• Ager, A., Burnham, G., Checchi, F., Gayer, M., Grais, R. F., Henkens, M., … Spiegel, P. (2014). Strengthening the evidence base for health programming in humanitarian crises. Science, 345(6202), 1290–1292. https://doi.org/10.1126/science.1254164
  • Arai, A, Knippenberg, E., Meyer, M., and Witayangkurn, A. (2021). The Hidden Potential of Call Detail Records in The Gambia. Data & Policy, 3(e9).
  • Arai, A., Witayangkurn, A., Kanasugi, H., Fan, Z., Cumbane, S., & Shibasaki, R. (2020). Building a data ecosystem for using telecom data to inform the COVID-19 response efforts. Data for Policy, 15-17 September 2020. https://doi.org/10.5281/zenodo.3992420
  • Barrios, S., Bertinelli, L., & Strobl, E. (2011). Climatic Change and Rural-Urban Migration: The Case of Sub-Saharan Africa. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.925652
  • 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 Medicine, 8(8), 1–9. https://doi.org/10.1371/journal.pmed.1001083
  • Brown, V., Coulombier, D., Belanger, F., Jacquier, G., Balandine, S., & Legros, D. (2001). Rapid assessment of population size by area sampling in disaster situations. Disasters, 25(2), 164–171. https://doi.org/10.1111/1467-7717.00168
  • Buckee, C. O., & Engø-Monsen, K. (2016). Mobile phone data for public health: towards data-sharing solutions that protect individual privacy and national security. Retrieved from https://arxiv.org/abs/1606.00864
  • Couper, M. P. (2013). Is the sky falling? new technology, changing media, and the future of surveys. Survey Research Methods, 7(3), 145–156. https://doi.org/10.18148/srm/2013.v7i3.5751
  • Crawford, K. (2013). The Hidden Biases in Big Data. Harvard Business Review. Retrieved from http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html
  • CRED. (n.d.). Classification | EM-DAT. Retrieved October 6, 2020, from https://www.emdat.be/classification
  • CRED. (2015). Estimating populations affected by disasters: a review of methodological issues and research gaps. https://doi.org/10.1016/S0140-6736(11)60319-X
  • Dalziel, B. D., Pourbohloul, B., & Ellner, S. P. (2013). Human mobility patterns predict divergent epidemic dynamics among cities. Proceedings of the Royal Society B: Biological Sciences, 280(1766), 20130763.
  • Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., … Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences of the United States of America, 111(45), 15888–15893. https://doi.org/10.1073/pnas.1408439111
  • Evidence Aid Priority Setting Group. (2013). Prioritization of Themes and Research Questions for Health Outcomes in Natural Disasters, Humanitarian Crises or Other Major Healthcare Emergencies. PLoS Currents, (OCT). https://doi.org/10.1371/currents.dis.c9c4f4db9887633409182d2864b20c31
  • Flowminder. (n.d.). Flowminder COVID-19 Resources - Mobility indicators. Retrieved September 25, 2020, from https://covid19.flowminder.org/mobility-indicators
  • Fussell, E., Curtis, K. J., & DeWaard, J. (2014). Recovery migration to the City of New Orleans after Hurricane Katrina: A migration systems approach. Population and Environment, 35(3), 305–322. https://doi.org/10.1007/s11111-014-0204-5
  • González, M. C., Hidalgo, C. A., & Barabási, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958
  • Google. (n.d.). COVID-19 Community Mobility Reports. Retrieved September 3, 2020, from https://www.google.com/covid19/mobility/
  • Gray, C. L., & Mueller, V. (2012). Natural disasters and population mobility in Bangladesh. Proceedings of the National Academy of Sciences of the United States of America, 109(16), 6000–6005. https://doi.org/10.1073/pnas.1115944109
  • GSMA. (2018). Helping end Tuberculosis in India by 2025. Retrieved from https://aiforimpacttoolkit.gsma.com/resources/Big-Data-for-Social-Good_Airtel-INDIA_TB_Case_Study.pdf
  • GSMA. (2019). Utilising real-time mobile analytics to inform emergency disaster response in Turkey: Turkey and Natural Disasters. Retrieved from https://aiforimpacttoolkit.gsma.com/resources/Turkey-Turkcell-Emergency-Response-Case-Study.pdf
  • Hori, M., Schafer, M. J., & Bowman, D. J. (2009). Displacement dynamics in Southern Louisiana after Hurricanes Katrina and Rita. Population Research and Policy Review, 28(1), 45–65. https://doi.org/10.1007/s11113-008-9118-1
  • ITU. (n.d.-a). Big Data for development: preventing the spread of epidemics. Retrieved October 15, 2018, from https://www.itu.int/en/ITU-D/Emergency-Telecommunications/Pages/BigData/default.aspx
  • ITU. (n.d.-b). ITU ICT-Eye: ICT DATA PORTAL. Retrieved October 13, 2020, from https://www.itu.int/net4/ITU-D/icteye/#/topics/1002
  • Jansen, Ronald, Karoly Kovacs, Siim Esko, Erki Saluveer, Kaja Sõstra, Linus Bengtsson, Tracey Li, Ayumi Arai, Esperanza Magpantay. "Guiding Principles to Maintain Public Trust in the Use of Mobile Operator Data for Policy Purposes." Data & Policy 3 (2021): e24. doi:10.1017/dap.2021.21. https://doi.org/10.1017/dap.2021.21
  • Kishore, N., Kiang, M. V, Engø-Monsen, K., Vembar, N., Schroeder, A., Balsari, S., & Buckee, C. O. (2020). Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis. The Lancet Digital Health, 7500(20). https://doi.org/10.1016/s2589-7500(20)30193-x
  • Li, Tracey, Rachel Bowers, Omar Seidu, Gloria Akoto-Bamfo, David Bessah, Victor Owusu, and Laurent Smeets. "Analysis of Call Detail Records to Inform the COVID-19 Response in Ghana—Opportunities and Challenges." Data & Policy 3 (2021): e11. doi:10.1017/dap.2021.5.
  • Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE, 9(1). https://doi.org/10.1371/journal.pone.0086026
  • Loebach, P., & Korinek, K. (2019). Disaster vulnerability, displacement, and infectious disease: Nicaragua and Hurricane Mitch. Population and Environment, 40(4), 434-455.
  • Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, M., Wetter, E., Iqbal, A., … Bengtsson, L. (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(May), 1–7. https://doi.org/10.1016/j.gloenvcha.2016.02.002
  • Maas, P., Gros, A., McGorman, L., Alex Dow, P., Iyer, S., Park, W., & Nayak, C. (2019). Facebook disaster maps: Aggregate insights for crisis response & recovery. Proceedings of the International ISCRAM Conference, (May 2019), 836–847.
  • Miliband, D., & Gurumurthy, R. (2015). Improving Humanitarian Aid. Foreign Affairs, 94(4), 118–129. Retrieved from https://www.jstor.org/stable/24483823
  • Myers, C. A., Slack, T., & Singelmann, J. (2008). Social vulnerability and migration in the wake of disaster: The case of Hurricanes Katrina and Rita. Population and Environment, 29(6), 271–291. https://doi.org/10.1007/s11111-008-0072-y
  • Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. Proceedings - IEEE Symposium on Security and Privacy, 111–125. https://doi.org/10.1109/SP.2008.33
  • Plyer, A., Bonaguro, J., & Hodges, K. (2010). Using administrative data to estimate population displacement and resettlement following a catastrophic U.S. disaster. Population and Environment, 31(1–3), 150–175. https://doi.org/10.1007/s11111-009-0091-3
  • Pulse Lab Jakarta. (n.d.). Understanding Population Movement After the 2018 Central Sulawesi Natural Disasters. Retrieved October 28, 2020, from https://medium.com/pulse-lab-jakarta/understanding-population-movement-after-the-2018-central-sulawesi-natural-disasters-70ab95b7741b
  • Relief Web. (2008). Glossary of Humanitarian Terms. Retrieved from www.reliefweb.int/glossaries
  • Simini, F., González, M. C., Maritan, A., & Barabási, A. L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96–100. https://doi.org/10.1038/nature10856
  • Smith, S. K., & Mccarty, C. (1996). Demographic effects of natural disasters: A case study of Hurricane Andrew. Demography, 33(2), 265–275. https://doi.org/10.2307/2061876
  • Song, C., Qu, Z., Blumm, N., & Barabási, A. L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021. https://doi.org/10.1126/science.1177170
  • Statistics New Zealand. (2012). Using cellphone data to measure population movements Experimental analysis following the 22 February 2011 Christchurch earthquake.
  • Sweeney, L. (2000). Uniqueness of Simple Demographics in the U.S. Population. Retrieved from https://dataprivacylab.org/projects/identifiability/paper1.pdf
  • Tam, S. M., & Clarke, F. (2015). Big data, official statistics and some initiatives by the Australian Bureau of statistics. International Statistical Review, 83(3), 436–448. https://doi.org/10.1111/insr.12105
  • UN Global Pulse. (n.d.). Strengthening Humanitarian Action with Insights from Mobile Network Data. Retrieved October 9, 2020, from https://www.unglobalpulse.org/2019/01/strengthening-humanitarian-action-with-insights-from-mobile-network-data/
  • UN Global Pulse. (2014). Using mobile phone activity for disaster management during floods. Retrieved from https://www.unglobalpulse.org/document/using-mobile-phone-activity-for-disaster-management-during-floods/
  • United Nations. (2015a). Paris Agreement.
  • United Nations. (2015b). Sendai Framework for Disaster Risk Reduction 2015 - 2030.
  • United Nations. (2015c). Transforming our world: the 2030 Agenda for Sustainable Development. Retrieved from https://sustainabledevelopment.un.org/post2015/transformingourworld
  • United Nations. (2019). Recommendations on the Role of Official Statistics in Measuring Hazardous Events and Disasters.
  • United Nations. (2020). The Sustainable Development Goals Report 2020.
  • Van den Homberg, M., Monné, R., & Spruit, M. (2018). Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers and Geosciences, 120(May), 60–72. https://doi.org/10.1016/j.cageo.2018.06.002
  • Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012). Heterogeneous mobile phone ownership and usage patterns in Kenya. PloS One. https://doi.org/10.1371/journal.pone.0035319
  • Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W., & Buckee, C. O. (2013). The impact of biases in mobile phone ownership on estimates of human mobility. Journal of the Royal Society Interface, 10(81). https://doi.org/10.1098/rsif.2012.0986
  • 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(6104), 267–270. https://doi.org/10.1126/science.1223467
  • Wesolowski, A., Metcalf, C. J. E., Eagle, N., Kombich, J., Grenfell, B. T., Bjørnstad, O. N., … Buckee, C. O. (2015). Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Proceedings of the National Academy of Sciences of the United States of America, 112(35), 11114–11119. https://doi.org/10.1073/pnas.1423542112
  • Wesolowski, A., Qureshi, T., Boni, M. F., Sundsøy, P. R., Johansson, M. A., Rasheed, S. B., … Singer, B. H. (2015). Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences of the United States of America, 112(38), 11887–11892. https://doi.org/10.1073/pnas.1504964112
  • Wesolowski, A., Stresman, G., Eagle, N., Stevenson, J., Owaga, C., Marube, E., … Buckee, C. O. (2014). Quantifying travel behavior for infectious disease research: A comparison of data from surveys and mobile phones. Scientific Reports, 4. https://doi.org/10.1038/srep05678
  • Wilson, R., Erbach-Schoenberg, E. Z., Albert, M., Power, D., Tudge, S., Gonzalez, M., … Bengtsson, L. (2016). Rapid and near real-time assessments of population displacement using mobile phone data following disasters: The 2015 Nepal earthquake. PLoS Currents, 8(Disasters). https://doi.org/10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c
  • World Bank. (2020, forthcoming). The Hidden Potential of CDR Data in The Gambia. World Bank Policy Working Paper Series, Washington D.C.


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