Enhancing Policy Decision Making with Large-Scale Digital Traces

Vanessa Frias-MartinezUniversity 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. Yes2. No

Mobile Digital Footprints…

…for Social Good?

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 MiningMachine LearningStatistical

MOBILE DIGITAL TRACES

To enhance or complement

information in an affordable manner

OUTLINE

Outline

• Cell Phone Data

• Projects with Social Impact– Cencell– AlertImpact

Cell Phone Data

Call Detail Records

Anonymized

Granularity1-4km²

CDR: Caller | Callee | Date | Duration | Geolocation

Modeling Human Behavior

Consumption•Number calls, duration, frequency, SMS/MMS/voice•Expenses•Handset Type and Features

Social•Degree of the social network •Strength of the contacts (Reciprocity & Frequency)•Geography of the social contacts

Mobility•Mobility Patterns (Entropy)•Diameter of mobility•Radius of gyration (Home/Work)

Over 270 variables

Cost-Effective Census Maps From Cell Phone Data

CenCell

Motivation: Census Maps

A/BC+CDE

National Statistical Institutes

A/BC+CDE

Important Data Comes at a Price

Expensive

Low resource regions

A/BC+CDE

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

Cost-effective Maps

NSI carries out surveys

Cell Phone Data

REDUCE COSTS

NSI surveys subset of regions

Forecasting Models

Predict the Present

Methodology

Classifying SELs - Training

Consumption

Social

Mobility

SEL

CLASSIFIER

Aggregated1-4km²

SEL

Classifying SELs - Testing

CLASSIFIER

Consumption

Social

Mobility

Aggregated

Experimental Evaluation

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)

Human Behavior and

Census Variables

Large Scale Quantitative Analysis

Consumption

Social

Mobility

InsightsConsumption Variables

Mobility Variables

AlertImpact

Understanding the Impact of Health Alerts using Cell Phone Data

H1N1 Mexico Timeline

PrefluClosing Schools

27th AprilReopen

6th May

Can we measure the impact that government alerts had on the mobility of the population ?

Evaluation

• Call Records from 1st Jan till 31st May 2009– Compute mobility as different number of BTSs visited

• Stages– Medical Alert - Stage 1 (17th-27th April)– Closing Schools - Stage 2 (28th-1st May)– Suspension of Essential Activities - Stage 3 (1st May-6th May)

• Baselines– same periods, different year (2008)

Changes in Mobility

April 27th May 1st May 6th

Alert Closed Shutdown Reopen

Baseline

Mobility reduced between 10% and 30%

Alert Closed Suspension Reopen

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%

BASELINEK

University Campus

Statistically Significant Decrease during Stages 2 and 3

Airport

Statistically Significant Increase during Stages 2 and 3

Take Away Message

Take Away Message

• Geolocated traces allow us to quantitatively – Model human behavior– Measure behavioral changes– Predict/Classify external sources of information

Future

• Enhance and complement the tools currently used by decision makers in organizations working for social good

– Use of open datasets, social media and other digital traces

Thanks !!

vfrias@umd.edu