MOBILE POSITIONING AS A POSSIBLE DATA SOURCE FOR INTERNATIONAL TRAVEL SERVICE STATISTICS

Jaanus KroonStatistics Department, Bank of Estonia

UN CONFERENCE OF EUROPEAN STATISTICIANS Seminar on New Frontiers for Statistical Data Collection

31.10.2012, Geneva

Outline

• Background of the “story”• Exploring alternative data sources• The phenomena of mobile positioning• Methodological aspects

– Estimation of inbound travel– Estimation of outbound travel

• Co-operation model• Conclusions

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Background (1)• Bank of Estonia - a provider of official statistics in Estonia,

– responsible for• Monetary & Financial Statistics• External Sector Statistics

• Balance of Payments, reflecting cross-border transactions, consists of many sub-items, including– Current Account

• International Trade in Services • Simplified algorithm for “Travel Services”

– exports and imports by countries

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Length of stay

Length of stay

Daily expenditures

Daily expendituresx

Prepaid travel agency

and hotel services

Prepaid travel agency

and hotel services

+Expenditures of short-term workers and

students

Expenditures of short-term workers and

students+Number of

travellersNumber of travellers x

Border-crossing statistics

Background (2)• Data sources for Border-Crossing Statistics

• Changing environment– 2004/2007 Entering into the Schengen: abolishment of border-controls

• Exploring alternatives (2007…)– 2008… The economic crisis and remarkable budget cuts of NSI 4

Poor coverage

Since 2007, only Extra-EU flows

Secondary data source

Cancelled since 2010

Cancelled since 2010

Exploring alternatives (1)

Alternatives Pros Cons

To finance the border-survey of NSI

• NSI experience•Possibility to partly integrate with visitor motivation surveys

•Time and labour intensive•Unreasonably expensive•Insufficient reliability

Implement an accommodation statistics-based assessment model

•Monthly frequency•Easy to implement•Reliable geographical allocation•Low costs

•Additional costs regarding regular calibration survey•Does not cover outbound part•Estimation errors

Participate in the project initiated by the Road Office to install car-counters on the road at border-crossings

•High periodicity•Supplement to harbour and airport statistics

•Investment costs•Additional costs regarding calibration survey• Errors and quality issues

Credit-card information, based on Northern European Transaction Services (NETS)

•High periodicity•Gives estimation on total expenditures and indirect geo allocations•Only one service provider•No administrative burden•Low costs for compiler

•Additional costs for calibration survey•Negative experience from neighbouring countries•“Noise” related to e-commerce

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Exploring alternatives (2)

Alternatives Pros Cons

Derive travelling statistics from mobile-phone roaming information

•High periodicity•Representativeness (almost everyone has a cell phone)•Operational information in time and space (incl. geographical allocation)•No administrative burden

•Lack of experience and practices •Undefined co-operation model with data providers•Additional costs regarding the calibration survey of mobile usage pattern•Remarkable IT resources for data processing

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• Mobile positioning as the final choice– High quality, cost effective, low labour intensive– Availability of potential partner experienced in using mobile positioning

data in urban and regional geography/planning• Department of Geography, University of Tartu (Estonia) and spin-off

company, Positium LBS. • Scale effect: regular data exchange, calibration surveys

– Cooperation contract for data collection methodology and models • 2008-2010

The phenomena of mobile positioning

• Mobile positioning - locating (pinpointing) mobile telephones using radio waves– Active mobile positioning - tracking the location of mobile phones in real

time through a network of antennas– Passive mobile positioning - uses location and activity information from

historical log files stored by mobile service providers (for charging clients)

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Methodological aspects (1)

• Presumable residence of phone owners is determined by the registration country of the SIM-card

• The methodology is based on the anonymised patterns of roaming activities of mobile phones– by non-residents in resident reporting operator network (inbound

travel)– by reporting operator domestic clients in networks abroad (outbound

travel)• Country and region specific aspects should be taken into account when

determining calculation algorithms:– Same-day visits

• Exclusion of transit travel– Overnight visits and length of stay

• Exclusion of long-term stay– Exclusion of “cross-border noise” 8

Methodological aspects (2)

• From the statistical point of view, mobile owners are a representative large sample within a statistical population whose spatial behavior and characteristics in time can be extended to the entire population

• Extension coefficients (weights) take into account when grossing up– Market share of reporting mobile operators– Mobile phone usage traditions

• abroad by age and social groups• in Estonia by visitor country

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Inbound travel (1)

• Number of travellers – number of roamed cell phones in domestic net

• Length of stay– days between first and last activity

• Border countries: 2 visits 2 and 3 days • Other countries: 1 visit 8 days

Roaming activities per day by single mobile phone

Inbound travel (2)• Cross-border noise

– Certain antennas should be detected and excluded• sea transit, random border switching

Inbound travel (3)• Transit travel

– Antennas in 10 transit corridors are detected and excluded• Sea ports• Airports• Road transit corridors

Inbound travel (4)• Results

Inbound travel (5)• Quality

Outbound travel (1)• Number of visits

– number of domestic phones roamed in foreign networks• Lenght of stay

– days between first and last activity

In Estonia

Abroad

Outbound travel (2)

• Elimination of cross-border noise– registration of phone roaming by Estonian residents that

are incidentally in the coverage area of foreign mobile operators

• Destination and transit country– Analyses of the travel pattern according to the length of

stay and number of countries visited per day• One of the criteria is the distance from Estonia

• Long-term visits (residents working or studying abroad)– Stay for over 183 days during the past 12 months.

Outbound travel (3)

• Example: number of days by countries visited

Outbound travel (4)

• Example: the quality– “Blue” – official statistics until 2009– “Red” – mobile positioning results

TurkeyEgypt

Co-operation model

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Conclusions (1)• Advantages

– In time data • information as co-product is already stored by mobile operators as the owner of

the data source – Cost effective

• No direct costs associated with the network of interviewers • No burden for travellers as potential respondents.

• Disadvantages– Tailor made methodology (universal does not exists)– Legal and privacy issues– Sophisticated data processing and storage– Co-operation with mobile operators as statistical reporters– Sampling and calibration issues (total population is unknown)

• models need to be re-estimated according to changes in economic environment– Unknown future / technical progress (ww wifi, internet phones etc.) 20

Conclusions (2)

• Challenges– Mobile positioning has wider possibilities to serve official statistics as a

data source in• Regional visitor statistics • Domestic travel statistics• Detailed and specified research• Etc.

– In Estonia it would be legally possible to request data on mobile positioning directly from the source to serve official statistics

• First priority to already collected, readily available data

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References

• International Recommendations for Tourism Statistics 2008. United Nations 2010.• Mobiilpositsioneerimisel põhinev väliskülastajate turismistatistikute uuring. OÜ

Positium LBS, Eesti Pank; Tartu 2009.• Mobiilpositsioneerimisel põhinev väliskülastajate turismistatistikute uuring:

penetratsioonimudeli kirjeldus. OÜ Positium LBS, Eesti Pank; Tartu 2009.• Mobiilpositsioneerimisel põhinev Eesti residentide välisturismistatistikute uuring. OÜ

Positium LBS, Eesti Pank; Tartu 2010.• Tiru, M. & Ahas, R., “Using mobile positioning data for tourism statistics:

methodological and legal issues”. 10th International Forum on Tourism Statistics, Portugal 2010.

• Ahas, R., Tiru, M., Saluveer, E. & Demunter, C., “Mobile telephones and mobile positioning data as source for statistics: Estonian experiences” Conference on New Techniques and Technologies for Statistics, 2011.

• Ahas, R. Aasa, A., Roose, A., Mark, Ü., Silm, S. 2008. Evaluating passive mobile positioning data for tourism surveys: An Estonian case study. Tourism Management 29(3): 469–486.

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THANK YOU!jaanus.kroon@eestipank.ee

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