• Chapter 1 (“Measuring Poverty”) from: Banerjee, A.V., Benabou, R., and Mookherjee, D. (2006). Understanding Poverty. Oxford University Press, Oxford ; New York.
• Burke, M., Driscoll, A., Lobell, D., and Ermon, S. (2020). Using Satellite Imagery to Understand and Promote Sustainable Development. Science 371.
• Chi, G., Fang, H., Chatterjee, S., and Blumenstock, J. E. (2022). Microestimates of wealth for all low-and middle-income countries. Proceedings of the National Academy of Sciences, 119(3), e2113658119.
• Sahoo, Roshni, Joshua Blumenstock, Paul Niehaus, Leo Selker, and Stefan Wager. 2025. “What Would It Cost to End Extreme Poverty?” Working Paper No. 34583. Working Paper Series. National Bureau of Economic Research, December. https://doi.org/10.3386/w34583.

For those with less background in development economics and applied microeconomics

• Banerjee, A.V., Duflo, E., 2007. The economic lives of the poor. Journal of Economic Perspectives 21, 141–167.
• Olken, Benjamin A. 2020. “Banerjee, Duflo, Kremer, and the Rise of Modern Development Economics.” The Scandinavian Journal of Economics 122 (3): 853–78.
• Athey, S., Imbens, G.W., 2017. The State of Applied Econometrics: Causality and Policy Evaluation. Journal of Economic Perspectives 31, 3–32.
• Duflo, E., Glennerster, R., Kremer, M., 2007. Using randomization in development economics research: A toolkit. Handbook of Development Economics 4, 3895–3962.

On traditional data and measurement gaps

• Ravallion, M. (2020). On measuring global poverty. Annual Review of Economics, 12, 167-188.
• Cameron, Grant J., Hai‐Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin. 2021. “Measuring the Statistical Capacity of Nations*.” Oxford Bulletin of Economics and Statistics 83 (4): 870–96. https://doi.org/10.1111/obes.12421.
• Deaton, Angus. 2016. “Measuring and Understanding Behavior, Welfare, and Poverty.” American Economic Review 106 (6): 1221–43. https://doi.org/10.1257/aer.106.6.1221.
• 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): 116–32. https://doi.org/10.1080/00220388.2014.968138.
• Aiken, Emily, Joshua E Blumenstock, and Tim Ohlenberg. 2025. “Moving Targets: Can Machine Learning Help Proxy Means Tests Better Account for Poverty Dynamics?” Working Paper.
• Espey, Jessica M., Andrew J. Tatem, and Dana R. Thomson. 2025. “Disappearing People: A Global Demographic Data Crisis Threatens Public Policy.” Science 388 (6753): 1277–80. https://doi.org/10.1126/science.adx8683.

Overviews of satellite imagery in development

• Donaldson, D., Storeygard, A., 2016. The View from Above: Applications of Satellite Data in Economics. Journal of Economic Perspectives 30, 171–198. doi:10.1257/jep.30.4.171
• Khan, M. (2018). Launching into space: Using satellite imagery in financial services. Partnership for finance in a digital Africa.
• Septiandri, A. et al. (2023). WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT? In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23).
• Carnap, Tillmann von, Reza M. Asiyabi, Paul Dingus, and Anna Tompsett. 2026. “Using Satellite Imagery to Map Rural Marketplaces and Monitor Their Activity at High Frequency.” arXiv:2407.12953. Preprint, arXiv, January 9. https://doi.org/10.48550/arXiv.2407.12953.
• Aiken, Emily, Esther Rolf, and Joshua Blumenstock. 2023. “Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy.” IJCAI, ahead of print. https://doi.org/10.48550/arXiv.2305.01783.

Poverty mapping with satellites and remote sensing

• Engstrom, Ryan, Jonathan Hersh, and David Newhouse. 2022. “Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-Being.” The World Bank Economic Review 36 (2): 382–412. https://doi.org/10.1093/wber/lhab015.
• Head, A., Manguin, M., Tran, N., Blumenstock, J.E. 2017. Can Human Development be Measured with Satellite Imagery? ICTD ’17: Proceedings of the Ninth International Conference on Information and Communication Technologies and Development.
• Henderson, J.V., Storeygard, A., Weil, D.N., 2012. Measuring Economic Growth from Outer Space. American Economic Review 102, 994–1028. doi:10.1257/aer.102.2.994
• Martinez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates? Journal of Political Economy, 130(10), 2731-2769.
• Sherman, Luke, Jonathan Proctor, Hannah Druckenmiller, Heriberto Tapia, and Solomon M. Hsiang. 2023. “Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-Learning.” Working Paper No. 31044. Working Paper Series. National Bureau of Economic Research, March. https://doi.org/10.3386/w31044.
• Rolf, Esther, Jonathan Proctor, Tamma Carleton, et al. 2021. “A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery.” Nature Communications 12 (1): 4392. https://doi.org/10.1038/s41467-021-24638-z.
• Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., Burke, M., 2020. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications 11, 2583. https://doi.org/10.1038/s41467-020-16185-w

Mapping built areas and infrastructure

• Cadamuro, G., Muhebwa, A., Taneja, J., 2018. Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery. arXiv:1812.01699
• Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., & Zhang, Y. (2019). Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sensing, 11(7), 830.
• Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., … & Dang, H. A. H. (2017). Mapping the world population one building at a time. arXiv preprint arXiv:1712.05839.

Satellite imagery in impact evaluation and change detection

• Ratledge, N., Cadamuro, G., de la Cuesta, B., Stigler, M., & Burke, M. (2022). Using machine learning to assess the livelihood impact of electricity access. Nature, 611(7936), 491-495.
• Zheng, Zhuo, Timothy Wu, Richard Lee, et al. 2026. “Dynamic, High-Resolution Poverty Measurement in Data-Scarce Environments.” Journal of Development Economics 179 (February): 103691. https://doi.org/10.1016/j.jdeveco.2025.103691.
• Rambachan, Ashesh, Rahul Singh, and Davide Viviano. 2025. “Program Evaluation with Remotely Sensed Outcomes.” arXiv:2411.10959. Preprint, arXiv, October 27. https://doi.org/10.48550/arXiv.2411.10959.
• Huang, L. Y., Hsiang, S. M., & Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. National Bureau of Economic Research Working Paper No. 29105.
• Klemmer, Konstantin, Esther Rolf, Caleb Robinson, Lester Mackey, and Marc Rußwurm. 2024. “SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery.” arXiv:2311.17179. Preprint, arXiv, April 12. https://doi.org/10.48550/arXiv.2311.17179.
• Michler, J. D., Josephson, A., Kilic, T., & Murray, S. (2022). Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data. Journal of Development Economics, 158, 102927.
• Mueller, Hannes, Andre Groeger, Jonathan Hersh, Andrea Matranga, and Joan Serrat. 2021. “Monitoring War Destruction from Space Using Machine Learning.” Proceedings of the National Academy of Sciences 118 (23): e2025400118. https://doi.org/10.1073/pnas.2025400118.
• Deininger, Klaus, Daniel Ayalew Ali, Nataliia Kussul, Andrii Shelestov, Guido Lemoine, and Hanna Yailimova. 2023. “Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security.” Food Policy 115 (February): 102418. https://doi.org/10.1016/j.foodpol.2023.102418.

AI and development

• Adams, Rachel, Ayantola Alayande, Zameer Brey, et al. 2023. “A New Research Agenda for African Generative AI.” Nature Human Behaviour, October 6, 1–3. https://doi.org/10.1038/s41562-023-01735-1.
• De-Arteaga, M. et al. (2018). Machine Learning for the Developing World. ACM Transactions on Management Information Systems 9.
• Sambasivan, N. and Holbrook, J. (2018). Toward responsible AI for the next billion users. ACM Interactions 26, 68–71.

• Aiken, Emily, Joshua E. Blumenstock, Sveta Milusheva, and Merritt Smith. 2026. “Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries.” American Economic Review: Papers and Proceedings.
• Wesolowski, A., Eagle, N., Noor, A.M., Snow, R.W., and Buckee, C.O. (2012). Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya. PLOS ONE 7, e35319.
• Miyauchi, Yuhei, Kentaro Nakajima, and Stephen J. Redding. 2025. “The Economics of Spatial Mobility: Theory and Evidence Using Smartphone Data*.” The Quarterly Journal of Economics 140 (4): 2507–70. https://doi.org/10.1093/qje/qjaf038.

Overviews of mobile phone data in development

• Blondel, V.D., Decuyper, A., Krings, G., 2015. A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 10. doi:10.1140/epjds/s13688-015-0046-0
• UN Global Pulse, 2017. The State of Mobile Data for Social Good
• United Nations. 2019. Handbook on the Use of Mobile Phone Data for Official Statistics.
• Okmi, Mohammed, Lip Yee Por, Tan Fong Ang, et al. 2023. “Mobile Phone Data: A Survey of Techniques, Features, and Applications.” Sensors 23 (2). https://doi.org/10.3390/s23020908.
• Oliver, Nuria, Bruno Lepri, Harald Sterly, et al. 2020. “Mobile Phone Data for Informing Public Health Actions across the COVID-19 Pandemic Life Cycle.” Science Advances 6 (23): eabc0764. https://doi.org/10.1126/sciadv.abc0764.
• Watch some of the videos from this conference on mobile data for development

Mobile phone use and demographics

• Blumenstock, J.E., Eagle, N., 2012. Divided We Call: Disparities in Access and Use of Mobile Phones in Rwanda. Information Technology and International Development 8, 1–16.
• Aiken, E., Thakur, V., & Blumenstock, J. (2022). Phone Sharing and Cash Transfers in Togo: Quantitative Evidence from Mobile Phone Data. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (pp. 214-231).

Privacy and access (See also readings under Privacy week)

• Landau, S. (2016). Transactional information is remarkably revelatory. Proceedings of the National Academy of Sciences of the United States of America, 113(20), 5467–5469.
• Abebe, R. et al. (2021). Narratives and Counternarratives on Data Sharing in Africa. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency Pp. 329–341. FAccT ’21. New York, NY, USA: Association for Computing Machinery.
• de Montjoye, YA., Gambs, S., Blondel, V. et al. On the privacy-conscientious use of mobile phone data. Sci Data 5, 180286 (2018).

Predicting welfare from mobile phone data

• Decuyper, A., Rutherford, A., Wadhwa, A., Bauer, J.-M., Krings, G., Gutierrez, T., Blondel, V.D., Luengo-Oroz, M.A., 2014. Estimating Food Consumption and Poverty Indices with Mobile Phone Data. arXiv preprint arXiv:1412.2595.
• Eagle, N., Macy, M., Claxton, R., 2010. Network Diversity and Economic Development. Science 328, 1029–1031. And Supplementary Materials
• Frias-Martinez, V., Virseda, J., 2012. On the relationship between socio-economic factors and cell phone usage, in: Proceedings of the Fifth International Conference on Information and Communication Technologies and Development, ICTD ’12. ACM, New York, NY, USA, pp. 76–84. doi:10.1145/2160673.2160684
• Hernandez, M., Hong, L., Frias-Martinez, V., Frias-Martinez, E., 2017. Estimating poverty using cell phone data : evidence from Guatemala (Policy Research Working Paper Series No. 7969). The World Bank.
• Jahani, E., Sundsøy, P., Bjelland, J., Bengtsson, L., Pentland, A. “Sandy”, Montjoye, Y.-A. de, 2017. Improving official statistics in emerging markets using machine learning and mobile phone data. EPJ Data Sci. 6, 3. https://doi.org/10.1140/epjds/s13688-017-0099-3
• Liang, L., Shrestha, R., Ghosh, S., Webb, P., 2020. Using mobile phone data helps estimate community-level food insecurity: Findings from a multi-year panel study in Nepal. PLOS ONE 15, e0241791. https://doi.org/10.1371/journal.pone.0241791
• Njuguna, C., McSharry, P., 2017. Constructing spatiotemporal poverty indices from big data. Journal of Business Research 70, 318–327. doi:10.1016/j.jbusres.2016.08.005
• Schmid, T., Bruckschen, F., Salvati, N., Zbiranski, T., 2017. Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal. J. R. Stat. Soc. A 180, 1163–1190. https://doi.org/10.1111/rssa.12305
• Slavchevska, V., Tyszler, M., Burra, D.D., Seymour, G., Sementsov, D., Van Lierde, A., King, B., 2021. Can call detail records provide insights into women’s empowerment? A case study from Uganda. White paper / Case Study.
• Smith-Clarke, C., Mashhadi, A., Capra, L., 2014. Poverty on the Cheap: Estimating Poverty Maps Using Aggregated Mobile Communication Networks, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’14. ACM, New York, NY, USA, pp. 511–520. doi:10.1145/2556288.2557358
• Toole JL, Lin Y-R, Muehlegger E, Shoag D, Gonzalez MC, Lazer D., 2015. Tracking Employment Shocks using Mobile Phone Data. J. R. Soc. Interface. 2015;12 (107).

Mobility, violence, and natural disasters

• Bengtsson, L., Lu, X., Thorson, A., Garfield, R., von Schreeb, J., 2011. Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 8, e1001083.
• Gething and Tatem (2011). Can Mobile Phone Data Improve Emergency Response to Natural Disasters? PLoS Medicine.
• Hong, L., Lee, M., Mashhadi, A., Frias-Martinez, V., 2018. Towards Understanding Communication Behavior Changes During Floods Using Cell Phone Data, in: Staab, S., Koltsova, O., Ignatov, D.I. (Eds.), Social Informatics, Lecture Notes in Computer Science. Springer International Publishing, pp. 97–107
• Lu, Xin, Linus Bengtsson, and Petter Holme. Predictability of Population Displacement after the 2010 Haiti Earthquake. Proceedings of the National Academy of Sciences 109, no. 29 (July 17, 2012): 11576–81.
• Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Hentenryck, P.V., Fowler, J., Cebrian, M., 2016. Rapid assessment of disaster damage using social media activity. Science Advances 2, e1500779. doi:10.1126/sciadv.1500779
• Kapoor, A., Eagle, N., Horvitz, E., (2010) People, Quakes, and Communications: Inferences from Call Dynamics about a Seismic Event and its Influences on a Population.
• Li, T., Dejby, J., Albert, M., Bengtsson, L., Lefebvre, V., 2019. Detecting individual internal displacements following a sudden-onset disaster using time series analysis of call detail records. arXiv:1908.02377.
• Robinson, B., Power, R., Cameron, M., 2013. A sensitive Twitter earthquake detector, in: Proceedings of the 22nd International Conference on World Wide Web, WWW ’13 Companion. Association for Computing Machinery, New York, NY, USA, pp. 999–1002.
• Wilson, R. et al. (2016). Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLoS Curr 8.
• Smallwood, T. R., Lefebvre, V., & Bengtsson, L. (2022). Mobile phone usage data for disaster response. Communications of the ACM, 65(4), 40-41.
• Kohli, N., Aiken, E., & Blumenstock, J. (2023). Privacy Guarantees for Personal Mobility Data in Humanitarian Response. arXiv preprint arXiv:2306.09471.

Mobility and anomaly detection

• Barnett, I., Khanna, T., Onnela, J.-P., 2016. Social and Spatial Clustering of People at Humanity’s Largest Gathering. PLOS ONE 11, e0156794.
• Young, W., Blumenstock, J.E., Fox, E., McCormick, T., 2014. Detecting and classifying anomalous behavior in spatiotemporal network data, in: The 20th ACM Conference on Knowledge Discovery and Mining (KDD ’14), Workshop on Data Science for Social Good. Presented at the Data Science for Social Good workshop at KDD, New York, NY.
• Wang, W., Kennedy, R., Lazer, D., Ramakrishnan, N., 2016. Growing pains for global monitoring of societal events. Science 353, 1502–1503.
• Dobra, A., Williams, N.E., Eagle, N., 2015. Spatiotemporal detection of unusual human population behavior using mobile phone data. PloS one 10, e0120449.
• Dong, X., Meyer, J., Shmueli, E., Bozkaya, B., Pentland, A., 2018. Methods for quantifying effects of social unrest using credit card transaction data. EPJ Data Sci. 7, 8.
• Gundogdu, D., Incel, O.D., Salah, A.A., Lepri, B., 2016. Countrywide arrhythmia: emergency event detection using mobile phone data. EPJ Data Sci. 5, 25.

Migration

• Deville, P. et al. (2014). Dynamic population mapping using mobile phone data. PNAS 111, 15888–15893. doi:10.1073/pnas.1408439111
• Blumenstock, J.E., 2012. Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from Rwanda. Information Technology for Development 18, 107–125.
• Blumenstock, J., Chi, G., and Tan, X., 2019. Migration and the Value of Social Networks. Working paper.
• Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L., 2008. Understanding individual human mobility patterns. Nature 453, 779-782.
• Hong, L., Wu, J., Frias-Martinez, E., Villarreal, A., Frias-Martinez, V., 2019. Characterization of Internal Migrant Behavior in the Immediate Post-migration Period Using Cell Phone Traces, in: Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, ICTD ’19. ACM, New York, NY, USA, pp. 4:1–4:12.
• Isaacman, S., Frias-Martinez, V., Frias-Martinez, E., 2018. Modeling Human Migration Patterns During Drought Conditions in La Guajira, Colombia, in: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS ’18. ACM, New York, NY, USA, pp. 31:1–31:9.
• Kriendler, G., Miyauchi, Y., 2019. Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records. Working Paper.
• Lenormand, M. et al. (2014). Cross-Checking Different Sources of Mobility Information. PLOS ONE 9, e105184.
• Lu, X. et al. (2016). Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change 38, 1–7.
• Palchykov, V., Mitrović, M., Jo, H.-H., Saramäki, J., Pan, R.K., 2014. Inferring human mobility using communication patterns. Scientific Reports 4, 6174.
• Ranjan, G., Zang, H., Zhang, Z.-L., Bolot, J., 2012. Are Call Detail Records Biased for Sampling Human Mobility? SIGMOBILE Mob. Comput. Commun. Rev. 16, 33–44.
• Simini, F., González, M.C., Maritan, A., Barabási, A.-L., 2012. A universal model for mobility and migration patterns. Nature 484, 96–100.
• Song, C., Qu, Z., Blumm, N., Barabasi, A.-L., 2010. Limits of Predictability in Human Mobility. Science 327, 1018–1021.
• Zhao, Z., Shaw, S.-L., Xu, Y., Lu, F., Chen, J., Yin, L., 2016. Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science 30, 1738–1762.
• Chi, Guanghua, Lin, F., Chi, Guangqing, Blumenstock, J., 2020. A general approach to detecting migration events in digital trace data. PLOS ONE 15, e0239408.

• Althoff, T., Sosič, R., Hicks, J.L., King, A.C., Delp, S.L., Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature 547.
• Breen, Casey F., Masoomali Fatehkia, Jiani Yan, et al. 2025. “Mapping Subnational Gender Gaps in Internet and Mobile Adoption Using Social Media Data.” Proceedings of the National Academy of Sciences 122 (42): e2416624122. https://doi.org/10.1073/pnas.2416624122.
• Fatehkia, M., Tingzon, I., Orden, A., Sy, S., Sekara, V., Garcia-Herranz, M., Weber, I., 2020. Mapping socioeconomic indicators using social media advertising data. EPJ Data Sci. 9, 1–15.

Measuring and mapping wealth and poverty with internet (and related) data

• Lampos, V., Aletras, N., Geyti, J.K., Zou, B., Cox, I.J., 2016. Inferring the Socioeconomic Status of Social Media Users Based on Behaviour and Language, in: Advances in Information Retrieval. Presented at the European Conference on Information Retrieval, Springer, Cham, pp. 689–695. See also associated blog post.
• Chakraborty, S., Venkataraman, A., Jagabathula, S., Subramanian, L., 2016. Predicting Socio-Economic Indicators Using News Events, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. ACM, New York, NY, USA, pp. 1455–1464.
• Dong, L., Chen, S., Cheng, Y., Wu, Z., Li, C., Wu, H., 2016. Measuring Economic Activities of China with Mobile Big Data. arXiv:1607.04451
• Fatehkia, M., Kashyap, R., Weber, I., 2018. Using Facebook ad data to track the global digital gender gap. World Development 107, 189–209.
• Glaeser, E.L., Kim, H., Luca, M., 2017. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity (Working Paper No. 24010). National Bureau of Economic Research.
• Glaeser, E.L., Kominers, S.D., Luca, M., Naik, N., 2015. Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life (Working Paper No. 21778). National Bureau of Economic Research.
• Norbutas, L., Corten, R., 2017. Network structure and economic prosperity in municipalities: A large-scale test of social capital theory using social media data. Social Networks.
• Sheehan, E., Meng, C., Tan, M., Uzkent, B., Jean, N., Lobell, D., Burke, M., Ermon, S., 2019. Predicting Economic Development using Geolocated Wikipedia Articles. arXiv:1905.01627.
• Chi, G., Fang, H., Chatterjee, S., and Blumenstock, J. E. (2022). Microestimates of wealth for all low-and middle-income countries. Proceedings of the National Academy of Sciences, 119(3), e2113658119.

Contagion and spreading information over web networks

• Alatas, V., Chandrasekhar, A.G., Mobius, M., Olken, B.A., Paladines, C., 2019. When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination In Indonesia. National Bureau of Economic Research Working Paper Series.
• Aral, S., Nicolaides, C., 2017. Exercise contagion in a global social network. Nature Communications 8, 14753.
• Blumenstock, J.E., Keleher, N., 2015. The Price is Right? Statistical Evaluation of a Crowd-sourced Market Information System in Liberia, in: Proceedings of the 2015 Annual Symposium on Computing for Development, DEV ’15. ACM, New York, NY, USA, pp. 117–125.
• Raza, A.A., Ul Haq, F., Tariq, Z., Pervaiz, M., Razaq, S., Saif, U., Rosenfeld, R., 2013. Job Opportunities Through Entertainment: Virally Spread Speech-based Services for Low-literate Users, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13. ACM, New York, NY, USA, pp. 2803–2812.

Mapping populations and mobility using web data

• Palotti, J., Adler, N., Morales-Guzman, A., Villaveces, J., Sekara, V., Garcia Herranz, M., … & Weber, I. (2020). Monitoring of the Venezuelan exodus through Facebook’s advertising platform. Plos one, 15(2), e0229175.
• Antenucci, D., Cafarella, M., Levenstein, M., Ré, C., Shapiro, M.D., 2014. Using Social Media to Measure Labor Market Flows (Working Paper No. 20010). National Bureau of Economic Research.
• Patel, N.N., Stevens, F.R., Huang, Z., Gaughan, A.E., Elyazar, I., Tatem, A.J., 2017. Improving Large Area Population Mapping Using Geotweet Densities. Trans. in GIS 21, 317–331.
• S. Spyratos, M. Vespe, F. Natale, I. Weber, E. Zagheni, M. Rango, Quantifying international human mobility patterns using Facebook Network data. PLOS ONE. 14, e0224134 (2019).

Streetview and related imagery

• Fan, Zhuangyuan, Fan Zhang, Becky P. Y. Loo, and Carlo Ratti. 2023. “Urban Visual Intelligence: Uncovering Hidden City Profiles with Street View Images.” Proceedings of the National Academy of Sciences 120 (27): e2220417120. https://doi.org/10.1073/pnas.2220417120.
  Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E., 2015. Social Media Fingerprints of Unemployment. PLOS ONE 10, e0128692. doi:10.1371/journal.pone.0128692.
• Gebru, Timnit, Jonathan Krause, Yilun Wang, et al. 2017. “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods across the United States.” Proceedings of the National Academy of Sciences 114 (50): 13108–13. https://doi.org/10.1073/pnas.1700035114.

Other applications of web and social media data analysis

• Fan, Zhuangyuan, Fan Zhang, Becky P. Y. Loo, and Carlo Ratti. 2023. “Urban Visual Intelligence: Uncovering Hidden City Profiles with Street View Images.” Proceedings of the National Academy of Sciences 120 (27): e2220417120. https://doi.org/10.1073/pnas.2220417120.
  Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E., 2015. Social Media Fingerprints of Unemployment. PLOS ONE 10, e0128692. doi:10.1371/journal.pone.0128692.
• Gebru, Timnit, Jonathan Krause, Yilun Wang, et al. 2017. “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods across the United States.” Proceedings of the National Academy of Sciences 114 (50): 13108–13. https://doi.org/10.1073/pnas.1700035114.

Issues with using platform data for social science research

• Bruns et al. (2023), Nature Human Behaviour: Platform-controlled social media APIs threaten open science.
• Sen et al. (2021), Public Opinion Quarterly: Total Error Framework for Digital Traces of Human Behavior on Online Platforms (TED-On)
• Giorgi, Salvatore, Veronica E. Lynn, Keshav Gupta, et al. 2022. “Correcting Sociodemographic Selection Biases for Population Prediction from Social Media.” Proceedings of the International AAAI Conference on Web and Social Media 16 (May): 228–40. https://doi.org/10.1609/icwsm.v16i1.19287.
• Cesare, Nina, Hedwig Lee, Tyler McCormick, Emma Spiro, and Emilio Zagheni. 2018. “Promises and Pitfalls of Using Digital Traces for Demographic Research.” Demography 55 (5): 1979–99. https://doi.org/10.1007/s13524-018-0715-2.

• Kohli, Nitin, and Joshua Blumenstock. 2024. “Enabling Humanitarian Applications with Targeted Differential Privacy.” arXiv:2408.13424. Preprint, arXiv, August 24. https://doi.org/10.48550/arXiv.2408.13424.
• Seltzer, W. (2006). The Dark Side of Numbers: Updated. In Bevölkerungsforschung und Politik in Deutschland im 20. Jahrhundert. Rainer Mackensen, ed. Pp. 119–136. Wiesbaden: VS Verlag für Sozialwissenschaften.
• Hosein, G., Nyst, C., 2013. Aiding Surveillance: An Exploration of How Development and Humanitarian Aid Initiatives are Enabling Surveillance in Developing Countries. https://doi.org/10.2139/ssrn.2326229
• Blumenstock, J. and Kohli, N. (2023). Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda. arXiv preprint.
• [Particularly introduction, conclusion, and chapter 7] Scott, J. (1998). Seeing like a state.

• Kahn, Zoe, Carelle Meyebinesso Farida, Emily Aiken, Nitin Kohli, and Joshua E Blumenstock. 2024. “Expanding Perspectives on Data Privacy: Insights from Rural Togo.” CSCW (New York, NY, USA). https://dl.acm.org/doi/10.1145/3710968
• de Montjoye, Y., Hidalgo, C., Verleysen, M., and Blondel, V. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports.
• Ahmed, S. I., Haque, R., Chen, J., and Dell, N. (2017). Digital Privacy Challenges with Shared Mobile Phone Use in Bangladesh. Proceedings of the ACM on Human-Computer Interaction 1(CSCW): 1–20.
• Kohli, Nitin, Emily Aiken, and Joshua E. Blumenstock. 2024. “Privacy Guarantees for Personal Mobility Data in Humanitarian Response.” Scientific Reports 14 (1): 28565. https://doi.org/10.1038/s41598-024-79561-2.
• Gebru, T. et al. (2021). Datasheets for datasets. Commun. ACM 64, 12 (December 2021), 86–92.Seltzer, W. (2006). The Dark Side of Numbers: Updated. In Bevölkerungsforschung und Politik in Deutschland im 20. Jahrhundert. Rainer Mackensen, ed. Pp. 119–136. Wiesbaden: VS Verlag für Sozialwissenschaften.

• Belmont Report
• Blumenstock, J. (2018). Don’t forget people in the use of big data for development. Nature, 561(7722), 170–172.
• Watch: Democratizing AI: Principles for meaningful public participation.
• Human rights for development
• Sambasivan, N. et al. (2021). Re-imagining Algorithmic Fairness in India and Beyond. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 315–328.
• Green, B. (2019). ‘Good’ Isn’t good enough. NeurIPS workshop on AI for social good.
• Elsayed-Ali, S. (2019). In Response to “Good” Isn’t Enough.
• Png, M. (2022). At the Tensions of South and North: Critical Roles of Global South Stakeholders in AI Governance. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22).

• CARE Principles
• FAIR Principles
• Heeks, Richard, and Jaco Renken. 2018. “Data Justice for Development: What Would It Mean?” Information Development 34 (1): 90–102. https://doi.org/10.1177/0266666916678282.
• Taylor, Linnet. 2017. “What Is Data Justice? The Case for Connecting Digital Rights and Freedoms Globally.” Big Data & Society 4 (2): 2053951717736335. https://doi.org/10.1177/2053951717736335.
• Birhane, A. (2020). Algorithmic Colonization of Africa. SCRIPT-Ed 17(2): 389–409. Dogan, A. and Wood, D. (2023). “Do you collect data to give to the university or do you do the work to benefit people?”: Indigenous Data Sovereignty in Environmental Contexts. In Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’23).
• [Introduction, chapter 2, and pages 87-96 (68 pages total)] Friedman, B. and Hendry, D. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. The MIT Press.
• [Read sections 1; 2.3; Section 3.2; Section 4] Schmude, T., Koesten, L., Möller, T., and Tschiatschek, S. (2023). On the Impact of Explanations on Understanding of Algorithmic Decision-Making. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23). Association for Computing Machinery, New York, NY, USA, 959–970.
• Selbst, A D. and Barocas, S, (2018). The Intuitive Appeal of Explainable Machines. 87 Fordham Law Review 1085.
• Mitchell, M. et al. (2019.) Model Cards for Model Reporting. In Proceedings of the Conference on Fairness,
• Weitzberg, K. et al. (2021). Between Surveillance and Recognition: Rethinking Digital Identity in Aid. Big Data & Society 8(1).
• Vines, J. et al. (2013). Configuring Participation: On How We Involve People in Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Pp. 429–438. CHI ’13. New York, NY, USA: Association for Computing Machinery.
• Gilman, M. (2023). Democratizing AI: Principles for Meaningful Public Participation. Data & Society Policy Brief. 
• Cooper, F. (2010). Writing the History of Development. Journal of Modern European History 8(1). Verlag C.H.Beck: 5–23.
• Okolo, C., Dell, N., and Vashistha, A. (2022). Making AI Explainable in the Global South: A Systematic Review. In Proceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’22).

• Björkegren, D, Blumenstock, JE, Folajimi-Senjobi, O, Mauro, J, and Nair, S. (2025). Welfare Impacts of Digital Credit: A Randomized Evaluation in Nigeria, Economic Development and Cultural Change, 74:1, 117-140
• Björkegren, D, Blumenstock, JE, and Knight, S (2026). “Manipulation-Robust Prediction.” [pdf].
• Banerjee, A., Karlan, D., Zinman, J. (2015). Six Randomized Evaluations of Microcredit: Introduction and Further Steps. American Economic Journal: Applied Economics 7, 1–21.
• Francis, E., Blumenstock, J.E., Robinson, J. (2017). Digital Credit: A Snapshot of the Current Landscape and Open Research Questions. BREAD Working Paper No. 516.
• Totolo, E. (2018). Kenya’s Digital Credit Revolution Five Years On. CGAP.
• Suri et al. (2022). Mobile Money. Vox Dev.
• Rizzi, A., Kessler, A., and Menajovsky, J. (2021). The Stories Algorithms Tell: Bias and Financial Inclusion at the Data Margins.

Digital credit scoring

• Pedro, J.S., Proserpio, D., Oliver, N., 2015. MobiScore: Towards Universal Credit Scoring from Mobile Phone Data, in: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (Eds.), User Modeling, Adaptation and Personalization, Lecture Notes in Computer Science. Springer International Publishing, pp. 195–207.
• Shema, A., 2019. Effective Credit Scoring Using Limited Mobile Phone Data, in: Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, ICTD ’19. ACM, New York, NY, USA, pp. 7:1–7:11.
• Dean, M. (2018). Can big data shape financial services in East Africa? Partnership for finance in a digital Africa.
• Nordin, K., Hunter, R., Adaii, A., 2017. Branch case study: Exploring the potential of alternative data for creating new markets. Insight2Impact Case Study.

Impacts and diffusion of digital credit

• Robinson, J., Park, D. S., & Blumenstock, J. E. (2023). The impact of digital credit in developing economies: A review of recent evidence. KDI School of Pub Policy & Management Paper.
• Björkegren, D. et al. (2022). Instant loans can lift subjective well-being: A randomized evaluation of digital credit in Nigeria. arXiv preprint arXiv:2202.13540.
• Banerjee, A., Duflo, E., Glennerster, R., Kinnan, C., 2015. The miracle of microfinance? Evidence from a randomized evaluation. American Economic Journal: Applied Economics 7, 22–53.
• Banerjee, Abhijit, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson. The Diffusion of Microfinance. Science 341, no. 6144 (July 26, 2013): 1236498.
• Khan, M. (2018). Delving into human consciousness: using psychometric assessments in financial services. Partership for finance in a digital Africa.
• Bharadwaj, P., Jack, W., Suri, T., 2019. Fintech and Household Resilience to Shocks: Evidence from Digital Loans in Kenya. National Bureau of Economic Research Working Paper Series.
• Gubbins, P. & Totolo, E. (2018) Digital credit in Kenya: Evidence from demand-side surveys. Nairobi, Kenya: FSD Kenya.
• Khwaja, A.I., Mian, A., 2005. Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market. Q J Econ 120, 1371–1411.
• Meager, R., 2019. Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments. American Economic Journal: Applied Economics 11, 57–91.
• Paruthi, G., Frias-Martinez, E., Frias-Martinez, V., 2016. Peer-to-peer microlending platforms: Characterization of online traits, in: 2016 IEEE International Conference on Big Data (Big Data). Presented at the 2016 IEEE International Conference on Big Data (Big Data), pp. 2180–2189.

Mobile money

• Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288-1292.
• Blumenstock, J.E., Eagle, N., Fafchamps, M., 2016. Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters. Journal of Development Economics 120, 157–181.
• GSMA (2020). State of the Industry Report on Mobile Money.
• [Introduction only] Riley, E. (2022). “Resisting Social Pressure in the Household Using Mobile Money: Experimental Evidence on Microenterprise Investment in Uganda,” CSAE Working Paper Series 2022-04.
• Khan, M.R., Blumenstock, J.E., 2016. Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries. Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD `16).
• Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183-223.
• Suri, T. (2017). Mobile Money. Annual Review of Economics 9(1), 497-520.

Debit cards and ATMs

• Bachas, P., Gertler, P., Higgins, S., Seira, E., 2018. Digital financial services go a long way: Transaction costs and financial inclusion, in: AEA Papers and Proceedings. pp. 444–48.
• Clemente, R.D., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., González, M.C., 2018. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications 9, 3330.
• Martinez, E.A., Rubio, M.H., Martinez, R.M., Arias, J.M., Patane, D., Zerbe, A., Kirkpatrick, R., Luengo-Oroz, M., Zerbe, A., 2016. Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data. arXiv:1609.09340 [cs].
• Carpio-Pinedo, J. et al. (2022). Towards a new urban geography of expenditure: Using bank card transactions data to analyze multi-sector spatiotemporal distributions. Cities 131.

• Aiken, Emily, Anik Ashraf, Joshua Blumenstock, Raymond Guiteras, and Ahmed Mushfiq Mobarak. 2025. “Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge?” Working Paper No. 33919. Working Paper Series. National Bureau of Economic Research, June. https://doi.org/10.3386/w33919.
• Smythe, I. S., & Blumenstock, J. E. (2022). Geographic microtargeting of social assistance with high-resolution poverty maps. Proceedings of the National Academy of Sciences, 119(32), e2120025119.
• Sahoo, Roshni, Joshua Blumenstock, Paul Niehaus, Leo Selker, and Stefan Wager. 2025. “What Would It Cost to End Extreme Poverty?” Working Paper No. 34583. Working Paper Series. National Bureau of Economic Research, December. https://doi.org/10.3386/w34583.
  Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., & Tobias, J. (2012). Targeting the poor: evidence from a field experiment in Indonesia. American Economic Review, 102(4), 1206-1240.
• Hanna, R., & Olken, B. A. (2018). Universal basic incomes versus targeted transfers: Anti-poverty programs in developing countries. Journal of Economic Perspectives, 32(4), 201-226.
• Sen, A. (1992). The political economy of targeting. Washington, DC: World Bank.

• Roelen, Keetie. 2020. Receiving Social Assistance in Low- and Middle-Income Countries: Negating Shame or Producing Stigma? Journal of Social Policy 49(4): 705–723.
• Automated Neglect: How The World Bank’s Push to Allocate Cash Assistance Using Algorithms Threatens Rights. June 13, 2023. Human Rights Watch.
• Noriega-Campero, A. et al. (2020). Algorithmic targeting of social policies: fairness, accuracy, and distributed governance. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20).
• Premand, P., and Schnitzer, P. (2021). Efficiency, legitimacy, and impacts of targeting methods: Evidence from an experiment in Niger. The World Bank Economic Review, 35(4), 892-920.
• Wein, Tom, Heather Lanthorn, and Torben Fischer. 2023. First Steps toward Building Respectful Development: Three Experiments on Dignity in Aid in Kenya and the United States. World Development Perspectives 29: 100485.
• Aiken, E. and Ohlenburg, T. (2023). Novel digital data sources for social protection: Opportunities and challenges. GIZ.
• Della Guardia, Anne, Milli Lake, and Pascale Schnitzer. 2022. Selective Inclusion in Cash Transfer Programs: Unintended Consequences for Social Cohesion. World Development 157: 105922. 
• Abelson, B., Varshney, K. R., & Sun, J. (2014, August). Targeting direct cash transfers to the extremely poor. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1563-1572).
• Lindert, K., Karippacheril, T. G., Caillava, I. R., & Chávez, K. N. (Eds.). (2020). Sourcebook on the foundations of social protection delivery systems. World Bank Publications.
• Mukherjee, A. N., Bermeo Rojas, L. X., Okamura, Y., Muhindo, J. V., & Bance, P. G. (2023). Digital-first Approach to Emergency Cash Transfers: STEP-KIN in the DemocraticRepublic of Congo (No. 181798). The World Bank.

• Milusheva, S. (2020). Managing the spread of disease with mobile phone data. Journal of Development Economics 147. 
• Buckee, C. et al. (2020). Aggregated mobility data could help fight COVID-19. Science 368, 145–146.
• Wesolowski, A., Buckee, C.O., Engø-Monsen, K., Metcalf, C.J.E. (2016). Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. J Infect Dis. 214, S414–S420.
• [Read sections 1, 2, 3, 5, and 6] Amugongo, L., Bidwell, N., and Corrigan, C. (2023). Invigorating Ubuntu ethics in AI for healthcare: enabling equitable care. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT).

Public health: Modeling disease spread with mobility inferred from web and phone data

• Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L., 2009. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014. doi:10.1038/nature07634
• Choi, H., Varian, H., 2012. Predicting the Present with Google Trends. Economic Record 88, 2–9. doi:10.1111/j.1475-4932.2012.00809.x
• Olson, D.R., Konty, K.J., Paladini, M., Viboud, C., Simonsen, L., 2013. Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales. PLOS Computational Biology 9, e1003256. doi:10.1371/journal.pcbi.1003256
• Milusheva, S., 2020. Using Mobile Phone Data to Reduce Spread of Disease  Working Paper
• Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Delataille, S., Nadai, M.D., Letouzé, E., Salah, A.A., Benjamins, R., Cattuto, C., Colizza, V., Cordes, N. de, Fraiberger, S.P., Koebe, T., Lehmann, S., Murillo, J., Pentland, A., Pham, P.N., Pivetta, F., Saramäki, J., Scarpino, S.V., Tizzoni, M., Verhulst, S., Vinck, P., 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances
• Wesolowski, A., Metcalf, C.J.E., Eagle, N., Kombich, J., Grenfell, B.T., Bjørnstad, O.N., Lessler, J., Tatem, A.J., Buckee, C.O., 2015. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. PNAS 112, 11114–11119. doi:10.1073/pnas.1423542112
• Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J.J., Vespignani, A., 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. PNAS 106, 21484–21489. doi:10.1073/pnas.0906910106
• Finger, F., Genolet, T., Mari, L., Magny, G.C. de, Manga, N.M., Rinaldo, A., Bertuzzo, E., 2016. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. PNAS 113, 6421–6426. doi:10.1073/pnas.1522305113
• Salathé, Marcel, Maria Kazandjieva, Jung Woo Lee, Philip Levis, Marcus W. Feldman, and James H. Jones. A High-Resolution Human Contact Network for Infectious Disease Transmission. Proceedings of the National Academy of Sciences 107, no. 51 (December 21, 2010): 22020–25. doi:10.1073/pnas.1009094108.
• Signorini, A., Segre, A.M., Polgreen, P.M., 2011. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLOS ONE 6, e19467. https://doi.org/10.1371/journal.pone.0019467
• Tatem, A.J., Huang, Z., Narib, C., Kumar, U., Kandula, D., Pindolia, D.K., Smith, D.L., Cohen, J.M., Graupe, B., Uusiku, P., Lourenço, C., 2014. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria Journal 13, 52. doi:10.1186/1475-2875-13-52
• Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, R.W., Buckee, C.O., 2012. Quantifying the Impact of Human Mobility on Malaria. Science 338, 267–270. doi:10.1126/science.1223467
• Wesolowski, A., Qureshi, T., Boni, M.F., Sundsøy, P.R., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K., Buckee, C.O., 2015. Impact of human mobility on the emergence of dengue epidemics in Pakistan. PNAS 112, 11887–11892. doi:10.1073/pnas.1504964112
• Wesolowski, A., Erbach-Schoenberg, E. zu, Tatem, A.J., Lourenço, C., Viboud, C., Charu, V., Eagle, N., Engø-Monsen, K., Qureshi, T., Buckee, C.O., Metcalf, C.J.E., 2017. Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics. Nature Communications 8, 2069.

Public health: Social distancing and movement restrictions

• Jeffrey, B., Walters, C.E., Ainslie, K.E., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N.F., 2020. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research 5.
• Kraemer, M.U., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D.M., Du Plessis, L., Faria, N.R., Li, R., Hanage, W.P., 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497.
• Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K.E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N.F., Cattarino, L., Cooper, L.V., 2021. Reduction in mobility and COVID-19 transmission. Nature communications 12, 1–9.
• Warren, M.S., Skillman, S.W., 2020. Mobility changes in response to COVID-19. arXiv preprint arXiv:2003.14228.
• Wellenius, G.A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T.C., Hennessy, J., Williams, B., Gadepalli, K., Boulange, A., Pearce, A., 2020. Impacts of state-level policies on social distancing in the United States using aggregated mobility data during the COVID-19 pandemic. arXiv preprint arXiv:2004.10172.
• Peak, C.M., Wesolowski, A., zu Erbach-Schoenberg, E., Tatem, A.J., Wetter, E., Lu, X., Power, D., Weidman-Grunewald, E., Ramos, S., Moritz, S., Buckee, C.O., Bengtsson, L. Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data. Int J Epidemiol.

Public health: Other applications and data sources

• Bharti, N., Tatem, A.J., Ferrari, M.J., Grais, R.F., Djibo, A., Grenfell, B.T., 2011. Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery. Science 334, 1424–1427.
• Ford, T.E., Colwell, R.R., Rose, J.B., Morse, S.S., Rogers, D.J., Yates, T.L., 2009. Using Satellite Images of Environmental Changes to Predict Infectious Disease Outbreaks. Emerg Infect Dis 15, 1341–1346.
• Generous, N., Fairchild, G., Deshpande, A., Del Valle, S.Y., Priedhorsky, R., 2014. Global Disease Monitoring and Forecasting with Wikipedia. PLoS Comput Biol 10, e1003892. doi:10.1371/journal.pcbi.1003892
• De Choudhury, M., Gamon, M., Counts, S., Horvitz, E., 2013. Predicting Depression via Social Media, in: ICWSM. p. 2.
• Lampos, V., Yom-Tov, E., Pebody, R., Cox, I.J., 2015. Assessing the impact of a health intervention via user-generated Internet content. Data Min Knowl Disc 29, 1434–1457. 
• Abebe, R., Hill, S., Vaughan, J.W., Small, P.M., Schwartz, H.A., 2018. Using Search Queries to Understand Health Information Needs in Africa. arXiv:1806.05740
• Heft-Neal, S., Burney, J., Bendavid, E., Voss, K.K., Burke, M., 2020. Dust pollution from the Sahara and African infant mortality. Nature Sustainability 3, 863–871.

• Tai, X., Mehra, S., and Blumenstock, J. (2021). Mobile phone data reveal the effects of violence on internal displacement in Afghanistan. Nature Human Behavior 6, 624-634.
• Tai, Xiao Hui, Suraj R. Nair, Shikhar Mehra, and Joshua E. Blumenstock. 2025. “Satellite and Mobile Phone Data Reveal How Violence Affects Seasonal Migration in Afghanistan.” arXiv:2507.00279. Preprint, arXiv, June 30. https://doi.org/10.48550/arXiv.2507.00279.
• Simko, L., Ramulu, H., Kohno, T., and Acar, Y. (2023). The Use and Non-Use of Technology During Hurricanes. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 366 (October 2023).

• Deininger, Klaus, Daniel Ayalew Ali, Nataliia Kussul, Andrii Shelestov, Guido Lemoine, and Hanna Yailimova. 2023. “Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security.” Food Policy 115 (February): 102418. https://doi.org/10.1016/j.foodpol.2023.102418.
• Mueller, Hannes, Christopher Rauh, and Ben Seimon. 2024. “Introducing a Global Dataset on Conflict Forecasts and News Topics.” Data & Policy 6 (January): e17. https://doi.org/10.1017/dap.2024.10.
  

• Sharma, K. et al. (2020). Conservation and people: Towards an ethical code of conduct for the use of camera traps in wildlife research. Ecological Solutions and Evidence 1.
• Ma, Yuchi, Shuo Chen, Stefano Ermon, and David B. Lobell. 2024. “Transfer Learning in Environmental Remote Sensing.” Remote Sensing of Environment 301 (February): 113924. https://doi.org/10.1016/j.rse.2023.113924.
• Burke, M., Lobell, D.B., 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences 201616919.
• Guirado, E. et al. (2019). Whale counting in satellite and aerial images with deep learning. Nature Scientific Reports.

• Constenla-Villoslada, Susana, Yanyan Liu, Linden McBride, Clinton Ouma, Nelson Mutanda, and Christopher B. Barrett. 2025. “High-Frequency Monitoring Enables Machine Learning–Based Forecasting of Acute Child Malnutrition for Early Warning.” Proceedings of the National Academy of Sciences 122 (23): e2416161122. https://doi.org/10.1073/pnas.2416161122.
• Ma, Yuchi, Shuo Chen, Stefano Ermon, and David B. Lobell. 2024. “Transfer Learning in Environmental Remote Sensing.” Remote Sensing of Environment 301 (February): 113924. https://doi.org/10.1016/j.rse.2023.113924.
• Nolte, C. (2020). High-resolution land value maps reveal underestimation of conservation costs in the United States. Proceedings of the National Academy of Sciences 117, 29577–29583.
• Shah, Nair, Boehnel, and Blumenstock (2023). Sand Mining Watch: Leveraging Earth Observation Foundation Models to Inform Sustainable Development. Neurips Workshop on Tackling Climate Change with Machine Learning.
• Kozińska, P, and J Górniak-Zimroz. 2021. “A Review of Methods in the Field of Detecting Illegal Open-Pit Mining Activities.” IOP Conference Series: Earth and Environmental Science 942 (1): 012027. https://doi.org/10.1088/1755-1315/942/1/012027.
• Fang, F., Stone, P., and Tambe, M. (2015). When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing. In IJCAI (pp. 2589-2595).
• Burke, M., Lobell, D.B., 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences 201616919.
• Tuia, D. et al. (2022). Perspectives in machine learning for wildlife conservation. Nature communications, 13(1), 792.
• Kerner, H. et al. (2020). Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. arXiv preprint.
• Axbard, Sebastian. 2016. “Income Opportunities and Sea Piracy in Indonesia: Evidence from Satellite Data.” American Economic Journal: Applied Economics 8 (2): 154–94. https://doi.org/10.1257/app.20140404.
• Xu, L. et al. (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE International Conference on Data Engineering.
• Guo, R. et al. (2020). Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery. ML4D.
• Bondi, E. et al. (2018). SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time. Proceedings of the 30th Annual Conference on Innovative Applications of Artificial Intelligence (Emerging Applications Track).
• Guirado, E. et al. (2019). Whale counting in satellite and aerial images with deep learning. Nature Scientific Reports.

• Rolf, E. et al. (2020). Balancing competing objectives with noisy data: Score-based classifiers for welfare-aware machine learning. Proceedings of the 37th International Conference on Machine Learning.
• Rambachan, Ashesh, Rahul Singh, and Davide Viviano. 2025. “Program Evaluation with Remotely Sensed Outcomes.” arXiv:2411.10959. Preprint, arXiv, October 27. https://doi.org/10.48550/arXiv.2411.10959.
• Ratlege et al. (2022). Using machine learning to assess the livelihood impact of electricity access. Nature 611.
• Huang et al. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. NBER Working Paper.
• Aiken, Emily, Suzanne Bellue, Joshua E. Blumenstock, Dean Karlan, and Christopher Udry. 2025. “Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo.” Journal of Development Economics 175 (June): 103477. https://doi.org/10.1016/j.jdeveco.2025.103477.
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