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Enhancing Policy Decision Making with Large-Scale Digital Traces

Enhancing Policy Decision Making with Large-Scale Digital Traces. Vanessa Frias-Martinez University of Maryland NFAIS, February 2014. 5.9 billion 87%. 3.2 billion unique users 45%. mobile devices >>humans . Have you ever heard of DATIFICATION? 1. Yes

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Enhancing Policy Decision Making with Large-Scale Digital Traces

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  1. EnhancingPolicyDecisionMakingwithLarge-Scale Digital Traces Vanessa Frias-Martinez University of Maryland NFAIS, February 2014

  2. 5.9 billion 87% 3.2 billion unique users 45% mobile devices >>humans

  3. Have you ever heard of DATIFICATION? 1. Yes 2. No

  4. Mobile Digital Footprints… …for Social Good?

  5. ResearchGoal To extract human behavioral information from mobile digital traces in order to assist decision makers in organizations working for social development

  6. TOOLS Energy BEHAVIORAL INSIGHTS Education To enhance or complement information in an affordable manner Interviews, surveys: Information to assist on policy decisions Health Data Mining Machine Learning Statistical Transportation MOBILE DIGITAL TRACES Safety RESEARCH DECISION MAKERS

  7. OUTLINE

  8. Outline • Cell Phone Data • Projects with Social Impact • Cencell • AlertImpact

  9. CellPhone Data

  10. CallDetail Records Granularity 1-4km² Anonymized CDR: Caller | Callee | Date | Duration | Geolocation

  11. ModelingHumanBehavior Over 270 variables

  12. CenCell Cost-EffectiveCensusMaps FromCellPhone Data

  13. Motivation: CensusMaps A/B C+ C D E

  14. NationalStatisticalInstitutes A/B C+ C D E

  15. Important Data Comes at a Price Expensive Lowresourceregions A/B C+ C D E

  16. Can the variables extracted from Call Detail Records be used as predictors of regional socioeconomic levels (SELs)?

  17. Cost-effectiveMaps NSI surveyssubset of regions NSI carriesoutsurveys CellPhone Data ForecastingModels REDUCE COSTS PredictthePresent

  18. Methodology

  19. Classifying SELs - Training • SEL CLASSIFIER Aggregated 1-4km²

  20. ClassifyingSELs - Testing CLASSIFIER SEL Aggregated

  21. Experimental Evaluation

  22. 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

  23. EvaluationResults RandomForests 86% 3 SELs (A,B,C) EM Clustering 68% 6 SELs (A,B,…,F)

  24. HumanBehaviorandCensus Variables

  25. LargeScaleQuantitativeAnalysis

  26. Insights Consumption Variables Mobility Variables

  27. AlertImpact UnderstandingtheImpact of HealthAlertsusingCellPhone Data

  28. H1N1 MexicoTimeline

  29. Can wemeasuretheimpactthatgovernmentalertshadonthemobility of thepopulation ?

  30. Evaluation • Call Records from 1stJantill 31stMay 2009 • Compute mobility as differentnumber of BTSsvisited • Stages • MedicalAlert - Stage 1 (17th-27thApril) • ClosingSchools - Stage 2 (28th-1stMay) • Suspension of EssentialActivities - Stage 3 (1st May-6thMay) • Baselines • sameperiods, differentyear (2008)

  31. Changes in Mobility May 6th April 27th May 1st Mobilityreducedbetween 10% and 30% Reopen Alert Closed Shutdown Alert Suspension Reopen Closed Baseline

  32. Changes in EpidemicSpreading Baseline (“preflu” behaviorallweeks) Intervention (alert,closed,shutdown) BASELINE K Epidemicpeakpostponed 40 hours Reducednumber of infected in peakagentsby 10%

  33. University Campus StatisticallySignificantDecreaseduringStages 2 and 3

  34. Airport StatisticallySignificantIncreaseduringStages 2 and 3

  35. TakeAwayMessage

  36. TakeAwayMessage • Geolocated traces allowustoquantitatively • Modelhumanbehavior • Measurebehavioralchanges • Predict/Classifyexternalsources of information

  37. Future • Enhance and complementthetoolscurrentlyusedbydecisionmakersin organizationsworkingforsocial good • Use of open datasets, social media and other digital traces

  38. Thanks !! vfrias@umd.edu

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