Abstract
To quick and accurate identify poverty-stricken areas in the context of poverty eradication is a challenge that has troubled researchers. Based on the nighttime light data of NPP-VIIRS, this study takes three prefectures in South Xinjiang in China as the study objective to analyze the development and changes of concentrated contiguous poverty-stricken areas from 2012 to 2018. The multidimensional poverty index of administrative region of the study area and county is constructed, with the optimal light index selected through correlation analysis with different nighttime light indexes. Spatial visualization analysis of poverty differences in the study area from 2012 to 2018 was conducted. The landscape ecological index was used to analyze the nighttime light patches classified by DN (Digital Number) interval. The results show that: (1) There are significant differences in poverty among different administrative regions in the study area. From 2012 to 2018, the multidimensional poverty index (MPI) of all county-level administrative regions showed a significant growth trend, with corresponding decrease in poverty to varying degrees. (2) The fitting analysis of MPI and the three different light indexes show that MPI has the highest correlation with nighttime light area, and the administrative area with a larger nighttime light area develops better while the poverty level is lower in the administrative area with a smaller nighttime light area. (3) From 2012 to 2018, the nighttime light area in the study area increased steadily, and the growth area was mainly in villages and towns, indicating that the development level of rural and township areas where poverty mainly clustered was improved, and the possibility of poverty decreased.
Similar content being viewed by others
References
Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487. https://doi.org/10.1016/j.jpubeco.2010.11.006
Barati, A. A., Zhoolideh, M., Moradi, M., Sohrabi Mollayousef, E., & Fürst, C. (2021). Multidimensional poverty and livelihood strategies in rural Iran. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-021-01977-x
Betti, G., Gagliardi, F., Lemmi, A., & Verma, V. (2015). Comparative measures of multidimensional deprivation in the European Union. Empirical Economics, 49(3), 1071–1100. https://doi.org/10.1007/s00181-014-0904-9
Chen, X., Pei, Z., Chen, A. L., Wang, F., Shen, K., Zhou, Q., & Sun, L. (2015). Spatial distribution patterns and influencing factors of poverty-a case study on the key country from national contiguous special poverty-stricken areas in China. Procedia Environmental Sciences, 26, 82–90. https://doi.org/10.1016/j.proenv.2015.05.005
Chen, Y. F., Wang, Y. H., Zhao, W. J., Hu, Z., & Duan, F. (2017). Contributing factors and classification of poor villages in China. Acta Geographica Sinica, 72(10), 1827–1844. https://doi.org/10.11821/dlxb201710008
Doll, C. N., Muller, J. P., & Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1), 75–92. https://doi.org/10.1016/j.ecolecon.2005.03.007
Dou, H., Ma, L., Liu, S., & Fang, F. (2022). Identification of rural regional poverty type based on spatial multi-criteria decision-making—taking Gansu Province, an underdeveloped area in China, as an example. Environment, Development and Sustainability, 24(3), 3439–3460. https://doi.org/10.1007/s10668-021-01573-z
Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F. C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Proceedings of the Asia-Pacific Advanced Network, 35, 62. https://doi.org/10.7125/APAN.35.7
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21), 5860–5879. https://doi.org/10.1080/01431161.2017.1342050
Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., & Bright, E. (2009). A global poverty map derived from satellite data. Computers & Geosciences, 35(8), 1652–1660. https://doi.org/10.1016/j.cageo.2009.01.009
Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T., Milesi, C., & Nemani, R. (2007). Global distribution and density of constructed impervious surfaces. Sensors, 7(9), 1962–1979. https://doi.org/10.3390/s7091962
Ge, Y., Hu, S., Ren, Z., Jia, Y., Wang, J., Liu, M., & Chen, Y. (2019). Mapping annual land use changes in China’s poverty-stricken areas from 2013 to 2018. Remote Sensing of Environment, 232, 111285. https://doi.org/10.1016/j.rse.2019.111285
Ghosh, T., Anderson, S. J., Elvidge, C. D., & Sutton, P. C. (2013). Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability, 5(12), 4988–5019. https://doi.org/10.3390/su5124988
Glauben, T., Herzfeld, T., Rozelle, S., & Wang, X. (2012). Persistent poverty in rural China: Where, why, and how to escape? World Development, 40(4), 784–795. https://doi.org/10.1016/j.worlddev.2011.09.023
Han, M., Sun, Y. N., Xu, S. G., & Tang, X. L. (2005). Study on changes of marsh landscape pattern in Zhalong wetland assisted by RS and GIS. Progress in Geography, 24(6), 42–49. https://doi.org/10.11820/dlkxjz.2005.06.005
Hassan, M. S., Bukhari, S., & Arshed, N. (2020). Competitiveness, governance and globalization: What matters for poverty alleviation? Environment, Development and Sustainability, 22(4), 3491–3518. https://doi.org/10.1007/s10668-019-00355-y
Jiang, J., Zhang, J., Zhang, Y., Zhang, C., & Tian, G. (2016). Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model. Science of the Total Environment, 544, 1119–1127. https://doi.org/10.1016/j.scitotenv.2015.11.113
Labar, K., & Bresson, F. (2011). A multidimensional analysis of poverty in China from 1991 to 2006. China Economic Review, 22(4), 646–668. https://doi.org/10.1016/j.chieco.2011.08.005
Li, G., Cai, Z., Liu, X., Liu, J., & Su, S. (2019). A comparison of machine learning approaches for identifying high-poverty counties: Robust features of DMSP/OLS night-time light imagery. International Journal of Remote Sensing, 40(15), 5716–5736. https://doi.org/10.1080/01431161.2019.1580820
Li, X., Elvidge, C., Zhou, Y., Cao, C., & Warner, T. (2017). Remote sensing of night-time light. International Journal of Remote Sensing, 38(21), 5855–5859. https://doi.org/10.1080/01431161.2017.1351784
Liu, Y. H., & Xu, Y. (2015). Geographical identification and classification of multi-dimensional poverty in rural China. Acta Geographica Sinica, 70(6), 993–1007.
Liu, Z., He, C., Zhang, Q., Huang, Q., & Yang, Y. (2012). Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106(1), 62–72. https://doi.org/10.1016/j.landurbplan.2012.02.013
Ma, M., Lang, Q., Yang, H., Shi, K., & Ge, W. (2020). Identification of polycentric cities in China based on NPP-VIIRS nighttime light data. Remote Sensing, 12(19), 3248. https://doi.org/10.3390/rs12193248
McGarigal, K., & Marks, B. J. (1995). Spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1–122.
Min, M., Lin, C., Duan, X., Jin, Z., & Zhang, L. (2021). Research on targeted land poverty alleviation patterns based on the precise identification of dominant factors of rural poverty: A case study of Siyang County, Jiangsu Province, China. Environment, Development and Sustainability, 23(9), 12791–12813. https://doi.org/10.1007/s10668-020-01185-z
Pan, J. H., Zhao, H. Y., & Dong, L. L. (2018). Spatial identification of multi-dimensional poverty in rural China by using nighttime light and sustainable livelihoods. Acta Ecologica Sinica, 38(17), 6180–6193. https://doi.org/10.5846/stxb201709101627
Pan, W., Fu, H., & Zheng, P. (2020). Regional poverty and inequality in the Xiamen-Zhangzhou-Quanzhou city cluster in China based on NPP/VIIRS night-time light imagery. Sustainability, 12(6), 2547. https://doi.org/10.3390/su12062547
Rao, K. S., & Pant, R. (2001). Land use dynamics and landscape change pattern in a typical micro watershed in the mid elevation zone of central Himalaya, India. Agriculture, Ecosystems and Environment, 86(2), 113–124. https://doi.org/10.1016/S0167-8809(00)00274-7
Shan, X. K., & Wang, J. (2017). Analysis of poverty situation and countermeasures in the four States of southern Xinjiang. Seeking Truth from Facts, 01, 87–91. https://doi.org/10.3969/j.issn.1003-4641.2017.01.19
Sharma, R. C., Tateishi, R., Hara, K., Gharechelou, S., & Iizuka, K. (2016). Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data. International Journal of Digital Earth, 9(10), 1004–1020. https://doi.org/10.1080/17538947.2016.1168879
Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., Chen, L., & Wu, J. (2014). Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sensing, 6(2), 1705–1724. https://doi.org/10.3390/rs6021705
Shi, X. (2015). Study on the spillover effect of sustainable land use in the new urbanization process. Rural Economy and Technology, 26(09), 13–16.
Steven, M., William, S., Stephen, M., Christopher, E., Thomas, L., Jeremy, S., Andi, W., Andrew, H., & Stephanie, W. (2013). Illuminating the Capabilities of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band. Remote Sensing, 5(12), 6717–6766. https://doi.org/10.3390/rs5126717
Tian, Y., Wang, Z., Zhao, J., Jiang, X., & Guo, R. (2018). A geographical analysis of the poverty causes in China’s contiguous destitute areas. Sustainability, 10(6), 1895. https://doi.org/10.3390/su10061895
Ustaoglu, E., Bovkır, R., & Aydınoglu, A. C. (2021). Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: A case study of Turkey. Environment, Development and Sustainability, 23(7), 10309–10343. https://doi.org/10.1007/s10668-020-01058-5
Wang, L., Wang, S., Zhou, Y., Liu, W., Hou, Y., Zhu, J., & Wang, F. (2018). Mapping population density in China between 1990 and 2010 using remote sensing. Remote Sensing of Environment, 210, 269–281. https://doi.org/10.1016/j.rse.2018.03.007
Wang, W., Cheng, H., & Zhang, L. (2012). Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China. Advances in Space Research, 49(8), 1253–1264. https://doi.org/10.1016/j.asr.2012.01.025
Wang, Y., & Wang, B. (2016). Multidimensional poverty measure and analysis: A case study from Hechi City, China. Springerplus, 5(1), 1–25. https://doi.org/10.1186/s40064-016-2192-7
Wu, R., Yang, D., Dong, J., Zhang, L., & Xia, F. (2018). Regional inequality in China based on NPP-VIIRS night-time light imagery. Remote Sensing, 10(2), 240. https://doi.org/10.3390/rs10020240
Yaolebasi·X. (2018). The research of Multidimensional Poverty Measurement in three cities of southern Xinjiang contiguous poor areas. Shihezi University.
Xie, D., Zhang, N., & Su, Y. (2020). Industial development modes and restraining factors of severe poverty region after resident relocation for poverty alleviation: Cases of three prefectures in South Xinjiang. Arid Land Geography, 43(05), 1401–1408.
Yanyun, G. (2012). The multidimensional poverty in urban and rural China: Measurement and comparison. Statistical Research, 29(11), 61–66.
Yao, S., Zhang, Z., & Hanmer, L. (2004). Growing inequality and poverty in China. China Economic Review, 15(2), 145–163. https://doi.org/10.1016/j.chieco.2003.09.002
Yu, B., Shi, K., Hu, Y., Huang, C., Chen, Z., & Wu, J. (2017). Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1217–1229. https://doi.org/10.1109/JSTARS.2015.2399416
Yu, Q. (2018). Study on the Multidimensional Poverty Formation Mechanism and Precise Poverty Alleviation in the Three Regions of Southern Xinjiang. Shihezi University.
Zhang, P., Shi, X., Sun, Y., Cui, J., & Shao, S. (2019). Have China’s provinces achieved their targets of energy intensity reduction? Reassessment based on nighttime lighting data. Energy Policy, 128, 276–283. https://doi.org/10.1016/j.enpol.2019.01.014
Zhang, Q. J., Fu, B. J., & Chen, L. D. (2003). Several problems about landscape pattern change research. Scientia Geographica Sinica, 23(3), 270–275. https://doi.org/10.13249/j.cnki.sgs.2003.03.264
Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., Yang, C., Chen, Z., & Wu, J. (2018). NPP-VIIRS DNB daily data in natural disaster assessment: Evidence from selected case studies. Remote Sensing, 10(10), 1526. https://doi.org/10.3390/rs10101526
Zhou, Y., Guo, Y., & Liu, Y. (2018). Comprehensive measurement of county poverty and anti-poverty targeting after 2020 in China. Acta Geogr. Sin, 73, 1478–1493. https://doi.org/10.11821/dlxb201808007
Zhou, Y., Ma, T., Zhou, C., & Xu, T. (2015). Nighttime light derived assessment of regional inequality of socioeconomic development in China. Remote Sensing, 7(2), 1242–1262. https://doi.org/10.3390/rs70201242
Acknowledgements
This research was supported by the Strategic Priority Program of the Chinese Academy of Sciences (CAS), Pan-Third Pole Environment Study for a Green Silk Road (XDA20040400), and the Tianshan Talent Project (Phase III) of the Xinjiang Uygur Autonomous Region. The authors would like to thank the Mr. Changjiang An and Dr.Jingchao Shi for providing helpful suggestions to improve this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, R., Zhang, F., Chan, N.W. et al. Multidimensional poverty measurement and spatial–temporal pattern analysis at county level in the arid area of Xinjiang, China. Environ Dev Sustain 25, 13805–13824 (2023). https://doi.org/10.1007/s10668-022-02629-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10668-022-02629-4