Research Goal To extract human behavioral information from mobile digital traces in order to assist decision makers in organizations working for social development
TOOLS BEHAVIORAL INSIGHTS Energy RESEARCH DECISION MAKERS Health Education Safety Transportation Interviews, surveys: Information to assist on policy decisions Data Mining Machine Learning Statistical MOBILE DIGITAL TRACES To enhance or complement information in an affordable manner
Datasets Data for a city in Latin America (NSI) – 1200 regions (GUs) – SEL values from 0..100 Call Detail Records – 6 months, 500K customers – City has 920 coverage areas – 279 variables per coverage area
Evaluation Results Random Forests 86% 3 SELs (A,B,C) EM Clustering 68% 6 SELs (A,B,…,F)
AlertImpact Understanding the Impact of Health Alerts using Cell Phone Data
H1N1 Mexico Timeline Preflu Medical Alert 17th April Closing Schools 27th April Suspension 1st May Reope n 6th May
Can we measure the impact that government alerts had on the mobility of the population ?
Evaluation Call Records from 1 st Jan till 31 st May 2009 – Compute mobility as different number of BTSs visited Stages – Medical Alert - Stage 1 (17 th -27 th April) – Closing Schools - Stage 2 (28 th -1 st May) – Suspension of Essential Activities - Stage 3 (1 st May-6 th May) Baselines – same periods, different year (2008)
Changes in Mobility April 27thMay 1st May 6th AlertClosed Shutdown Reopen Baseline Mobility reduced between 10% and 30% Alert Closed SuspensionReopen
Changes in Epidemic Spreading Baseline (“preflu” behavior all weeks) Intervention (alert,closed,shutdown) Epidemic peak postponed 40 hours Reduced number of infected in peak agents by 10% BASELINE K
University Campus Statistically Significant Decrease during Stages 2 and 3
Airport Statistically Significant Increase during Stages 2 and 3
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