade
Fig.8  Percentage of counties at different levels of poverty in southwestern China
Fig.9  Temporal variations of the average NLMPI in southwest China from 2000 to 2019
Fig.10  Trend of MPI in southwest China during the different periods
Fig.11  Morans’I index for southwest China during the period of 2000—2019
Fig.12  Distribution of NLMPI index of local spatial autocorrelation in southwest China from 2000 to 2019
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