Estimating asset wealth using multidimensional luminous information in areas lacking nighttime light

References

• Bennett, M. M., and L. C. Smith. 2017. “Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics.” Remote Sensing of Environment 192: 176–197. https://doi.org/10.1016/j.rse.2017.01.005.
   Web of Science ®Google Scholar
• Benton, R. A., and L. A. Keister. 2017. “The Lasting Effect of Intergenerational Wealth Transfers: Human Capital, Family Formation, and Wealth.” Social Science Research 68: 1–14. https://doi.org/10.1016/j.ssresearch.2017.09.006.
   PubMed Web of Science ®Google Scholar
• Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24: 123–140. https://doi.org/10.1007/BF00058655.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Briggs, R. C. 2018. “Poor Targeting: A Gridded Spatial Analysis of the Degree to Which Aid Reaches the Poor in Africa.” World Development 103: 133–148. https://doi.org/10.1016/j.worlddev.2017.10.020.
   Web of Science ®Google Scholar
• Briggs, R. C. 2021. “Why Does Aid Not Target the Poorest?” International Studies Quarterly 65 (3): 739–752. https://doi.org/10.1093/isq/sqab035.
   Web of Science ®Google Scholar
• Chen, X., and W. D. Nordhaus. 2011. “Using Luminosity Data as a Proxy for Economic Statistics.” Proceedings of the National Academy of Sciences 108 (21): 8589–8594. https://doi.org/10.1073/pnas.1017031108.
   PubMed Web of Science ®Google Scholar
• Chen, Z. Q., Y. Wei, K. F. Shi, Z. Y. Zhao, C. X. Wang, B. Wu, B. W. Qiu, and B. L. Yu. 2022. “The Potential of Nighttime Light Remote Sensing Data to Evaluate the Development of Digital Economy: A Case Study of China at the City Level.” Computers, Environment and Urban Systems 92: 101749. https://doi.org/10.1016/j.compenvurbsys.2021.101749.
   Web of Science ®Google Scholar
• Diaz-Sarachaga, J. M., D. Jato-Espino, and D. Castro-Fresno. 2018. “Is the Sustainable Development Goals (SDG) Index an Adequate Framework to Measure the Progress of the 2030 Agenda?” Sustainable Development 26 (6): 663–671. https://doi.org/10.1002/sd.1735.
   Web of Science ®Google Scholar
• Elvidge, C. D., K. Baugh, M. Zhizhin, F. C. Hsu, and T. Ghosh. 2017. “VIIRS Night-Time Lights.” International Journal of Remote Sensing 38 (21): 5860–5879. https://doi.org/10.1080/01431161.2017.1342050.
   Web of Science ®Google Scholar
• Elvidge, C. D., P. C. Sutton, T. Ghosh, B. T. Tuttle, K. E. Baugh, B. Bhaduri, and E. 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.
   Web of Science ®Google Scholar
• Elvidge, C. D., M. Zhizhin, T. Ghosh, F. C. Hsu, and J. Taneja. 2021. “Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019.” Remote Sensing 13 (5): 922. https://doi.org/10.3390/rs13050922.
   Web of Science ®Google Scholar
• Filho, W. L., V. O. Lovren, M. Will, A. L. Salvia, and F. Frankenberger. 2021. “Poverty: A Central Barrier to the Implementation of the UN Sustainable Development Goals.” Environmental Science & Policy 125: 96–104. https://doi.org/10.1016/j.envsci.2021.08.020.
   Web of Science ®Google Scholar
• Filmer, D., and K. Scott. 2012. “Assessing Asset Indices.” Demography 49 (1): 359–392. https://doi.org/10.1007/s13524-011-0077-5.
   PubMed Web of Science ®Google Scholar
• Gibson, J., S. Olivia, and G. Boe-Gibson. 2020. “Night Lights in Economics: Sources and Uses.” Journal of Economic Surveys 34 (5): 955–980. https://doi.org/10.1111/joes.12387.
   Web of Science ®Google Scholar
• Goswami, S., and A. Chakrabarti. 2012. “Quartile Clustering: A Quartile Based Technique for Generating Meaningful Clusters.” arXiv preprint arXiv:1203.4157.
   Google Scholar
• Gu, Q. H., J. N. Tian, X. X. Li, and S. Jiang. 2022. “A Novel Random Forest Integrated Model for Imbalanced Data Classification Problem.” Knowledge-Based Systems 250: 109050. https://doi.org/10.1016/j.knosys.2022.109050.
   Web of Science ®Google Scholar
• Halisçelik, E., and M. A. Soytas. 2019. “Sustainable Development from Millennium 2015 to Sustainable Development Goals 2030.” Sustainable Development 27 (4): 545–572. https://doi.org/10.1002/sd.1921.
   Web of Science ®Google Scholar
• Haveman, R., and E. N. Wolff. 2004. “The Concept and Measurement of Asset Poverty: Levels, Trends and Composition for the U.S.,1983–2001.” The Journal of Economic Inequality 2: 145–169. https://doi.org/10.1007/s10888-004-4387-3.
   Google Scholar
• Hu, S., Y. Ge, M. X. Liu, Z. P. Ren, and X. N. 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.
   Web of Science ®Google Scholar
• Jean, N., M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon. 2016. “Combining Satellite Imagery and Machine Learning to Predict Poverty.” Science 353: 790–794. https://doi.org/10.1126/science.aaf7894.
   PubMed Web of Science ®Google Scholar
• Kyba, C. C. M., T. Kuester, A. S. D. Miguel, K. Baugh, A. Jechow, F. Hölker, J. Bennie, C. D. Elvidge, K. J. Gaston, and L. Guanter. 2017. “Artificially Lit Surface of Earth at Night Increasing in Radiance and Extent.” Science Advances 3: e1701528. https://doi.org/10.1126/sciadv.1701528.
   PubMed Web of Science ®Google Scholar
• Lee, K., and J. Braithwaite. 2022. “High-resolution Poverty Maps in Sub-Saharan Africa.” World Development 159: 106028. https://doi.org/10.1016/j.worlddev.2022.106028.
   Web of Science ®Google Scholar
• Levin, N., C. C. M. Kyba, Q. L. Zhang, A. S. d. Miguel, M. O. Román, X. Li, B. A. Portnov, et al. 2020. “Remote Sensing of Night Lights: A Review and an Outlook for the Future.” Remote Sensing of Environment 237: 111443. https://doi.org/10.1016/j.rse.2019.111443.
   Web of Science ®Google Scholar
• Lin, Y., T. H. Zhang, X. Q. Liu, J. Yu, J. Li, and K. 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.
   Web of Science ®Google Scholar
• Marconcini, M., A. Metz-Marconcini, S. Ureyen, D. Palacios-Lopez, W. Hanke, F. Bachofer, J. Zeidler, et al. 2020. “Outlining Where Humans Live, the World Settlement Footprint 2015.” Scientific Data 7 (1): 242. https://doi.org/10.1038/s41597-020-00580-5.
   PubMed Web of Science ®Google Scholar
• Matsuki, K., V. Kuperman, and J. A. Van Dyke. 2016. “The Random Forests Statistical Technique: An Examination of Its Value for the Study of Reading.” Scientific Studies of Reading 20 (1): 20–33. https://doi.org/10.1080/10888438.2015.1107073.
   PubMed Web of Science ®Google Scholar
• McCallum, I., C. C. M. Kyba, J. C. L. Bayas, E. Moltchanova, M. Cooper, J. C. Cuaresma, S. Pachauri, et al. 2022. “Estimating Global Economic Well-Being with Unlit Settlements.” Nature Communications 13 (1): 2459. https://doi.org/10.1038/s41467-022-30099-9.
   PubMed Web of Science ®Google Scholar
• Mellander, C., J. Lobo, K. Stolarick, and Z. Matheson. 2015. “Night-Time Light Data: A Good Proxy Measure for Economic Activity?” PLoS One 10 (10): e0139779. https://doi.org/10.1371/journal.pone.0139779.
   PubMed Web of Science ®Google Scholar
• Miller, S. D., S. P. Mills, C. D. Elvidge, D. T. Lindsey, T. F. Lee, and J. D. Hawkins. 2012. “Suomi Satellite Brings to Light a Unique Frontier of Nighttime Environmental Sensing Capabilities.” Proceedings of the National Academy of Sciences 109 (39): 15706–15711. https://doi.org/10.1073/pnas.1207034109.
   PubMed Web of Science ®Google Scholar
• Niu, T., Y. M. Chen, and Y. 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.
   Web of Science ®Google Scholar
• Rutstein, S. O., and K. Johnson. 2004. The DHS Wealth Index. DHS Comparative Reports NO. 6. Calverton, USA: ORC Macro. https://dhsprogram.com/pubs/pdf/CR6/CR6.pdf.
   Google Scholar
• Rutstein, S. O., and S. Staveteig. 2014. Making the Demographic and Health Surveys Wealth Index Comparable. DHS Methodological Reports NO. 9. Rockville, USA: ICF International. https://iussp.org/sites/default/files/event_call_for_papers/ComparativeWealth-DRAFT-IUSSP.pdf.
   Google Scholar
• Sahn, D. E., and D. Stifel. 2003. “Exploring Alternative Measures of Welfare in the Absence of Expenditure Data.” Review of Income and Wealth 49 (4): 463–489. https://doi.org/10.1111/j.0034-6586.2003.00100.x.
   Web of Science ®Google Scholar
• Schmidt-Traub, G., C. Kroll, K. Teksoz, D. Durand-Delacre, and J. D. Sachs. 2017. “National Baselines for the Sustainable Development Goals Assessed in the SDG Index and Dashboards.” Nature Geoscience 10 (8): 547–555. https://doi.org/10.1038/ngeo2985.
   Web of Science ®Google Scholar
• Shao, Z. X., and X. 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.
   Web of Science ®Google Scholar
• Shi, K. F., Z. J. Chang, Z. Q. Chen, J. P. Wu, and B. L. 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: 120245. https://doi.org/10.1016/j.jclepro.2020.120245.
   Web of Science ®Google Scholar
• Smith, B., and S. Wills. 2018. “Left in the Dark? Oil and Rural Poverty.” Journal of the Association of Environmental and Resource Economists 5 (4): 865–904. https://doi.org/10.1086/698512.
   Web of Science ®Google Scholar
• Smits, J., and R. Steendijk. 2015. “The International Wealth Index (IWI).” Social Indicators Research 122 (1): 65–85. https://doi.org/10.1007/s11205-014-0683-x.
   Web of Science ®Google Scholar
• Stokes, E. C., M. O. Romn, Z. S. Wang, C. C. M. Kyba, S. D. Miller, T. Storch, and K. R. Gurney. 2021. “Retired Satellites: A Chance to Shed Light.” Science 373: 1451–1452. https://doi.org/10.1126/science.abl9965.
   PubMed Web of Science ®Google Scholar
• Weidmann, N. B., and S. Schutte. 2017. “Using Night Light Emissions for the Prediction of Local Wealth.” Journal of Peace Research 54 (2): 125–140. https://doi.org/10.1177/0022343316630359.
   Web of Science ®Google Scholar
• Xu, J. B., J. Song, B. C. Li, D. Liu, and X. S. 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.
   Web of Science ®Google Scholar
• Yeh, C., A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. 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.
   PubMed Web of Science ®Google Scholar