1. Eurostat Item 10 Special session dedicated to big data sources with potential for tourism statistics The possible future impact of big data on tourism statistics and their mutual relation within a system of tourism statistics DG EUROSTAT – Christophe Demunter, tourism statistics & TF Big Data Working Group on Tourism Statistics Luxembourg, 21 and 22 September 2015

2. Eurostat Outline 1.Jigsaw falling into place ? 2.Lifecycle for the coming years ? 3.Sketch of big data in tourism statistics ? 4.Gaps & challenges !

3. Eurostat Step 1 – Finding the pieces of the puzzle

4. Eurostat Step 2 – Starting with the frame

5. Eurostat Step 3 – Solving the rest of the puzzle Oops… missing pieces !

6. Eurostat Step 3b,c,…z – Many brain cracking efforts later The full picture!

7. Eurostat Lifecycle for the coming years ? TOURISM STATISTICS Mobile phone data Payment cards data HOUSEHOLD & BUSINESS SURVEYS Other big data SHORT TERM → 'Traditional' surveys as main input for tourism statistics → Big data sources slowly becoming auxiliary information SHORT TERM → 'Traditional' surveys as main input for tourism statistics → Big data sources slowly becoming auxiliary information

8. Eurostat Lifecycle for the coming years ? (2) TOURISM STATISTICS Mobile phone data Payment cards data HOUSEHOLD & BUSINESS SURVEYS Other big data MID TERM → Weight of surveys decreases in favour of big data ? → Surveys no longer 'main filter' but 'one of the sources' ? MID TERM → Weight of surveys decreases in favour of big data ? → Surveys no longer 'main filter' but 'one of the sources' ?

9. Eurostat Lifecycle for the coming years ? (3) TOURISM STATISTICS Mobile phone data Payment cards data HOUSEHOLD & BUSINES S SURVEYS Other big data Web (prices) Web (prices) Bookings (nowcast /forecast) Bookings (nowcast /forecast) NEW LONGER TERM → 'Replacement of surveys continues (smaller samples) ? → Enhanced tourism statistics via embedding of newer sources ? LONGER TERM → 'Replacement of surveys continues (smaller samples) ? → Enhanced tourism statistics via embedding of newer sources ?

10. Eurostat Putting the building blocks together 1.Data on flows  Mobile phone data as the most obvious source? → Call Detail Records enhanced with other location info to improve coverage and completeness → Improved geographical and temporal detail (mid-week, long weekends, etc.) → Pinpointing location data to exact location (geo-matching with known location of accommodation establishments?)  Auxiliary information from other sources → Improving completeness via reservation data (the more remote the destination, the lower the use of mobile phones?), credit card use, traffic counts, smart meters etc.

11. Eurostat Putting the building blocks together (2) 2.Expenditure  Payment cards data as the most obvious source ? → P.O.S. transactions (derive product via merchant code, e.g. accommodation, retail, transport) → Quid transactions before trip (purchase of plane tickets) ? → ATM withdrawals to estimate cash payments at the destination (bias for shorter trips? bias for intra-eurozone?) → Clear filtering of non-tourist related transactions with foreign entities will be essential (e-commerce)  Auxiliary information from other sources → Breakdown of expenditure types (retail cashier data) → Survey, also for other characteristics but smaller scale  quality trade-off?

12. Eurostat Putting the building blocks together (3) ?? How to combine 1 & 2 ?? (unanswered…) 3.Other tourism info  Previously unavailable statistics → Using webscraping to estimate prices (REVPAR, …) → Using internet activity to estimate visitors at destination level (e.g. Wikistats project)  Nowcasting (flash estimates) and forecasting → Using search engines, booking & reservation info, social media scraping 4. Your views & feedback please !

13. Eurostat Gaps & challenges Completeness of the data  E.g socio-demographic info customer contract info? profiling?  E.g purpose of the trip profiling experiments ongoing  Other info such as 'composition of travel party' → Collected via a small follow-up survey how to link to other data? → Or… drop re-assessing user needs & priorities  balancing completeness with higher timeliness or higher temporal/geographical granularity

14. Eurostat Gaps & challenges (2) Matching different sources  Data-linking at micro-level: not likely to be feasible  Data-linking at macro-level: are we comparing apples & oranges?  Integrating different (big data) sources: gaps vs. risk of overlap and double counting?  Calendar: all sources simultaneously available to produce the desired statistical output ? or release determined by the weakest link in the production chain/system?

15. Eurostat Gaps & challenges (3) Substitution bias  Is the increase/decrease of the value of a given indicator driven by a real increase/decrease of what is measured or by substitution effects affecting the 'measurement tool'? → E.g. increase in expenditure (measured using credit cards) real increase or only a more widespread use of cards by tourists? → E.g. decrease in number of tourists (measured using mobile positioning data) real decrease or only a drop in use of mobile phones while abroad (in favour of newer/cheaper technologies or devices) ?  … an important new quality feature to assess/monitor!

16. Eurostat Gaps & challenges (4) Transformation & skills  Trust of users ?  Transition period with parallel systems (impact on cost?)  Shift in IT infrastructure and data treatment/processing capacity (see CBS traffic intensities statistics)  Shift from experts in survey methodology to bricklayers & purifiers of big data  … from data coLLectors to data coNNectors !

17. Eurostat Thank you for your attention !