The multidimensional measure and spatial-temporal evolution analysis of poverty in southwestern China based on nighttime light data
-
摘要: 中国的区域性整体贫困问题在2020年已经解决,但相对贫困仍将长期存在。因此,对贫困地区进行长期的贫困测量和发展分析仍具有重要意义。但是传统的测度方式使用社会经济数据存在较大的限制。以中国西南4省(市)为研究区域,首先,建立了基于粒子群优化算法的反向传播(back propagation, BP)神经网络模型,构建了2000—2019年的长时间序列夜光(nighttime light, NTL)数据集; 然后,根据社会经济和地理数据,构建了反映县域贫困的多维贫困指数; 最后,将长时间序列NTL数据与多维贫困指数相结合,构建了贫困测度模型,输出基于NTL数据的多维贫困指数(nighttime light multidimensional poverty index, NLMPI)。同时,在NLMPI指数的基础上进行了县域贫困测度和时空动态分析。研究表明,在2000年NLMPI表明西南4省(市)多维贫困状况分化较为严重,但随国家扶贫工作的开展,极低和较低等级县域占比下降,中等县域占比提高; 在2000—2019年间,西南地区各县域的NLMPI具有正的空间自相关,Moran’s I指数呈现先降后升的趋势,这反映出在2000—2010年,贫困聚集现象有所减弱,而在之后进入了较为分散的脱贫攻坚阶段; 局部空间自相关的结果表明,中国西南地区的多维贫困模式正在改善,但不平衡; 结果反映在成渝、昆明和贵阳的高-高聚集,以及四川西北部和云南西部的低-低聚集的空间模式。本研究强调了夜光遥感数据在区域尺度贫困研究中的应用能力。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.
-
-
[1] Steele J E, Sunds?y P R, Pezzulo C, et al. Mapping poverty using mobile phone and satellite data[J]. Journal of the Royal Society Interface, 2017, 14(127):20160690.
[2] 刘彦随, 周扬, 刘继来. 中国农村贫困化地域分异特征及其精准扶贫策略[J]. 中国科学院院刊, 2016, 31(3):269-278.
[3] Liu Y S, Zhou Y, Liu J L. Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China[J]. Bulletin of Chinese Academy of Sciences, 2016, 31(3):269-278.
[4] 周扬, 郭远智, 刘彦随. 中国县域贫困综合测度及2020年后减贫瞄准[J]. 地理学报, 2018, 73(8):1478-1493.
[5] Zhou Y, Guo Y Z, Liu Y S. Comprehensive measurement of county poverty and anti-poverty targeting after 2020 in China[J]. Acta Geographica Sinica, 2018, 73(8):1478-1493.
[6] Amis P, Rakodi C. Urban poverty:Concepts,characteristics and policies[J]. Habitat International, 1995, 19(4):403-405.
[7] 胡为安. 基于合成NPP-VIIRS年度数据的中国贫困地区发展状况研究[D]. 赣州: 江西理工大学, 2020.
[8] Hu W A. Research on the development situation of poor regions in China based on synthetic NPP-VIIRS annual data[D]. Ganzhou: Jiangxi University of Science and Technology, 2020.
[9] 王武林, 余翠婵, 曾献君, 等. 东南沿海县域贫困度演变特征及驱动因素研究——以福建省为例[J]. 地理科学进展, 2020, 39(11):1860-1873.
[10] Wang W L, Yu C C, Zeng X J, et al. Evolution characteristics and driving factors of county poverty degree in China’s southeast coastal areas:A case study of Fujian Province[J]. Progress in Geography, 2020, 39(11):1860-1873.
[11] 郭熙保, 罗知. 论贫困概念的演进[J]. 江西社会科学, 2005(11):38-43.
[12] Guo X B, Luo Z. Evolution of the concept of poverty[J]. Jiangxi Social Sciences, 2005(11):38-43.
[13] 胡蝶. 基于机器学习的贫困等级分类[D]. 武汉: 华中师范大学, 2019.
[14] Hu D. Classification of poverty levels based on machine learning[D]. Wuhan: Central China Normal University, 2019.
[15] Cheli B, Lemmi A. A totally fuzzy and relative approach to the multidimensional analysis of poverty[J]. Economic Notes, 1995, 24(1):115-134.
[16] Bourguignon F, Chakravarty S R. The measurement of multidimensional poverty[M]. Smgapore: Poverty,Social Exclusion and Stochastic Dominance. Springer, 2019:83-107.
[17] Lugo M, Maasoumi E. Multidimensional poverty measures from an information theory perspective[M]. Oxford Poverty and Human Development Initiative (OPHI), 2009.
[18] Alkire S, Foster J. Counting and multidimensional poverty measurement[J]. Journal of Public Economics, 2011, 95(7-8):476-487.
[19] Noor A M, Alegana V A, Gething P W, et al. Using remotely sensed night-time light as a proxy for poverty in Africa[J]. Population Health Metrics, 2008, 6(1):5.
[20] Smith B, Wills S. Left in the dark? Oil and rural poverty[J]. Journal of the Association of Environmental and Resource Economists, 2018, 5(4):865-904.
[21] Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data[J]. Computers and Geosciences, 2009, 35(8):1652-1660.
[22] Wang W, Cheng H, Zhang L. Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China[J]. Advances in Space Research, 2012, 49(8):1253-1264.
[23] Li G, Chang L, Liu X, et al. Monitoring the spatiotemporal dynamics of poor counties in China:Implications for global sustainable development goals[J]. Journal of Cleaner Production, 2019, 227:392-404.
[24] Han L, Zhang Q, Ma P, et al. The spatial distribution characteristics of a comprehensive drought risk index in southwestern China and underlying causes[J]. Theoretical and Applied Climatology, 2016, 124(3):517-528.
[25] Zhao J, Ji G, Yue Y, et al. Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets[J]. Applied Energy, 2019, 235:612-624.
[26] Sun Y, Zheng S, Wu Y, et al. Spatiotemporal variations of city-level carbon emissions in China during 2000—2017 using nighttime light data[J]. Remote Sensing, 2020, 12(18):2916.
[27] Wu J, He S, Peng J, et al. Intercalibration of DMSP-OLS night-time light data by the invariant region method[J]. International Journal of Remote Sensing, 2013, 34(20):7356-7368.
[28] Yue Y, Tian L, Yue Q, et al. Spatiotemporal variations in energy consumption and their influencing factors in China based on the integration of the DMSP-OLS and NPP-VIIRS nighttime light datasets[J]. Remote Sensing, 2020, 12(7):1151.
[29] Wu Y, Jiang M, Chang Z, et al. Does China’s urban development satisfy Zipf’s law? A multiscale perspective from the NPP-VIIRS nighttime light data[J]. International Journal of Environmental Research and Public Health, 2020, 17(4):1460.
[30] Li X, Zhou Y, Zhao M, et al. A harmonized global nighttime light dataset 1992—2018[J]. Scientific Data, 2020, 7(1):1-9.
[31] Elvidge C D, Ziskin D, Baugh K E, et al. A fifteen year record of global natural gas flaring derived from satellite data[J]. Energies, 2009, 2(3):595-622.
[32] Hall F G, Strebel D E, Nickeson J E, et al. Radiometric rectification:Toward a common radiometric response among multidate,multisensor images[J]. Remote Sensing of Environment, 1991, 35(1):11-27.
[33] Ma J, Guo J, Ahmad S, et al. Constructing a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light[J]. Remote Sensing, 2020, 12(6):937.
[34] Jeswani R, Kulshrestha A, Gupta P K, et al. Evaluation of the consistency of DMSP-OLS and SNPP-VIIRS night-time light datasets[J]. J Geomat, 2019, 13:98-105.
[35] Gori M, Tesi A. On the problem of local minima in back propagation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(1):76-86.
[36] Zhang J R, Zhang J, Lok T M, et al. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training[J]. Applied Mathematics and Computation, 2007, 185(2):1026-1037.
[37] Wang Y, Wang B. Multidimensional poverty measure and analysis:A case study from Hechi City,China[J]. SpringerPlus, 2016, 5(1):642.
[38] Li C, Yang W, Tang Q, et al. Detection of multidimensional poverty using Luojia 1-01 nighttime light imagery[J]. Journal of the Indian Society of Remote Sensing, 2020, 48(7):963-977.
[39] Alkire S, Foster J. Counting and multidimensional poverty measurement[J]. Journal of Public Economics, 2011, 95(7):476-487.
[40] Hu J, Zhang T, Xin X, et al. Income identification and long-term multidimensional poverty:An empirical analysis based on data from China household tracking survey[J]. On Economic Problems, 2019 (8):75-81,90.
[41] Yin J, Qiu Y, Zhang B. Identification of poverty areas by remote sensing and machine learning:A case study in Guizhou,Southwest China[J]. ISPRS Int ernational Jounra Geo-Inf ormation, 2021, 10(1):11.
[42] Yu B, Shi K, Hu Y, et al. Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2017, 8(3):1217-1229.
-
计量
- 文章访问数: 1595
- PDF下载数: 132
- 施引文献: 0
下载: