An introductionMay Offermans, Martijn Tennekes, Alex Priem, Shirley Ortega en Nico Heerschap

Using Mobile Phone Meta Data For

National Statistics

Content

1 Data Sources‐ Event Data Records(EDR)‐ Customer databases

2 Privacy and processing

3Results‐ Applications in statistics

• Daytime population• Tourism

4 Conclusions

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Source Call Detail Records/ Event Data Detail Records

Call Detail records can contain many variables like:

–the phone number of the subscriber originating the call (calling party)–the phone number receiving the call (called party)–the starting time of the call (date and time)–the call duration–the billing phone number that is charged for the call–the identification of the telephone exchange or equipment writing the record–a unique sequence number identifying the record–the disposition or the results of the call, indicating, for example, whether or not the call was connected–call type (voice, SMS, etc.)–Each exchange manufacturer decides which information is emitted on the tickets and how it is formatted. Examples:–Timestamp 3

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– Monthly 4 Billion Event Data/Detail Records of 6-7 million users contains information of:

‐ Antenna location‐ Time indicator‐ In- or outgoing‐ Technology information (data, sms, call ..dual/umts)‐ Roaming (foreign devices)

– Customer database (unique number of foreign callers per months)

Source – Mobile Phone MetadataCall Detail Records/ Event Data Detail Records

Applications under research

‐ Daytime population‐ Mobility, of which tourism‐ Safety‐ Demographics‐ Border traffic‐ Economical activity ‐ Disaster management or safety planning‐ Use of public services‐ Sociology (calling patterns)‐ Health

Population

Titel van de presentatie6Source: Vodafone/SN

– Problems big data‐ Dynamical data source that keeps on growing‐ Daily change of antenna locations (4G)‐ Software‐ Transporting data‐ Security issues‐ Privacy‐ Costs ->>>>

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Privacy & Process (1)

Privacy & Process (2)

Anonymized aggregated data

‐ Micro data from the mobile network will be transferred to a new server system.

‐ During this process most sensitive variables become hashed or deleted.

‐ Only Mezuro has access to the process to collect aggregated anonymized data Traffic data

(Events = CDR’s)

Replace User-IDs(Anonymisation – phase 1)

Automated ‘blind’ analysis

Aggregation & validation(Anonymisation – phase 2)

Validated outputfor mobility reporting

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Privacy & Process (3)

– Advantages‐ Save, quick, fast, cheap, limits the risks and

no personal data

– Disadvantages‐ Does not fit current methodological practice

• No personal data, so cannot be coupled to other personal data.

• Persons are not followed directly• No direct weighing

Research

– ‘New’ statistics- > Daytime population– Tourism statistics -> Inbound tourism

Titel van de presentatie10

Results (1) - Daytime Population

Source: Vodafone/Mezuro, compiled by SN

Results (2) - Day time population

Almere: commuter town?

Municipal Personal Records Database

Source: Vodafone/Mezuro, compiled by SN

Tourism

Inbound tourismRoaming data

Results (1) Tourism

– German tourists (= devices)

14Source: Vodafone/Mezuro, compiled by SN

Tourism (2) German tourists at the coast

Rainfall

Devices

Source: Vodafone/Mezuro, compiled by SN

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Portugese roaming data during 2013 UEFA Cup League final, Benfica (Portugal) - Chelsea (England)

Tourism (3) Portugese roaming

Source: Vodafone/Mezuro, compiled by SN

Tourism (4)

17Source: Vodafone/Mezuro, compiled by SN

Tourism (5) Different type of communication

18Source: Vodafone/Mezuro, compiled by SN

Conclusions for tourism– Potential

‐ Replace existing statistics and new statistics‐ Smaller area and smaller timeframes‐ Events‐ Also when 24 hour limit is dropped:

• Daytrips and number overnight stays• Flows of tourists• Tourist related areas

– Rather trends then volumes (benchmarking)

– Privacy issues, but also access (telecom providers)

– New methodological issues/new framework (representativeness)

– Role of national statistical offices?

– Revolutionary or evolutionary?

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