Mobile Phone Data for Children on the Move: Challenges and Opportunities

Today, 95% of the global population has 2G mobile phone coverage (GSMA 2017) and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data have the potential to revolutionize how we tackle humanitarian problems, such as many suffered by refugees all over the world (United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development. A world that counts: Mobilising the data revolution for sustainable development, 2014 [64]). While promising, mobile phone data and the new computational approaches bring both opportunities and challenges (Blumenstock in Estimating economic characteristics with phone data, pp. 72–76, 2018 [9]). Mobile phone traces contain detailed information regarding people’s whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals are of paramount importance (UNDG 2018). Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data are to be successfully applied in humanitarian contexts.

[1]  Taylor W. Brown,et al.  Exposure to opposing views on social media can increase political polarization , 2018, Proceedings of the National Academy of Sciences.

[2]  Henrik Harder,et al.  Children, Mobility, and Space , 2011 .

[3]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[4]  Ingmar Weber,et al.  Using Twitter Data to Estimate the Relationship between Short-term Mobility and Long-term Migration , 2017, WebSci.

[5]  Norbert Goldfield,et al.  Journal of Ambulatory Care Management , 1992 .

[6]  J. Gaudart,et al.  Using Mobile Phone Data to Predict the Spatial Spread of Cholera , 2015, Scientific Reports.

[7]  Jari Saramäki,et al.  From seconds to months: an overview of multi-scale dynamics of mobile telephone calls , 2015, The European Physical Journal B.

[8]  Roy Carr-Hill,et al.  Missing Millions and Measuring Development Progress , 2013 .

[9]  M. Barthelemy,et al.  From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.

[10]  J. Blumenstock,et al.  Divided We Call: Disparities in Access and Use of Mobile Phones in Rwanda , 2012 .

[11]  Nathan Eagle,et al.  Community Computing: Comparisons between Rural and Urban Societies Using Mobile Phone Data , 2009, 2009 International Conference on Computational Science and Engineering.

[12]  M. Barthelemy,et al.  Human mobility: Models and applications , 2017, 1710.00004.

[13]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[14]  Jane McAdam,et al.  Global Compact for Safe, Orderly and Regular Migration , 2018, International Journal of Refugee Law.

[15]  J. Blumenstock,et al.  The strength of long-range ties in population-scale social networks , 2018, Science.

[16]  Nitesh V. Chawla,et al.  Predictors of short-term decay of cell phone contacts in a large scale communication network , 2011, Soc. Networks.

[17]  Joshua A. Tucker,et al.  Less than you think: Prevalence and predictors of fake news dissemination on Facebook , 2019, Science Advances.

[18]  J. Fowler,et al.  Climate change may alter human physical activity patterns , 2017, Nature Human Behaviour.

[19]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[20]  Alex Rutherford,et al.  On the privacy-conscientious use of mobile phone data , 2018, Scientific Data.

[21]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[22]  B. Rosenfeld,et al.  The impact of detention on the health of asylum seekers. , 2003, The Journal of ambulatory care management.

[23]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[24]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[25]  Gabriel Cadamuro,et al.  Predicting poverty and wealth from mobile phone metadata , 2015, Science.

[26]  E. Paluck,et al.  Changing climates of conflict: A social network experiment in 56 schools , 2016, Proceedings of the National Academy of Sciences.

[27]  Etienne Huens,et al.  Data for Development: the D4D Challenge on Mobile Phone Data , 2012, ArXiv.

[28]  E. Zagheni,et al.  Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants , 2017 .

[29]  Un Desa Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[30]  Daniel Jurafsky,et al.  Word embeddings quantify 100 years of gender and ethnic stereotypes , 2017, Proceedings of the National Academy of Sciences.

[31]  Marta C. González,et al.  Coupling human mobility and social ties , 2015, Journal of The Royal Society Interface.

[32]  Jari Saramäki,et al.  Persistence of social signatures in human communication , 2012, Proceedings of the National Academy of Sciences.

[33]  Alex Pentland,et al.  Data for Refugees: The D4R Challenge on Mobility of Syrian Refugees in Turkey , 2018, ArXiv.

[34]  Debabrata Das,et al.  The 2030 Agenda for Sustainable Development: Where Does India Stand? , 2019, Journal of Rural Development.

[35]  Alex Pentland,et al.  Sensing the "Health State" of a Community , 2012, IEEE Pervasive Computing.

[36]  J. Shaman,et al.  Forecasting seasonal outbreaks of influenza , 2012, Proceedings of the National Academy of Sciences.

[37]  Xin Lu,et al.  Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh , 2016 .

[38]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[39]  Renaud Lambiotte,et al.  Uncovering space-independent communities in spatial networks , 2010, Proceedings of the National Academy of Sciences.

[40]  Venkata Rama Kiran Garimella,et al.  Inferring international and internal migration patterns from Twitter data , 2014, WWW.

[41]  Manuel Cebrián,et al.  Limited communication capacity unveils strategies for human interaction , 2013, Scientific Reports.

[42]  Naren Ramakrishnan,et al.  Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016 , 2019, Scientific Reports.

[43]  J. Beise,et al.  A child is a child: protecting children on the move from violence abuse and exploitation. , 2017 .

[44]  Joyce Chia,et al.  New York Declaration for Refugees and Migrants , 2016, Encyclopedia of the UN Sustainable Development Goals.

[45]  Albert-László Barabási,et al.  Collective Response of Human Populations to Large-Scale Emergencies , 2011, PloS one.

[46]  Maxime Lenormand,et al.  Immigrant community integration in world cities , 2016, PloS one.

[47]  David Lazer,et al.  Tracking employment shocks using mobile phone data , 2015, Journal of The Royal Society Interface.

[48]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[49]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[50]  Zbigniew Smoreda,et al.  D4D-Senegal: The Second Mobile Phone Data for Development Challenge , 2014, ArXiv.

[51]  Nathan Eagle,et al.  Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data , 2014, PloS one.

[52]  Joshua E. Blumenstock,et al.  Information Technology for Development Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from Rwanda Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from Rwanda , 2022 .

[53]  Hernán A. Makse,et al.  CUNY Academic Works , 2022 .

[54]  José Ignacio Alvarez-Hamelin,et al.  Socioeconomic correlations and stratification in social-communication networks , 2016, Journal of The Royal Society Interface.

[55]  Joshua Blumenstock,et al.  Estimating Economic Characteristics with Phone Data , 2018 .

[56]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[57]  Piotr Sapiezynski,et al.  Measuring Large-Scale Social Networks with High Resolution , 2014, PloS one.

[58]  John M. Marshall,et al.  Key traveller groups of relevance to spatial malaria transmission: a survey of movement patterns in four sub-Saharan African countries , 2016, Malaria Journal.

[59]  Kenth Engø-Monsen,et al.  Impact of human mobility on the emergence of dengue epidemics in Pakistan , 2015, Proceedings of the National Academy of Sciences.