An introduction May 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 2

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

4 –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 Metadata Call 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 presentatie 6 Source: 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 ->>>> 7 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 output for mobility reporting Vodafone Solution, controlled by Vodafone Mezuro

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 presentatie 10

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 tourism Roaming data

Results (1) Tourism –German tourists (= devices) 14 Source: Vodafone/Mezuro, compiled by SN

Tourism (2) German tourists at the coast Rainfall Devices Source: Vodafone/Mezuro, compiled by SN

16 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) 17 Source: Vodafone/Mezuro, compiled by SN

Tourism (5) Different type of communication 18 Source: 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? 19