Mobile positioning as a data source for estimation of travel statistics

1. Mobilepositioningas a datasourceforestimationoftravelstatistics Jaanus Kroon Head, Balanceofpayments and EconomicStatisticsDepartment 4.10.2010 Paris WPTGS; OECD

2. Outline • Current data sources and main challenges • The idea of mobile positioning and the project of Eesti Pank • Estimation of inbound travellers • Estimation of outbound travellers • Conclusions

3. Simplified algorithm of travel account estimation in BoP • Debits and credits • Countries Number oftravellers and lenghtofstay x Dailyexpenditures + Prepaidtravelagency and hotelservices + Expendituresofshort-termworkers and students = Travel

4. Data sourcesbefore 2009 Poor coverage (ShengenTreaty) Cancellingof NSI surveys

5. Data sourcesafter 2009

6. Challenge: how to estimate border crossing statistics? • Regular Statistical Surveys of border crossing? • Expensive • Quality issues? (sufficient geography etc.) • Installing car-counters on border-roads? • Only indirect indicator • Needs additional Surveying of the structure of “cars/trucks/travellers” • Deriving border-crossing statistics from mobile-phone roaming/positioning? • The best balance in between merits and costs • Methodology needs regular re-estimations • …

7. ICT in Travel Studies • ICT infulences travel • Travel influences ICT

8. The idea of mobile positioning • Mobile positioning -registering signals of cellphone usage and location by mobile phone network antennas • When you travel to a foreign country, your mobile phone keeps working via foreign network: you are roaming. • Registering roming activities could be the data source for estimating numbers of travellersand their lenght of stay • Simplification: customer of a phone operator is a resident of the same country

9. Co-operation with Tartu University spin-off project • LBS Positum (www.positium.ee) • founded in 2001 in cooperation with geographers, city planners and architects • services: using the idea of mobile positioning and methods for geographical studies and city planning to organize better and safer living, planning the city and sustainable environment • Data source EMT (major of 3 mobile providers in Estonia) • 2 procurement contracts for developing models for: • Inbound travel 2008-2009 • Outbound travel 2009-2010 • Regular monthly cooperation (data license)

10. Estimation of inbound travellers (1)

11. Estimation of inbound travellers (2) • # of travellers ~ # of abroad registered roamed cell phones • Lenghtofstay • - Daysbetweenfirst and last phoneoperation • Bordercountries: 2 visits 2 and 3 days • Others: 1 visit 8 days

12. Estimation of inbound travellers (3) • Nationality according to the country of origin of SIM-card • Differentiation: • Elimination of noise (sea transit, random border swiching) • Transit travel (airport, transit-road corridors) • Permanent (border) workers and students • One day visitors • Tourists • Grossing up / calibration • Penetration model

13. Estimation of inbound travellers (4) • “Noise” on borders (Certain antennas should be excluded)

14. Estimation of inbound travellers (5) • Foreign workers • more than 7 visits • more than 30 days

15. Estimation of inbound travellers (6) • Transit • Mobilepositionsindefinedareaonly • Sea ports • Airports • Certain road corridors: • Ikla-Pärnu-Tallinn • Riga-Pskov

16. Estimation of inbound travellers (7) • Visitors (up to 24 hr)

17. Estimation of inbound travellers (8) • Tourists (over 24 h)

18. Estimationofinboundtravellers (9) • Grossing up sampling and calibration • Bases on real statistical data series available up to 2008 • Calibration according to currently available border-crossing indicators • (Extra-EU statistics, Sea-ports and airport indirect statistics) • Statistical models for each country or country groups, taking into account… • coverage (only 1 network) • mobile phone usage pattern (surveys) • Pre-paid cards & cheap roaming service cards • …

19. Estimationofinboundtravellers (10) Accommodation statistics

20. Estimationofinboundtravellers (11)

21. Estimation of outbound travellers (1) • Lenght of stay and the geography

22. Estimation of outbound travellers (2) • Determining the number of visits

23. Estimation of outbound travellers (3) • Number of visits by countries visited

24. Estimationofoutboundtravellers (5) • Destination and transit country • Analyses of the travel pattern according to the lenght of stay and number of countries visited per day • Cross border worker if • # of trips during 6 month is over 6 • Sum of the lenght of days exceeds 30 days • Analyses of final destinations (over the world travellers)

25. Estimation of outbound travellers (4) • Number of days by countries visited

26. Estimationofoutboundtravellers (6) • Elimination of noise • Estonian border masts • Border masts abroad • Seamen • …

27. Penetration model needs to take into account… Estimationofoutboundtravellers (7) • The market shareof EMT • Otheroperators (2, compensationcofficents) • # ofnon-phoneusersabroad • Workers • EMT detaileduser market research • Datacalibration • Extra EU bordercrossing • Ports and airportstatistics • Accommodationstatisticsby NSI

28. Estimationofoutboundtravellers (8) • First results (red – travel statistics, blue – model)

29. Conclusions • … still too early to make  • So far we have… • covered the “hole” in statistics and become the only one institution in Estonia providing border-crossing statistics • been cost effective (data licenses are cheaper than regular border-crossing surveying) • taken advantage of partner scale-effect (one data source, wide range of usage possibilities) • Major questions… • models need to be re-estimated according to changes in economic environment (no real data besides) • developments (competition) on mobile market and ICT • The future of mobile phones vrs Skype?

30. CHALLENGE FOR STATISTICS! • Mobile positioning would have much wider possibilities to serve statistics as a data source Thank you!

31. Scientific Articles (not directly about current topic) • Tiru, M., Saluveer E., Ahas, R., Aasa, A. 2009. Web-based monitoring tool for assessing space-time mobility of tourists using mobile positioning data: Positium Barometer. Journal of Urban Technology, 17(1): • 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. • Kuusik, A., Ahas, R., Tiru, M. 2009. Analysing Repeat Visitation on Country Level wirh Passive Mobile Positioning Method: An Estonian Case Study. XVII Scientific Conference on Economic Policy, Estonia 1–3.07.2009 in Tartu and Värska. • Ahas, R., Saluvee, E., Tiru,M. and Silm, S. 2008.Mobile Positioning Based Tourism Monitoring System: Positium Barometer. In: O’Connor, P., Höpken, W. and Gretzel, U. (Eds.), Springer Computer Science: Information and Communication Technologies in Tourism, pp. 475-485. • Ahas, R., Aasa, A., Silm, S., Tiru, M. 2007. Mobile positioning data in tourism studies and monitoring: case study in Tartu, Estonia. In: Sigala, M., Mich, L., Murphy, J. (Eds.), Springer Computer Science: Information and Communication Technologies in Tourism, ISBN: 978-3-211-69564-7, pp. 119-128.