CDR Data Analysis
for Epidemic Control
ITU Technical Team for CDAEC
Prof. R. Shibasaki,
Dr. H.Kanasugi,
Dr.A.Watayangkurn and
Dr.W. Ohira
Estimated People Movements (2015.06.07)
N=640,504
Entire Sierra Leone
Freetown
SHIBASAKI & SEKIMOTO LAB. CSIS/IIS/EDITORIA. UTOKYO
Bo
2015.08.26
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People Flow (7, June 2015) Sierra Leone
4
MapofSierraLeone
Freetown
(CapitalCity)
Possibleepicenter
Kenema
(Hospital)
Contribution of CDR Analysis
• Real-time Information on People Flow will help
–
–
–
–
Ebola outbreak analysis and control
Evacuation Guidance from Floods
Road Planning and Management etc.
Transboundary movement could be estimated by using IMSI, if
randomized with the same seed number.
• For MNOʼs
– Base station
optimization etc.
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PrivacyisProtectedwithRandomization
ByMNO(Mobile
NetworkOperator)
IMSI,
IMEIetc
Identification
information
connectedtoa
specificuser
Impossible torestore
IMSIandIMEIfromthe
RandomizedID.
Random
ization
RandomizedID
Randomized,unique
number
NOT connectedtoa
specificuser
RegulatoryAuthorities
（Analyst@ITU)
AggregatedAnalysisResults
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CDR(CallDetailRecord)data
Dataontimeandlocation(basestation)of
voice/messaging/datacommunicationofeachhandsetforbillingpurposes.Dataisrecordedonlywhen
calling,messaginganddatacommunication.
data
data
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AnImportantFinding
• Shiftingfromruralareastocitiesand
townsfortheoutbreak.
– TogetherwithPeopleFlow.
HowtoTrackPeopleFlow?
• QuestionnaireorInterview?
• Countingvehiclesatgatesor
surveillancepoints?
• Cellularphonedata(CDRanalysis)!
HowtoIdentifytheLocationofSubscribers?
GPS
Location:<good>
10m.– 200m.
Needtoembedlocationdata
transmission software
Source:http://www.itisholdings.com/article.asp?id=296
http://5.freshminutes.it/2008/05/20/geolocalisation-mobile-computing/
Cell-ID
Location:<coarse>
100m.– 4km.
Allhand-setscanbecoveredwithout
anyadditional softwareforhand-sets.
Allhand-sets
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MappingMovementofaHandsetfromCDRdata
Movementofahandset
(CELLTOWERMigration)
EstimatingMovingRouteandStayPointsofaSubscriber
fromtheMovementoftheHandset
Staypoints
Estimatingrouteandstay
pointsofasubscriber
throughinterpolation.
è Repeating
forallN
subscribers
Aggregation
Time
12:00pm
6:00am
0:00am
EstimatedMoving Route
andStayPointsofa
Subscriber
Move
Stay
Space
EstimatingHourlyPopulationDensity
byAggregatingNumberofPeopleinaGrid-Cell.
At8:00AM
Needtoestimatescalingfactor to covert
subscriber number tototalpopulation
CallingDemandDensityataspecifictimeslot
byusingpopulationdensityandcallingbehaviormodel
Needtoestimatecallingdemand oftotal
populationbasedon“callingbehaviormodel”.
Optimizingcell
towerlocation
People Flow (7, June 2015) Freetown
SHIBASAKI & SEKIMOTO LAB. CSIS/IIS/EDITORIA. UTOKYO
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Hourly Population Density (7, June 2015) Sierra Leone
SHIBASAKI & SEKIMOTO LAB. CSIS/IIS/EDITORIA. UTOKYO
2015.08.26
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Hourly Population Density (7, June 2015) Freetown
SHIBASAKI & SEKIMOTO LAB. CSIS/IIS/EDITORIA. UTOKYO
2015.08.26
19
Hourly Population Density (7, June 2015) Freetown
Further Analysis of People Flow;
Freetown Bo
SHIBASAKI & SEKIMOTO LAB. CSIS/IIS/EDITORIA. UTOKYO
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Suggestionsforfutureworks
• 2nd Pilotproject
– Trackingtransboundary(international)movement
ofpeopleamongSierraLeone,GuineaandLiberia.
– ConnectingdatawithRandomizedIMEI.
• DevelopmentofReal-timeMonitoringandAnalysis
SystemforSierraLeone,GuineaandLiberia
– MNO’s:RandomizingCDR
– RA’s:IntegratingRandomizedCDRandConductingAnalysis
toMapReal-TimePeopleFlow
– ITU:TechnicalSupportandCapacityBuilding
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Appendix;DataManagementScheme
Conductedinthe
premiseofMNO
underthesupervision
ofNationalRegulatory
CommissionandITU
InthepremiseofMNO
Randomized/Anonymized CDRdata
Supervision
National
Regulatory
Commission
ByMNO
OriginalCDRdataprovision
Aggregating for
Peopleflowstatistics
Peopleflowstatisticsdata
ByMNO
ByMNOwithtechnical
assistanceofUT
Peopleflowstatistics
Additional analysis
forEbolaControl
Reporting
ByUTunderthe
supervisionofITU
ByUTunderthesupervisionofITU
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全体の流れ
• 課題
– エボラは⼈の移動にともなって動く
– ⼈の移動に関するデータが無い。
• ソリューション
– CDR分析を⾏う。
• 効果、リスク（対応⽅法）
– ⽇常的に得られるデータで、安価にリアルタ
イムに⼈の移動データが得られる。
– 携帯電話事業者内でRandomizeし、統合解析
はRAが⾏うことで、プライバシーリスクは避
けられる。