The UN Global Pulse has published ‘Mobile Phone Network Data for Development’, a primer on how analysis of Call Detail Records (CDRs) can provide valuable information for humanitarian and development purposes.
‘Mobile Phone Network Data for Development’, is an accessible synthesis of a growing body of research on mobile phone data analysis in development or humanitarian contexts. The prime explains three types of indicators that can be extracted through analysis of CDRs:
- Mobility: As mobile phone users send and receive calls and messages through different cell towers, it is possible to “connect the dots” and reconstruct the movement patterns of a community. This information may be used to visualize daily rhythms of commuting to and from home, work, school, markets or clinics, but also has applications in modeling everything from the spread of disease to the movements of a disaster-affected population.
- Social Interaction: The geographic distribution of one’s social connections may be useful both for building demographic profiles of aggregated call traffic and understanding changes in behavior. Studies have shown that men and women tend to use their phones differently, as do different age groups. Frequently making and receiving calls with contacts outside of one’s immediate community is correlated with higher socio-economic class.
- Economic Activity: Mobile network operators use monthly airtime expenses to estimate the household income of anonymous subscribers in order to target appropriate services to them through advertising. When people in developing economies have more money to spend, they tend to spend a significant portion of it on topping off their mobile airtime credit. Monitoring airtime expenses for trends and sudden changes could prove useful for detecting the early impact of an economic crisis, as well as for measuring the impact of programmes designed to improve livelihoods.
‘Mobile Phone Network Data for Development’ includes a synthesis of several research examples and a summary of privacy protection considerations. For example, de-identified CDRs have allowed researchers to see aggregate geographic trends such as the flow of mass populations during after natural disasters, how malaria can spread via human movement, or the passage of commuters through a city at peak times.
Earlier this year Global Pulse also produced a “Big Data for Development Primer”, an introductory guide for the global development community, summarizing key terms, concepts, case studies and challenges around big data.
Thank you. Thus great work.
Great informative article. Thank you for sharing.