Feasibility Study on the Use of Mobile Positioning Data in Tourism Statistics Big Data Seminar, 2nd June 2014 Ossi Nurmi

Agenda

Eurostat Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics

Data access in Finland Feasibility of use: coherence (in tourism statistics)

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Eurostat Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics

Project time: January 2013 – March 2014

The Aim of the Project

Exploring the possibilities - and limits - of using mobile

positioning data stored by mobile network providers for measuring tourism flows

Main Project Objectives

Assess feasibility to access databases with mobile positioning data in European countries

Assess the feasibility to use mobile positioning data for tourism statistics in the European context

Identify, discuss and address the main challenges for implementation

Assess the potential impact on cost-efficiency of data production

Assess the possibility to expand the methodology to other domains and define joint algorithms

Accessing Mobile Positioning Data in Finland

Main mobile network operators in Finland are Elisa, Sonera and DNA

Two main related authorities are the Data Protection Ombudsman (Tietosuojavaltuutettu) and the Finnish Telecom Regulatory Authority (Viestintävirasto)

Statistics Finland had initial meetings with all operators and the main authorities

Based on the request of Statistics Finland, the Office of the Data Protection Ombudsman prepared a statement concerning the use of CDRs (call detail records)

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Barriers of Access in Finland

Main barriers of access in Finland 1. Current Statistics Act doesn’t grant Statistics Finland the

authority to collect mobile positioning data from operators 2. According to the Act of Protection of Privacy in Electronic

Communications, the data containing identification may only be processed by a person employed or acting on behalf of the telecommunications operator

3. Raw mobile positioning data consitutes as personal data even if subscriber ID is anonymized

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Possible Paths for Using Mobile Positioning Data in Statistics

Voluntary basis: Operators process data into statistical aggregates – either themselves or by a third party

Legal basis: The national legislation should be updated to authorize national statistical institutes to obtain and process the raw mobile positioning data

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Feasibility of Use: Coherence in Tourism Statistics

Task coordinator Statistics Finland In-depth testing of tourism statistics compiled based on

mobile positioning data Analysis of coherence compared to reference statistics Key questions to be addressed:

Domain coverage: inbound, outbound and domestics tourism?

Tourism breakdowns: same-day and overnight trips? Coherence to existing statistical indicators? Reasons for

deviations? 13/06/2014 9 Tietopalveluyksikkö/Viestintä

Reference Data – Tourism Statistics Mobile positioning data Supply statistics

(=accommodation) Demand statistics

Target population Outbound / domestic: Population of the reference country Inbound: non-resident tourists

Accommodation establishments (all or above threshold)

Population over 15 years

Frame Data of mobile phone subscribers

Business / tourism register.

Population register or area frame

Source data Administrative Enterprise survey Survey of individuals Sampling design Census / sample Census / sample Sample Time units available

Day / week / month etc. Month Quarter / Year

Regional areas Any customized area NUTS 2 Country Nationality breakdown

Possible Possible Only residents

Timeliness 1-2 weeks 5-8 weeks 7-8 weeks Legal basis None Regulation 692/2011 Regulation 692/2011

Reference Data – Related Statistics Mobile positioning

data Border interview Passenger

statistics Border control

Target population

Outbound / domestic: Population of the reference country Inbound: non-resident tourists

Inbound visitors to the reference country.

All passengers by transport mode (air / sea / train etc.)

All passengers passing through border control.

Frame Data of mobile phone subscribers

Main border crossing locations

Register of transport authorities.

Register of border authority.

Source data Administrative Survey of individuals Administrative Administrative

Sampling design Census / sample Sample Census Census Time units available

Day / week / month etc. Month / quarter Day / week / month

Day / week / month

Regional areas Any customized area Country Country Country Nationality breakdown

Possible Possible Not possible Possible

Timeliness 1-2 weeks 10-20 weeks 1-2 weeks 1-2 weeks Legal basis None None EU Regulations National

Example: Very Good Coherence

Mobile positioning data provides very good consistency overall and broken down into countries. There is slight over-coverage as more trips are registered.

Inbound Overnight Trips: Accommodation Statistics, EU27>EE

0

50 000

100 000

150 000

200 000

250 000

300 000

350 000

Jan-

09M

ar-0

9M

ay-0

9Ju

l-09

Sep-

09No

v-09

Jan-

10M

ar-1

0M

ay-1

0Ju

l-10

Sep-

10No

v-10

Jan-

11M

ar-1

1M

ay-1

1Ju

l-11

Sep-

11No

v-11

Jan-

12M

ar-1

2M

ay-1

2Ju

l-12

Sep-

12No

v-12

MOB_IN(EU-27)_OVERNIGHT SUPPLY_EE(EU-27)_ARR

Example: Very good coherence

Most passengers travel by ferry between Finland and Estonia. Consistency is excellent. Number trips is less in mobile positioning other nationalities (than FI and EE) and transit passengers are on the ferry

Ferry passengers between Finland and Estonia

Example: Moderate Coherence Outbound Overnight Trips: Demand Statistics, EE>EU27

050 000

100 000150 000200 000250 000300 000350 000400 000450 000500 000

Q1-0

9

Q2-0

9

Q3-0

9

Q4-0

9

Q1-1

0

Q2-1

0

Q3-1

0

Q4-1

0

Q1-1

1

Q2-1

1

Q3-1

1

Q4-1

1

Q1-1

2

Q2-1

2

Q3-1

2

Q4-1

2

MOB_OUT(EU-27)_OVERNIGHT DEMAND_EE(EU-27)_OVERNIGHT

Mobile positioning data contains many such outbound trips that do not qualify as tourism trips in the Demand Survey either due to frequency, purpose or duration of the trip.

Example: Low Coherence

0

20 000

40 000

60 000

80 000

100 000

120 000

140 000

160 000

180 000

Jan-

09M

ar-0

9M

ay-0

9Ju

l-09

Sep-

09No

v-09

Jan-

10M

ar-1

0M

ay-1

0Ju

l-10

Sep-

10No

v-10

Jan-

11M

ar-1

1M

ay-1

1Ju

l-11

Sep-

11No

v-11

Jan-

12M

ar-1

2M

ay-1

2Ju

l-12

Sep-

12No

v-12

MOB_EE(RU) BORDCONT_EE(RU)

Inbound Overnight Trips: Border Control, RU>EE

Border control registers all trips, regardless of purpose. Very short trips close to the border may be seriously underestimated in mobile positioning data because these tourists might not use the roaming services of MNOs in the

inbound country.

Example: Domestic Trips Outside Usual Environment

Using LAU-1 for defining usual environment Using LAU-2 for defining usual environment

Coherence: Strength/Weaknesses Main strengths of mobile positioning data 1. Excellent consistency over time for the number of trips and nights spent 2. Superior coverage for overnight trips when compared to Supply Statistics: covers also trips in

non-rented or non-registered accommodation 3. Possibility to produce breakdowns based upon region and nationality 4. Possibility to apply rules for usual environment 5. Many short trips are excluded from mobile positioning data

Main weaknesses of mobile positioning data 1. No additional data about the trip

(purpose, expenditure, accommodation, means of transport) 2. Potential problems in the method for accurate breakdown of trips into same-day and overnight

trips 3. Over-coverage issues related to usual environment

(purpose, duration or frequency of trip) 4. Under-coverage issues based on mobile phone use:

some tourists don’t use their phone abroad

Main Conclusions

• Mobile positioning data alone cannot fulfill the requirements of the regulation on tourism statistics (EU 692 / 2011)

• Mobile positioning data provides good estimates for the number of trips, nights spent and destination

• Mobile positioning data doesn’t produce information on purpose of trip, type of accommodation or expenditure need for traditional surveys

More Information

More information and all reports of the Eurostat Feasibility study can be found on the project website:

http://mobfs.positium.ee/

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