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Big Data and Mobility Analysis

Presentation at Columbia U, April 9th, 2013

Emmanuel Letouzé

on 2 June 2013

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Transcript of Big Data and Mobility Analysis

Emmanuel Letouzé
Workshop on ICTs and Children & Youth on the Move
Columbia University, April 9th 2013 Big Data & Mobility Patterns 1. The Big Data Revolution 5. Principles
& policies Cell-phones in Nigeria
100,000 mobile phones in 2002
10,000,000 in 2012 Call Detail Records (CDRs) used to study:
Impact of human mobility on malaria transmission
Slum dynamics
Internal migration patterns
Track movement to predict cholera outbreak Many CDR-based mobility measures have been developed
• Detecting important places (Home or work, other important locations)
• Individual mobility, e.g. Radius of gyration
Collective mobility. e.g. estimation of traffic volumes 3. Measures & Methods Analytical challenges and risks 4. Risks and Challenges Big Data for Mobility Analysis 2. Existing applications to mobility analysis Big Data is not about the big data, it's about the nature of the data and the research around it Movement of an individual in Rwanda over 4 years. Source: Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from RwandaJ. Blumenstock, 2011 Source: Inferring Human Dynamics in Slums Using Mobile Phone Data
Amy P. Wesolowski and Nathan Eagle Email data used to study international migration Source: You are where you E-mail: Using E-mail Data to EstimateInternational Migration Rates.
Emilio Zagheni and Ingmar Weber Data is owned by private companies who are reluctant to share it. Most studies require partnerships between private companies---telecom, Yahoo!... Examples of individual mobility measures Study of mobility using email or social media/Twitter data is less advanced. But attempt at correcting the sample bias in email data. How about other streams: remote-sensing/satellite images? Sample bias/penetration and attempts at correcting it
Extract 'insights' from the data
Rush to judgement Technical challenges Access to the data? Data sharing? Ethical-institutional risks PRIVACY.
Digital divide & re-centralization Do No Harm: Norms? Tools?
Contextualization & Empowerment
Partnerships: Data and Analytical Philanthropy Thank you
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