GeoJournal

, Volume 80, Issue 4, pp 503–518 | Cite as

Is bigger better? The emergence of big data as a tool for international development policy

Article

Abstract

The use of digital communication technologies, and of mobile phones in particular, has seen an exponential rise in low- and middle-income countries over the last decade. These data, emitted as a byproduct of technologies such as mobile phone location information and calling metadata, have the potential to fill some of the problematic gaps in data resources available to country policymakers and international development organisations. Using three examples of current big data initiatives in the international development field, we examine the implications of these new types of data for development policy and planning: their advantages and drawbacks, emerging practices relating to their use, and how they potentially influence ideas and policies of development. We also assess the politics of these new types of digital data, which are often collected and processed by corporations or by researchers in industrialised countries. Our analysis indicates that these new data sources already represent an important complement to country-level statistics, but that there are currently important challenges which will need to addressed if the promises of big data in development are to be fulfilled.

Keywords

Big data Development Policy 

References

  1. BBC News. (2011). Mobile phones help to target disaster aid, says study. http://www.bbc.co.uk/news/technology-14761144.
  2. 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), e1001083. doi:10.1371/journal.pmed.1001083.CrossRefGoogle Scholar
  3. Berdou, E. (2012). Participatory technologies and participatory methodologies: Ways forward for innovative thinking and practice. IKM Working Paper No. 17.Google Scholar
  4. Berlingerio, M., Calabrese, F., Di Lorenzo, G., Nair, R., Pinelli, F., & Sbodio, M. L. (2013). AllAboard: A system for exploring urban mobility and optimizing public transport using cellphone data. In Machine learning and knowledge discovery in databases (pp. 663–666). Berlin: Springer.Google Scholar
  5. Blessing, M. (2005). Het verzet tegen de Volkstelling van 1971. Historische Nieuwsblad nr. 8/2005. http://www.historischnieuwsblad.nl/nl/artikel/6697/het-verzet-tegen-de-volkstelling-van-1971.html.
  6. Blumenstock, J. E. (2012). Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda. Information Technology for Development, 18(2), 107–125.CrossRefGoogle Scholar
  7. Boase, J., & Ling, R. (2013). Measuring mobile phone use: Self-report versus log data. Journal of Computer-Mediated Communication, 18(4), 508–519. doi:10.1111/jcc4.12021.CrossRefGoogle Scholar
  8. Borgman, C. (2014). Big data, little data and beyond. Cambridge: MIT Press.Google Scholar
  9. Cavallo, A. (2013). Scraped data and sticky prices. MIT Sloan Working Paper, http://www.mit.edu/~afc/. Accessed 4.10.2013.
  10. Cavallo, A., Cavallo, E., & Rigobon, R. (2013). Prices and supply disruptions during natural disasters. Working Paper 19474, NBER.Google Scholar
  11. Chambers, R. (1997). Whose reality counts? Putting the first last. Intermediate Technology Publications Ltd (ITP).Google Scholar
  12. Collier, P. (2007). The Bottom Billion: Why the Poorest Countries are Failing and What Can Be Done About It. Oxford: Oxford University Press.Google Scholar
  13. Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., et al. (2013). Beyond the geotag: Situating ‘big data’and leveraging the potential of the geoweb. Cartography and Geographic Information Science, 40(2), 130–139.CrossRefGoogle Scholar
  14. Dandeker, Christopher. (1990). Surveillance, power and modernity. Cambridge: Polity Press.Google Scholar
  15. de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the crowd: The privacy bounds of human mobility. Scientific reports, 3.Google Scholar
  16. Donner, Jonathan. (2010). Framing M4D: The utility of continuity and the dual heritage of “mobiles and development”. Electronic Journal on Information Systems in Developing Countries, 44(3), 1–16.Google Scholar
  17. Doron, A., & Jeffrey, R. (2013). The great Indian phone book: How the cheap cell phone changes business, politics, and daily life. London: Hurst&Co.CrossRefGoogle Scholar
  18. Eagle, N., & Pentland, A. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.CrossRefGoogle Scholar
  19. Eagle, N., de Montjoye, Y. A., & Bettencourt, L. M. (2009). Community computing: Comparisons between rural and urban societies using mobile phone data. In International conference on computational science and engineering, 2009 (CSE’09) (vol. 4, pp. 144–150), IEEE.Google Scholar
  20. Easterly, W. (2014). The Tyranny of Experts: Economists, Dictators, and the Forgotten Rights of the Poor. New York: Basic Books.Google Scholar
  21. Financial Times. (2013). Argentina: Questioning official inflation can land you in jail. Accessed September 13 http://blogs.ft.com/beyond-brics/2013/09/13/argentina-inflation-diverging-from-official-numbers-can-land-you-in-jail/#axzz2gYghm6jJ.
  22. Frias-Martinez, V., Virseda, J., Rubio, A., & Frias-Martinez, E. (2010). Towards large scale technology impact analyses: Automatic residential localization from mobile phone-call data. In Proceedings of the 4th ACM/IEEE international conference on information and communication technologies and development (p. 11), ACM.Google Scholar
  23. Godard, X. (2003). Urban transport and mobility in African cities. Crisis and inventive disorder. Paper prepared for TRB annual meeting January 2003. http://onlinepubs.trb.org/onlinepubs/archive/am/03-2786.pdf.
  24. González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2012). Assessing the bias in communication networks sampled from twitter. Available at SSRN 2185134.Google Scholar
  25. Greenleaf, G. (2012). Global data privacy laws: 89 countries, and accelerating. Queen Mary University of London, School of Law Legal Studies Research Paper No. 98/2012.Google Scholar
  26. Heeks, R., & Kenny, C. (2002). The economics of ICTs and global inequality: Convergence or divergence for developing countries? Development informatics. Working Paper No. 10a, Institute for Development Policy and Management, University of Manchester.Google Scholar
  27. Hildebrandt, M. (2013) Slaves to big data. Or are we? Keynote, 25th June 2013 9th annual conference on internet, Law & Politics (IDP 2013, Barcelona).Google Scholar
  28. ITU. (2013a). The world in 2013. International telecommunications union. http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2013.pdf.
  29. ITU. (2013b). International Internet connectivity in Latin America and the Caribbean. Geneva: International Telecommunications Union.Google Scholar
  30. Jerven, M. (2013). Poor numbers: How we are misled by African development statistics and what to do about it. Ithaca: Cornell University Press.Google Scholar
  31. Keeter, S. (2012). Survey research, its new frontiers, and democracy. Public Opinion Quarterly, 76(3), 600–608.CrossRefGoogle Scholar
  32. Kirkpatrick, R. (2011). Data philanthropy: Public and private sector data sharing for global resilience. http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience.
  33. Klein, N. (2008). The shock doctrine: The rise of disaster capitalism. New York: Metropolitan.Google Scholar
  34. Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.CrossRefGoogle Scholar
  35. Licoppe, C. (2004). ‘Connected’ presence: The emergence of a new repertoire for managing social relationships in a changing communication technoscape. Environment and Planning D: Society and Space, 22(1), 135–156.CrossRefGoogle Scholar
  36. Ling, R., & Donner, J. (2009). Mobile communication. Cambridge: Polity Press.Google Scholar
  37. Lombard, J. (2006). Enjeux privés dans le transport public d’Abidjan et de Dakar. Géocarrefour, 81(2), 167–174.CrossRefGoogle Scholar
  38. Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics. 22, 3–42.Google Scholar
  39. Lyon, D. (2007). Surveillance studies: An overview. Cambridge: Polity Press.Google Scholar
  40. Mann, L. (2013). Blogpost on OII’s Policy and Internet Blog: Big Data and Informal Economies in Africa. Accessed 2.10.2013. http://blogs.oii.ox.ac.uk/policy/seeing-like-a-machine-big-data-and-the-challenges-of-measuring-africas-informal-economies/.
  41. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big data: The next frontier for innovation, competition and productivity. Washington, DC: McKinsey Global Institute.Google Scholar
  42. Musa, P. F., Meso, P., & Mbarika, V. W. (2005). Toward sustainable adoption of technologies for human development in Sub-Saharan Africa: precursors, diagnostics, and prescriptions. Communications of the Association for Information Systems, 15.Google Scholar
  43. NetMob. (2013). Mobile phone data for development: Analysis of mobile phone datasets for the development of Ivory Coast. NetMob conference, May 1–3 2013, MIT, Cambridge, USA.Google Scholar
  44. New York Times. (2000). Who lives here? Who’s asking? In a Black Community, Official Mistrust Hinders Census. Mary 16, 2000. http://www.nytimes.com/2000/05/16/nyregion/who-lives-here-who-s-asking-black-community-official-mistrust-hinders-census.html?pagewanted=all&src=pm.
  45. New York Times. (2011). Haiti: Cellphone tracking helps groups set up more effective aid distribution, study says. http://www.nytimes.com/2011/09/06/health/06global.html?_r=2&scp=1&sq=haiti%20bengtsson&st=cse&pagewanted=all.
  46. Orange. (2012). D4D project. http://www.d4d.orange.com/learn-more.
  47. Reporters Without Borders. (2013). 2013 World Press Freedom Index. http://fr.rsf.org/IMG/pdf/classement_2013_gb-bd.pdf.
  48. Schroeder, R. (2014). Big Data: Towards a more Scientific Social Science and Humanities? In M. Graham, & W. H. Dutton (Eds.), Society and the Internet: How networks of information are changing our lives (pp. 164–176). Oxford: OUP.Google Scholar
  49. Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. New Haven: Yale University Press.Google Scholar
  50. Schifferes, S., Newman, N., Thurman, N., Corney, D. P. A., Goker, A., & Martin C. (2013) Identifying and verifying news through social media: Developing a user-centred tool for professional journalists. Paper presented at The Future of Journalism Conference, 12–13 September 2013, Cardiff, UK.Google Scholar
  51. Sen, A. (1999). Development as Freedom. Oxford: Oxford University Press.Google Scholar
  52. Taylor, L. (2014). No place to hide? The ethics and analytics of tracking mobility using African mobile phone data. Unpublished paper, University of Amsterdam. http://www.academia.edu/7502204/No_place_to_hide_The_ethics_and_analytics_of_tracking_mobility_using_mobile_phone_data.

Interviews

  1. Bengtsson, Linus. Director, Flowminder. Interviewed 16.5.2013Google Scholar
  2. Blondel, Vincent. Professor of applied mathematics at the Université Catholique de Louvain and organiser of the D4D challenge. Interviewed 29.3.13Google Scholar
  3. Cavallo, Alberto. Cecil and Ida Green Career Development Assistant Professor of Applied Economics, MIT. Interviewed 15.11.2012Google Scholar
  4. de Cordes, Nicolas. Vice President of Marketing Vision, Orange-France Telecom Group. Interviewed 16.4.2013Google Scholar
  5. Kirkpatrick, Robert. Director, UN Global Pulse. Interviewed 14.5.2013Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.Oxford Internet InstituteOxfordUK

Personalised recommendations