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遥感技术与应用  2022, Vol. 37 Issue (4): 908-918    DOI: 10.11873/j.issn.1004-0323.2022.4.0908
灯光遥感专栏     
基于夜间灯光数据的陕西省县域相对贫困水平时空差异分析
陈吉臻1(),张君2,薛亮1()
1.陕西师范大学 地理科学与旅游学院,陕西 西安 710119
2.西安财经大学 管理学院,陕西 西安 710100
Analysis of the Spatio-temporal Differences of Relative Poverty Levels in Shaanxi Province based on Night Light Data
Jizhen Chen1(),jun Zhang2,Liang Xue1()
1.School of Geography and Tourism,Shaanxi Normal University,Xi’an 710119,China
2.School of management,Xi’an University of Finance and Economics,Xi’an 710100,China
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摘要:

2020年中国消除了绝对贫困,但相对贫困还继续存在且长期存在,研究相对贫困对于巩固拓展脱贫攻坚成果和有效衔接乡村振兴具有重要意义。针对已有贫困研究中仍存在的贫困分类研究、动态研究等方面不足,从相对贫困角度入手,选取陕西省107个区县为研究对象,以2011—2020年为研究时段,基于夜间灯光数据、NDVI数据和社会经济统计数据,构建以夜间灯光指数为自变量的多维贫困指数估算模型来量化识别相对贫困县域,并综合运用锡尔指数、空间局部自相关等分析方法,对量化识别出的县域相对贫困水平时空动态差异进行研究。研究结果表明:①基于夜间灯光数据可以有效进行多维贫困指数估算且估算精度达84.62%,利用多维贫困指数均值序列的前50%作为区域相对贫困划分标准,适用于描述区域相对贫困水平,有利于探索建立解决相对贫困的长效机制。②时间上,从2011年到2020年陕西省相对贫困县个数总体下降,省内各市区间相对贫困水平的区域间差异增大直接导致了贫困县两极分化程度增强。③空间上,陕西省相对贫困县域呈现“陕南贫困程度深范围广,关中渭河沿岸次之,陕北无定河沿岸以北零星分布”的格局。

关键词: 陕西省夜间灯光数据多维贫困指数相对贫困    
Abstract:

China has eliminated absolute poverty in 2020, but relative poverty will continue to exist. Doing a good job in the prevention and control of relative poverty is of great significance for consolidating and expanding the results of poverty alleviation and effectively connecting rural revitalization. In view of the shortcomings of poverty classification research and dynamic research that still exist in the existing poverty research, from the perspective of relative poverty, 107 districts and counties in Shaanxi Province are selected as the research objects, and the research period is 2011~2020. Based on night light data, NDVI data and socio-economic statistics data, a multidimensional poverty index estimation model with night light index as the independent variable is constructed to quantitatively identify relatively poor counties, And comprehensively use the analysis methods of Sier index and spatial local autocorrelation to study the spatiotemporal dynamic differences of the relative poverty level in the county quantitatively identified. The results show that: (1) Based on the night light data, the multidimensional poverty index can be estimated effectively, and the estimation accuracy is 84.62%. Using the first 50% of the mean series of Multidimensional Poverty Index as the regional relative poverty division standard is suitable for describing the regional relative poverty level, which is conducive to exploring and establishing a long-term mechanism to solve relative poverty. (2) In terms of time, from 2011 to 2020, the number of relatively poor counties in Shaanxi Province has declined on the whole, and the increase of regional differences in the relative poverty level among cities in the province has directly led to the increase of polarization in poor counties.(3) The relatively poor counties of Shaanxi Province present a pattern of “the degree of poverty is deep and wide in the south, followed by the Weihe River in Guanzhong, and sporadically distributed in the north of the Wuding River in Northern Shaanxi".

Key words: Shaanxi Province    Night light data    Multi-dimensional Poverty Index    Relative poverty
收稿日期: 2021-07-06 出版日期: 2022-09-28
:  TP751  
基金资助: 陕西省社会科学基金项目(2017G008)
通讯作者: 薛亮     E-mail: 351651099@qq.com;brxue@snnu.edu.cn
作者简介: 陈吉臻(1997-),女,重庆永川人,硕士研究生,主要从事区域经济研究。E?mail:351651099@qq.com
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引用本文:

陈吉臻,张君,薛亮. 基于夜间灯光数据的陕西省县域相对贫困水平时空差异分析[J]. 遥感技术与应用, 2022, 37(4): 908-918.

Jizhen Chen,jun Zhang,Liang Xue. Analysis of the Spatio-temporal Differences of Relative Poverty Levels in Shaanxi Province based on Night Light Data. Remote Sensing Technology and Application, 2022, 37(4): 908-918.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.4.0908        http://www.rsta.ac.cn/CN/Y2022/V37/I4/908

图1  DMSP和VIIRS影像数据DN值统计分析
图2  陕西省2020年夜间灯光数据饱和校正效果对比
维度指标指标属性权重
社会年末总人口(X1)+0.079
农村居民人均纯收入与城镇居民可支配收入之比(X2)+0.022
每千人卫生技术人员数(X3)+0.072
经济人均生产总值(X4)+0.075
人均社会消费品零售总额(X5)+0.212
路网密度(X6)+0.401
环境归一化植被指数(X7)+0.011
地形起伏度(X8)-0.128
表1  多维贫困指数模型指标权重
图3  MPI与ANLI及TNLI的回归关系
贫困程度2011年2013年2015年2017年2020年
个数比例个数比例个数比例个数比例个数比例
重度相对贫困54.67%32.80%109.35%1312.15%1312.15%
高度相对贫困1514.02%1514.02%1110.28%1110.28%1110.28%
中度相对贫困98.41%1816.82%1211.21%1211.21%98.41%
轻度相对贫困1413.08%76.54%109.35%109.35%98.41%
非贫困6459.81%6459.81%6459.81%6560.75%6560.75%
相对贫困总量4340.19%4340.19%4340.19%4239.25%4239.25%
表2  陕西省县域相对贫困程度识别个数与比例
年份空间聚集类型个数县级行政区名称
2011高高聚集5子洲县、子长县、延川县、延长县、西乡县
低低聚集3镇安县、旬阳县、平利县
2015低高集聚1宜君县
低低聚集1镇安县
2020高高聚集2澄城县、大荔县
低高聚集3麟游县、宜君县、黄龙县
低低聚集1镇安县
表3  2011—2020年陕西省相对贫困水平空间集聚性变化
年份2011201220132014201520162017201820192020
总差异0.301 40.272 40.504 80.096 10.071 10.095 00.159 10.163 00.160 90.158 2
铜川市差异0.000 010.000 250.002 410.003 030.003 130.002 120.004 690.004 970.005 850.005 49
比重/%0.000.090.483.154.402.232.953.053.643.47
宝鸡市差异0.007 850.072 850.035 720.003 170.001 480.001 570.001 350.003 820.003 570.003 20
比重/%2.6126.757.083.302.071.650.852.342.222.02
咸阳市差异0.007 690.004 640.006 020.005 990.005 280.004 300.012 540.013 770.011 380.010 81
比重/%2.551.701.196.237.424.537.888.457.076.83
渭南市差异0.003 330.001 700.000 420.000 500.000 660.005 700.001 590.001 930.002 340.002 45
比重/%1.100.620.080.520.936.001.001.181.451.55
延安市差异0.036 860.019 910.031 240.032 100.029 180.018 650.010 970.008 480.008 310.008 48
比重/%12.237.316.1933.4041.0319.646.895.205.165.36
汉中市差异0.056 590.037 830.070 350.010 590.009 590.008 380.008 250.010 130.010 910.010 31
比重/%18.7713.8913.9411.0313.488.825.186.216.786.51
榆林市差异0.050 390.010 910.050 450.001 750.000 580.002 710.006 140.004 110.003 530.003 71
比重/%16.714.0110.001.820.812.863.862.522.192.34
安康市差异0.081 910.060 810.087 540.006 740.010 140.023 500.007 620.008 620.007 860.008 83
比重/%27.1722.3217.347.0114.2624.744.795.294.885.58
商洛市差异0.024 710.046 390.055 960.006 200.008 000.021 630.010 570.012 700.013 100.013 12
比重/%8.2017.0311.096.4511.2422.776.657.798.148.29
区域间差异0.032 10.017 10.164 60.026 00.003 10.006 40.095 40.094 50.094 10.091 9
比重/%10.656.2832.6227.094.366.7659.9557.9658.4658.05
表4  2011—2020年陕西省县域相对贫困水平差异变化及分解
图4  2011—2020年陕西省县域相对贫困水平差异
图5  陕西省2011年、2015年和2020年相对贫困水平空间差异
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