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Research Article

Measuring human settlement wealth index at 10-km resolution in low- and middle-income countries from 2005 to 2020 using multi-source remote sensing data

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Article: 2353160 | Received 15 Jan 2024, Accepted 04 May 2024, Published online: 16 May 2024

References

  • Andreano, Maria Simona, Roberto Benedetti, Federica Piersimoni, and Giovanni Savio. 2020. “Mapping Poverty of Latin American and Caribbean Countries from Heaven Through Night-Light Satellite Images.” Social Indicators Research 156 (2–3): 533–562. https://doi.org/10.1007/s11205-020-02267-1.
  • Barnett, Matthew J., Douglas Jackson-Smith, Joanna Endter-Wada, and Melissa Haeffner. 2020. “A Multilevel Analysis of the Drivers of Household Water Consumption in a Semi-Arid Region.” Science of the Total Environment 712: 136489. https://doi.org/10.1016/j.scitotenv.2019.136489.
  • Bataeva, Patimat, Hussein Chaplaev, and Ahmed Gachaev. 2020. “The Impact of Local Armed Conflicts on the Economic Performance of Countries in 1990–2019.” Economic Annals-XXI 182 (3–4): 41–48. https://doi.org/10.21003/ea.V182-05.
  • Bishop, Christopher M. 1995. Neural Networks for Pattern Recognition. New York: Oxford university press.
  • Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science 350 (6264): 1073–1076. https://doi.org/10.1126/science.aac4420.
  • Bruederle, Anna, and Roland Hodler. 2018. “Nighttime Lights as a Proxy for Human Development at the Local Level.” PLoS One 13 (9): e0202231. https://doi.org/10.1371/journal.pone.0202231.
  • Chen, Zuoqi, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Bin CongxiaoWang, and JianpingWu. Wu. 2021. “An Extended Time Series (2000–2018) of Global NPP-VIIRS-Like Nighttime Light Data from a Cross-Sensor Calibration.” Earth System Science Data 13 (3): 889–906. https://doi.org/10.5194/essd-13-889-2021.
  • Chi, Guanghua, Han Fang, Sourav Chatterjee, and Joshua E Blumenstock. 2022. “Microestimates of Wealth for all low-and Middle-Income Countries.” Proceedings of the National Academy of Sciences 119 (3): e2113658119. https://doi.org/10.1073/pnas.2113658119.
  • Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20: 273–297. https://doi.org/10.1007/Bf00994018.
  • De Nicolò, Silvia, Enrico Fabrizi, and Aldo Gardini. 2022. “Extended Beta Models for Poverty Mapping. An Application Integrating Survey and Remote Sensing Data in Bangladesh.” 10.6092/unibo/amsacta/7074. https://amsacta.unibo.it/id/eprint/7074.
  • Duque, Juan C, Jorge E Patino, Luis A Ruiz, and Josep E Pardo-Pascual. 2015. “Measuring Intra-Urban Poverty Using Land Cover and Texture Metrics Derived from Remote Sensing Data.” Landscape and Urban Planning 135: 11–21. https://doi.org/10.1016/j.landurbplan.2014.11.009.
  • Elvidge, Christopher D., Paul C. Sutton, Tilottama Ghosh, Benjamin T. Tuttle, Kimberly E. Baugh, Budhendra Bhaduri, and Edward Bright. 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.
  • Gouveia, João Pedro, Júlia Seixas, and Gavin Long. 2018. “Mining Households’ Energy Data to Disclose Fuel Poverty: Lessons for Southern Europe.” Journal of Cleaner Production 178: 534–550. https://doi.org/10.1016/j.jclepro.2018.01.021.
  • Han, Peng, Qing Zhang, Yanyun Zhao, and Frank Yonghong Li. 2021. “High-resolution Remote Sensing Data Can Predict Household Poverty in Pastoral Areas, Inner Mongolia, China.” Geography and Sustainability 2 (4): 254–263. https://doi.org/10.1016/j.geosus.2021.10.002.
  • Henderson, J. Vernon, Adam Storeygard, and David N. Weil. 2012. “Measuring Economic Growth from Outer Space.” American economic review 102 (2): 994–1028. https://doi.org/10.1257/aer.102.2.994.
  • Hu, Shan, Yong Ge, Mengxiao Liu, Zhoupeng Ren, and Xining Zhang. 2022. “Village-level Poverty Identification Using Machine Learning, High-Resolution Images, and Geospatial Data.” International Journal of Applied Earth Observation and Geoinformation 107: 102694. https://doi.org/10.1016/j.jag.2022.102694.
  • Imran, Muhammad, Alfred Stein, and Raúl Zurita-Milla. 2014. “Investigating Rural Poverty and Marginality in Burkina Faso Using Remote Sensing-Based Products.” International Journal of Applied Earth Observation and Geoinformation 26: 322–334. https://doi.org/10.1016/j.jag.2013.08.012.
  • Jean, Neal, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon. 2016. “Combining Satellite Imagery and Machine Learning to Predict Poverty.” Science 353 (6301): 790–794. https://doi.org/10.1126/science.aaf7894.
  • Joshua Evan, Blumenstock 2016. “Fighting Poverty with Data.” Science 353 (6301): 753–754. https://doi.org/10.1126/science.aah5217.
  • Kešeljević, Aleksandar, and Rok Spruk. 2023. “Estimating the Effects of Syrian Civil War.” Empirical Economics:1–33. https://doi.org/10.1007/s00181-023-02470-2.
  • LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
  • Lee, Kamwoo, and Jeanine Braithwaite. 2022. “High-resolution Poverty Maps in Sub-Saharan Africa.” World Development 159: 106028. https://doi.org/10.1016/j.worlddev.2022.106028.
  • Lerman, P. M. 1980. “Fitting Segmented Regression Models by Grid Search.” Journal of the Royal Statistical Society Series C: Applied Statistics 29 (1): 77–84. https://doi.org/10.2307/2346413.
  • Li, Guie, Zhongliang Cai, Xiaojian Liu, Ji Liu, and Shiliang Su. 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, Guie, Liyun Chang, Xiaojian Liu, Shiliang Su, Zhongliang Cai, Xinran Huang, and Bozhao Li. 2019. “Monitoring the Spatiotemporal Dynamics of Poor Counties in China: Implications for Global Sustainable Development Goals.” Journal of Cleaner Production 227: 392–404. https://doi.org/10.1016/j.jclepro.2019.04.135.
  • Li, Chengsong, Wunian Yang, Qiaolin Tang, Xiaolu Tang, Junjie Lei, Mingyan Wu, and Shuyue Qiu. 2020. “Detection of Multidimensional Poverty Using Luojia 1-01 Nighttime Light Imagery.” Journal of the Indian Society of Remote Sensing 48 (7): 963–977. https://doi.org/10.1007/s12524-020-01126-3.
  • Lin, Yi, Tinghui Zhang, Xuanqi Liu, Jie Yu, Jonathan Li, and Kyle Gao. 2022. “Dynamic Monitoring and Modeling of the Growth-Poverty-Inequality Trilemma in the Nile River Basin with Consistent Night-Time Data (2000–2020).” International Journal of Applied Earth Observation and Geoinformation 112: 102903. https://doi.org/10.1016/j.jag.2022.102903.
  • Liu, Mingyue, Xiaolong Feng, Sangui Wang, and Huanguang Qiu. 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.
  • Malah Kuete, Yselle F., and Simplice A. Asongu. 2023. “Infrastructure Development as a Prerequisite for Structural Change in Africa.” Journal of the Knowledge Economy 14 (2): 1386–1412. https://doi.org/10.1007/s13132-022-00989-w.
  • Masaki, Takaaki, David Newhouse, Ani Rudra Silwal, Adane Bedada, and Ryan Engstrom. 2022. “Small Area Estimation of non-Monetary Poverty with Geospatial Data.” Statistical Journal of the IAOS 38 (3): 1035–1051. https://doi.org/10.3233/SJI-210902.
  • Newhouse, David Locke, Joshua D. Merfeld, Anusha Ramakrishnan, Tom Swartz, and Partha Lahiri. 2022. “Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning.” Available at SSRN 4235976. https://doi.org/10.2139/ssrn.4235976.
  • Ni, Ye, Xutao Li, Yunming Ye, Yan Li, Chunshan Li, and Dianhui Chu. 2020. “An Investigation on Deep Learning Approaches to Combining Nighttime and Daytime Satellite Imagery for Poverty Prediction.” IEEE Geoscience and Remote Sensing Letters, 1–5. https://doi.org/10.1109/LGRS.2020.3006019.
  • Niu, Tong, Yimin Chen, and Yuan Yuan. 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.
  • Noor, Abdisalan M., Victor A. Alegana, Peter W. Gething, Andrew J. Tatem, and Robert W. Snow. 2008. “Using Remotely Sensed Night-Time Light as a Proxy for Poverty in Africa.” Population Health Metrics 6 (1): 1–13. https://doi.org/10.1186/1478-7954-6-5.
  • Opoku, Eric Evans Osei, and Isabel Kit-Ming Yan. 2019. “Industrialization as Driver of Sustainable Economic Growth in Africa.” The Journal of International Trade & Economic Development 28 (1): 30–56. https://doi.org/10.1080/09638199.2018.1483416.
  • Perez, Anthony, Christopher Yeh, George Azzari, Marshall Burke, David Lobell, and Stefano Ermon. 2017. “Poverty prediction with public landsat 7 satellite imagery and machine learning.” arXiv preprint arXiv:1711.03654. https://doi.org/10.48550/arXiv.1711.03654.
  • Permatasari, Novia, Bagaskoro Cahyo Laksono, and Azka Ubaidillah. 2023. “Small Area Estimation of Poverty using Remote Sensing Data (Case Study: Expenditure Per Capita Estimation of Very Poor Households in West Java, Indonesia).” Proc. Of ISI World Statistics Congress (WSC), International Statistical Institute, Ottawa, Canada. https://www.isi-next.org/media/abstracts/ottawa-2023_ca3836ef5000fd509ab002bf356175c1.pdf.
  • Pokhriyal, Neeti, and Damien Christophe Jacques. 2017. “Combining Disparate Data Sources for Improved Poverty Prediction and Mapping.” Proceedings of the National Academy of Sciences 114 (46): E9783–E9792. https://doi.org/10.1073/pnas.1700319114.
  • Putri, Salwa Rizqina, Arie Wahyu Wijayanto, and Setia Pramana. 2023. “Multi-Source Satellite Imagery and Point of Interest Data for Poverty Mapping in East Java, Indonesia: Machine Learning and Deep Learning Approaches.” Remote Sensing Applications: Society and Environment 29: 100889. https://doi.org/10.1016/j.rsase.2022.100889.
  • Puttanapong, Nattapong, Nutchapon Prasertsoong, and Wichaya Peechapat. 2023. “Predicting Provincial Gross Domestic Product using Satellite Data and Machine Learning Methods: A Case Study of Thailand.” Asian Development Review 40 (02): 39–85. https://doi.org/10.1142/S0116110523400024.
  • Ratledge, Nathan, Gabe Cadamuro, Brandon de la Cuesta, Matthieu Stigler, and Marshall Burke. 2022. “Using Machine Learning to Assess the Livelihood Impact of Electricity Access.” Nature 611 (7936): 491–495. https://doi.org/10.1038/s41586-022-05322-8.
  • Ridho, Helen Cantika, Laura Aisyatul, and Rindang Bangun Prasetyo. 2023. “Small Area Estimation of Multidimensional Poverty in East Java Province using Satellite Imagery.” Proceedings of The International Conference on Data Science and Official Statistics, https://doi.org/10.34123/icdsos.v2023i1.417.
  • Rutstein, Shea Oscar, and Sarah Staveteig. 2014. Making the Demographic and Health Surveys Wealth Index Comparable. Vol. 9. Rockville, MD: ICF International.
  • Sandefur, Justin, and Amanda Glassman. 2015. “The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics.” The Journal of Development Studies 51 (2): 116–132. https://doi.org/10.1080/00220388.2014.968138.
  • Schmid, Timo, Fabian Bruckschen, Nicola Salvati, and Till Zbiranski. 2017. “Constructing Sociodemographic Indicators for National Statistical Institutes by Using Mobile Phone Data: Estimating Literacy Rates in Senegal.” Journal of the Royal Statistical Society Series A: Statistics in Society 180 (4): 1163–1190. https://doi.org/10.1111/rssa.12305.
  • Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew Dabalen. 2015. “Data Deprivation: Another Deprivation to End.” World Bank Policy Research Working Paper (7252). https://ssrn.com/abstract=2600334.
  • Shao, Zixuan, and Xi Li. 2023. “Multi-scale Estimation of Poverty Rate Using Night-Time Light Imagery.” International Journal of Applied Earth Observation and Geoinformation 121: 103375. https://doi.org/10.1016/j.jag.2023.103375.
  • Shi, Kaifang, Zhijian Chang, Zuoqi Chen, Jianping Wu, and Bailang Yu. 2020. “Identifying and Evaluating Poverty Using Multisource Remote Sensing and Point of Interest (POI) Data: A Case Study of Chongqing, China.” Journal of Cleaner Production 255. https://doi.org/10.1016/j.jclepro.2020.120245.
  • Smits, Jeroen, and Roel Steendijk. 2015. “The International Wealth Index (IWI).” Social Indicators Research 122 (1): 65–85. https://doi.org/10.1007/s11205-014-0683-x.
  • Smythe, Isabella S., and Joshua E. Blumenstock. 2022. “Geographic Microtargeting of Social Assistance with High-Resolution Poverty Maps.” Proceedings of the National Academy of Sciences 119 (32): e2120025119. https://doi.org/10.1073/pnas.2120025119.
  • Tian, Fuyou, Bingfang Wu, Hongwei Zeng, Gary R. Watmough, Miao Zhang, and Yurui Li. 2022. “Detecting the Linkage Between Arable Land use and Poverty Using Machine Learning Methods at Global Perspective.” Geography and Sustainability 3 (1): 7–20. https://doi.org/10.1016/j.geosus.2022.01.001.
  • United Nations. 2023a. “United Nations Sustainable Development Goals(SDG).” Accessed August 28, 2023a. https://www.un.org/sustainabledevelopment/sustainable-development-goals/.
  • United Nations. 2023b. “Economic Development in Africa Report 2021.” August 28, 2023b. https://unctad.org/publication/economic-development-africa-report-2021.
  • Wang, Wen, Hui Cheng, and Li Zhang. 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.
  • Watmough, Gary R., Charlotte L. J. Marcinko, Clare Sullivan, Kevin Tschirhart, Patrick K. Mutuo, Cheryl A. Palm, and Jens-Christian Svenning. 2019. “Socioecologically Informed use of Remote Sensing Data to Predict Rural Household Poverty.” Proceedings of the National Academy of Sciences 116 (4): 1213–1218. https://doi.org/10.1073/pnas.1812969116.
  • Weiss, D. J., A. Nelson, H. S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, et al. 2018. “A Global Map of Travel Time to Cities to Assess Inequalities in Accessibility in 2015.” Nature 553 (7688): 333–336. https://doi.org/10.1038/nature25181.
  • World Bank. 2023a. “World Bank Open Data.” Accessed August 28, 2023a. https://data.worldbank.org.cn/indicator/SI.POV.DDAY.
  • World Bank. 2023b. “World Bank Open Data.” Accessed August 28, 2023b. https://data.worldbank.org.cn/.
  • World Bank. 2023c. “Poverty and Inequality Platform.” Accessed August 28, 2023c. https://pip.worldbank.org.
  • Worldpop. 2023. “Age and Sex Structres.” Accessed March 12, 2023 https://hub.worldpop.org/geodata/listing?id=65.
  • Xu, Jianbin, Jie Song, Baochao Li, Dan Liu, and Xiaoshu Cao. 2021. “Combining Night Time Lights in Prediction of Poverty Incidence at the County Level.” Applied Geography 135: 102552. https://doi.org/10.1016/j.apgeog.2021.102552.
  • Yao, Yao, Jianfeng Zhou, Zhenhui Sun, Qingfeng Guan, Zhiqiang Guo, Yin Xu, Jinbao Zhang, Ye Hong, Yuyang Cai, and Ruoyu Wang. 2023. “Estimating China’s Poverty Reduction Efficiency by Integrating Multi-Source Geospatial Data and Deep Learning Techniques.” Geo-spatial Information Science, 1–17. https://doi.org/10.1080/10095020.2023.2165975.
  • Yeh, Christopher, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke. 2020. “Using Publicly Available Satellite Imagery and Deep Learning to Understand Economic Well-Being in Africa.” Nature Communications 11 (1): 2583. https://doi.org/10.1038/s41467-020-16185-w.
  • Yin, Jian, Yuanhong Qiu, and Bin Zhang. 2020. “Identification of Poverty Areas by Remote Sensing and Machine Learning: A Case Study in Guizhou, Southwest China.” ISPRS International Journal of Geo-Information 10 (1), https://doi.org/10.3390/ijgi10010011.
  • Yu, Bailang, Kaifang Shi, Yingjie Hu, Chang Huang, Zuoqi Chen, and Jianping Wu. 2015. “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.
  • Zhao, Xizhi, Bailang Yu, Yan Liu, Zuoqi Chen, Qiaoxuan Li, Congxiao Wang, and Jianping Wu. 2019. “Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh.” Remote Sensing 11 (4): 375–393. https://doi.org/10.3390/rs11040375.
  • Zhou, Yang, and Yansui Liu. 2022. “The Geography of Poverty: Review and Research Prospects.” Journal of Rural Studies 93: 408–416. https://doi.org/10.1016/j.jrurstud.2019.01.008.
  • Ziulu, Virginia, Jessica Meckler Gonzalo, Hernández Licona, and Jozef Vaessen. 2022. “Poverty Mapping: Innovative Approaches to Creating Poverty Maps with New Data Sources.” In IEG Methods and Evaluation Capacity Development Working Paper Series. Independent Evaluation Group. Washington, DC: World Bank. https://ieg.worldbankgroup.org/evaluations/poverty-mapping-innovative-approaches-creating-poverty-maps-new-data-sources.