Identifying urban households in relative poverty with multi-source data: A case study in Zhengzhou

References

• Adesogan, A. T., Havelaar, A. H., McKune, S. L., Eilittä, M., & Dahl, G. E. (2020). Animal source foods: Sustainability problem or malnutrition and sustainability solution? Perspective matters. Global Food Security, 25, 100325. https://doi.org/10.1016/j.gfs.2019.100325
   Google Scholar
• Alkire, S., & Santos, M. E. (2014). Measuring acute poverty in the developing world: Robustness and scope of the Multidimensional Poverty Index. World Development, 59, 251–274. https://doi.org/10.1016/j.worlddev.2014.01.026
   Web of Science ®Google Scholar
• Allen, F., Qian, J., Zhang, C., & Zhao, M. (2012). China’s financial system: Opportunities and challenges. In J. Fan & R. Morck (Eds.), Capitalizing China (pp. 63–143). University of Chicago Press.
   Google Scholar
• Au, L. E., Gurzo, K., Gosliner, W., Webb, K. L., Crawford, P. B., & Ritchie, L. D. (2018). Eating school meals daily is associated with healthier dietary intakes: The Healthy Communities Study. Journal of the Academy of Nutrition and Dietetics, 118(8), 1474–1481. https://doi.org/10.1016/j.jand.2018.01.010
   PubMed Web of Science ®Google Scholar
• Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076. https://doi.org/10.1126/science.aac4420
   PubMed Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Carlin, B. P., & Louis, T. A. (1997). Bayes and empirical Bayes methods for data analysis. Statistics and Computing, 7(2), 153–154. https://doi.org/10.1023/A:1018577817064
   Google Scholar
• Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 68, 285–299. https://doi.org/10.1016/j.trc.2016.04.005
   PubMed Web of Science ®Google Scholar
• Chen, S., & Ravallion, M. (2007). Absolute poverty measures for the developing world, 1981–2004. Proceedings of the National Academy of Sciences, 104(43), 16757–16762. https://doi.org/10.1073/pnas.0702930104
   PubMed Web of Science ®Google Scholar
• Chen, S., & Ravallion, M. (2021). Reconciling the conflicting narratives on poverty in China. Journal of Development Economics, 153, 102711. https://doi.org/10.1016/j.jdeveco.2021.102711
   Web of Science ®Google Scholar
• Cho, D. I., Ogwang, T., & Opio, C. (2010). Simplifying the Water Poverty Index. Social Indicators Research, 97(2), 257–267. https://doi.org/10.1007/s11205-009-9501-2
   Web of Science ®Google Scholar
• Cutillo, A., Raitano, M., & Siciliani, I. (2020). Income-based and consumption-based measurement of absolute poverty: Insights from Italy. Social Indicators Research, 161, 689–710. https://doi.org/10.1007/s11205-020-02386-9
   Web of Science ®Google Scholar
• Deas, I., Robson, B., Wong, C., & Bradford, M. (2003). Measuring neighbourhood deprivation: A critique of the Index of Multiple Deprivation. Environment and Planning C, Government & Policy, 21(6), 883–903. https://doi.org/10.1068/c0240
   Web of Science ®Google Scholar
• Dehury, B., & Mohanty, S. K. (2015). Regional estimates of multidimensional poverty in India. Economics: The Open-Access, Open-Assessment E-Journal, 9(2015–36), 1–35. https://doi.org/10.5018/economics-ejournal.ja.2015-36
   Google Scholar
• Del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014). On the use of MapReduce for imbalanced big data using Random Forest. Information Sciences, 285, 112–137. https://doi.org/10.1016/j.ins.2014.03.043
   Web of Science ®Google Scholar
• Dhongde, S., & Haveman, R. (2017). Multi-dimensional deprivation in the US. Social Indicators Research, 133(2), 477–500. https://doi.org/10.1007/s11205-016-1379-1
   Web of Science ®Google Scholar
• Dollar, D., & Kraay, A. (2002). Growth is good for the poor. Journal of Economic Growth, 7(3), 195–225. https://doi.org/10.1023/A:1020139631000
   Web of Science ®Google Scholar
• Dong, L., Ratti, C., & Zheng, S. (2019). Predicting neighborhoods’ socioeconomic attributes using restaurant data. Proceedings of the National Academy of Sciences, 116(31), 15447–15452. https://doi.org/10.1073/pnas.1903064116
   PubMed Web of Science ®Google Scholar
• Foster, J. E. (1998). Absolute versus relative poverty. The American Economic Review, 88(2), 335–341. https://www.jstor.org/stable/116944
   Web of Science ®Google Scholar
• Gao, J., Qiao, W., Liu, Y., Li, Y., Tu, W., & Zhang, Y. (2018). The higher grain production, the more social deprivation? A case study of Henan province in traditional agricultural areas of China. Journal of Mountain Science, 15(1), 167–180. https://doi.org/10.1007/s11629-017-4441-9
   Web of Science ®Google Scholar
• Gong, X., & Wang, Y. (2021). Exploring dynamics of sports fan behavior using social media big data-A case study of the 2019 National Basketball Association Finals. Applied Geography, 129, 102438. https://doi.org/10.1016/j.apgeog.2021.102438
   Web of Science ®Google Scholar
• Guo, Y., Zhou, Y., & Liu, Y. (2019). Targeted poverty alleviation and its practices in rural China: A case study of Fuping county, Hebei Province. Journal of Rural Studies, 93, 430–440. https://doi.org/10.1016/j.jrurstud.2019.01.007
   Google Scholar
• Hood, A., & Waters, T. (2017). Living standards, poverty and inequality in the UK: 2016-2017 to 2021-2022. Report no. 127. Institute for Fiscal Studies. https://www.ifs.org.uk/publications/8957
   Google Scholar
• Huang, B., Zhou, Y., Li, Z., Song, Y., Cai, J., & Tu, W. (2020). Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study. Environment and Planning B: Urban Analytics and City Science, 47(9), 1543–1559. https://doi.org/10.1177/2399808319828730
   Google Scholar
• Huang, H., Yao, X. A., Krisp, J. M., & Jiang, B. (2021). Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions. Computers, Environment and Urban Systems, 90, 101712. https://doi.org/10.1016/j.compenvurbsys.2021.101712
   Web of Science ®Google Scholar
• Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894
   PubMed Web of Science ®Google Scholar
• Jolliffe, D. (2001). Measuring absolute and relative poverty: The sensitivity of estimated household consumption to survey design. Journal of Economic and Social Measurement, 27(1–2), 1–23. https://doi.org/10.3233/JEM-2003-0192
   Google Scholar
• Jolliffe, D., & Prydz, E. B. (2017). Societal poverty: A relative and relevant measure. The World Bank.
   Google Scholar
• Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992. https://doi.org/10.1016/j.cities.2020.102992
   Web of Science ®Google Scholar
• Kraemer, M. U., Yang, C., Gutierrez, B., Wu, C., Klein, B., Pigott, D. M., Group, O. C. D. W., du Plessis, L., Faria, N. R., & Li, R. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493–497. https://doi.org/10.1126/science.abb4218
   PubMed Web of Science ®Google Scholar
• Kuncheva, L. I. (2014). Combining pattern classifiers: Methods and algorithms. Wiley-Interscience.
   Google Scholar
• Langlois, A., & Kitchen, P. (2001). Identifying and measuring dimensions of urban deprivation in Montreal: An analysis of the 1996 census data. Urban Studies, 38(1), 119–139. https://doi.org/10.1080/00420980020014848
   Web of Science ®Google Scholar
• Li, S., Zhuang, C., Tan, Z., Gao, F., Lai, Z., & Wu, Z. (2021). Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China. Journal of Transport Geography, 91, 102974. https://doi.org/10.1016/j.jtrangeo.2021.102974
   Web of Science ®Google Scholar
• Li, X., Li, Y., Jia, T., Zhou, L., & Hijazi, I. H. (2021). The six dimensions of built environment on urban vitality: Fusion evidence from multi-source data. Cities, 121, 103482. https://doi.org/10.1016/j.cities.2021.103482
   Web of Science ®Google Scholar
• Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R News, 2(3), 18–22. http://www.CRAN.R-project.org/doc/Rnews/
   Google Scholar
• Liu, M., Feng, X., Wang, S., & Qiu, H. (2020). China’s poverty alleviation over the last 40 years: Successes and challenges. Australian Journal of Agricultural and Resource Economics, 64(1), 209–228. https://doi.org/10.1111/1467-8489.12353
   Web of Science ®Google Scholar
• Liu, X., Niu, N., Liu, X., Jin, H., Ou, J., Jiao, L., & Liu, Y. (2018). Characterizing mixed-use buildings based on multi-source big data. International Journal of Geographical Information Science, 32(4), 738–756. https://doi.org/10.1080/13658816.2017.1410549
   Web of Science ®Google Scholar
• Liu, Y., Liu, J., & Zhou, Y. (2017). Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. Journal of Rural Studies, 52, 66–75. https://doi.org/10.1016/j.jrurstud.2017.04.002
   Web of Science ®Google Scholar
• Lu, J., Zhou, S., Liu, L., & Li, Q. (2021). You are where you go: Inferring residents’ income level through daily activity and geographic exposure. Cities, 111, 102984. https://doi.org/10.1016/j.cities.2020.102984
   Web of Science ®Google Scholar
• Lu, Y., & Liu, Y. (2012). Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies. Computers, Environment and Urban Systems, 36(2), 105–108. https://doi.org/10.1016/j.compenvurbsys.2012.02.002
   Web of Science ®Google Scholar
• Madden, D. (2000). Relative or absolute poverty lines: A new approach. Review of Income and Wealth, 46(2), 181–199. https://doi.org/10.1111/j.1475-4991.2000.tb00954.x
   Google Scholar
• Magombeyi, M. T., & Odhiambo, N. M. (2018). Dynamic impact of FDI inflows on poverty reduction: Empirical evidence from South Africa. Sustainable Cities and Society, 39, 519–526. https://doi.org/10.1016/j.scs.2018.03.020
   Web of Science ®Google Scholar
• Marcuse, P., Connolly, J., Novy, J., Olivo, I., Potter, C., & Steil, J. (2009). Searching for the just city: Debates in urban theory and practice. Routledge.
   Google Scholar
• Müller, P. (2017). Poverty in Europe: Sociodemographics, portfolios, and consumption of wealth‐poor households. Poverty & Public Policy, 9(3), 306–330. https://doi.org/10.1002/pop4.188
   Google Scholar
• Niu, N., & Jin, H. (2021). Integrating multiple data to identify building functions in China’s urban villages. Environment and Planning B: Urban Analytics and City Science, 48(6), 1527–1542. https://doi.org/10.1177/2399808320938796
   Google Scholar
• Niu, N., Liu, X., Jin, H., Ye, X., Liu, Y., Li, X., Chen, Y., & Li, S. (2017). Integrating multi-source big data to infer building functions. International Journal of Geographical Information Science, 31(9), 1871–1890. https://doi.org/10.1080/13658816.2017.1325489
   Web of Science ®Google Scholar
• Niu, T., Chen, Y., & Yuan, Y. (2020). Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustainable Cities and Society, 54, 102014. https://doi.org/10.1016/j.scs.2020.102014
   Web of Science ®Google Scholar
• Noble, M., Wright, G., Smith, G., & Dibben, C. (2006). Measuring multiple deprivation at the small-area level. Environment & Planning A, 38(1), 169–185. https://doi.org/10.1068/a37168
   Web of Science ®Google Scholar
• Notten, G., & De Neubourg, C. (2011). Monitoring absolute and relative poverty: “Not enough” is not the same as “much less.” Review of Income and Wealth, 57(2), 247–269. https://doi.org/10.1111/j.1475-4991.2011.00443.x
   Web of Science ®Google Scholar
• Padda, I. U. H., & Hameed, A. (2018). Estimating multidimensional poverty levels in rural Pakistan: A contribution to sustainable development policies. Journal of Cleaner Production, 197, 435–442. https://doi.org/10.1016/j.jclepro.2018.05.224
   Web of Science ®Google Scholar
• Pirie, G. H. (1983). On spatial justice. Environment & Planning A, 15(4), 465–473. https://doi.org/10.1068/a150465
   Web of Science ®Google Scholar
• Rathore, M. M., Ahmad, A., & Paul, A. (2016). Real time intrusion detection system for ultra-high-speed big data environments. The Journal of Supercomputing, 72(9), 3489–3510. https://doi.org/10.1007/s11227-015-1615-5
   Web of Science ®Google Scholar
• Ravallion, M., Datt, G., & Van de Walle, D. (1991). Quantifying absolute poverty in the developing world. Review of Income and Wealth, 37(4), 345–361. https://doi.org/10.1111/j.1475-4991.1991.tb00378.x
   Google Scholar
• Ríos, S. A., & Muñoz, R. (2017). Land use detection with cell phone data using topic models: Case Santiago, Chile. Computers, Environment and Urban Systems, 61, 39–48. https://doi.org/10.1016/j.compenvurbsys.2016.08.007
   Web of Science ®Google Scholar
• Sun, J., & Xia, T. (2020). China’s anti-poverty strategy and post-2020 relative poverty line. China Economist, 15(3), 62–75. https://doi.org/10.19602/j.chinaeconomist.2020.05.05
   Google Scholar
• Townsend, P. (1987). Deprivation. Journal of Social Policy, 16(2), 125–146. https://doi.org/10.1017/S0047279400020341
   Web of Science ®Google Scholar
• Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J., & Li, Q. (2020). Portraying spatial dynamics of urban vibrancy using multisource urban big data: A comparative study in Shenzhen. Computers, Environment and Urban Systems, 80, 101428. https://doi.org/10.1016/j.compenvurbsys.2019.101428
   Web of Science ®Google Scholar
• UNDP. (2019). What changes after China defeats poverty? https://www.cn.undp.org/content/china/en/home/ourperspective/ourperspectivearticles/2019/what-changes-after-china-defeats-poverty-.html, Posted on December 23, 2019
   Google Scholar
• Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing: Theory and applications. Springer.
   Google Scholar
• Wagstaff, A., Flores, G., Smitz, M., Hsu, J., Chepynoga, K., & Eozenou, P. (2018). Progress on impoverishing health spending in 122 countries: A retrospective observational study. The Lancet Global Health, 6(2), e180–e192. https://doi.org/10.1016/S2214-109X(17)30486-2
   PubMed Web of Science ®Google Scholar
• Wang, D., & Li, F. (2016). Daily activity space and exposure: A comparative study of Hong Kong’s public and private housing residents’ segregation in daily life. Cities, 59, 148–155. https://doi.org/10.1016/j.cities.2015.09.010
   Web of Science ®Google Scholar
• Yamamori, T. (2019). The Smithian ontology of ‘relative poverty’: Revisiting the debate between Amartya Sen and Peter Townsend. Journal of Economic Methodology, 26(1), 70–80. https://doi.org/10.1080/1350178X.2018.1561081
   Web of Science ®Google Scholar
• Ye, X., & Yin, H. (2019). From eliminating absolute poverty to alleviating relative poverty: China’s history of poverty reduction and poverty reduction strategies after 2020. ReForm, 12, 5–15( Chinese). http://kns.cnki.net/kcms/detail/50.1012.F.20191224.0916.002.html
   Google Scholar
• Zhan, X., Ukkusuri, S. V., & Zhu, F. (2014). Inferring urban land use using large-scale social media check-in data. Networks and Spatial Economics, 14(3), 647–667. https://doi.org/10.1007/s11067-014-9264-4
   Web of Science ®Google Scholar
• Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., & Wu, J. (2019). Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh. Remote Sensing, 11(4), 375. https://doi.org/10.3390/rs11040375
   Web of Science ®Google Scholar
• Zheng, B. (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, 101(2), 337–356. https://doi.org/10.1016/S0304-4076(00)00088-9
   Web of Science ®Google Scholar