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Information Technology & Tourism

, Volume 21, Issue 1, pp 23–43 | Cite as

Determining the usual environment of cardholders as a key factor to measure the evolution of domestic tourism

  • Juan de Dios Romero PalopEmail author
  • Juan Murillo Arias
  • Diego J. Bodas-Sagi
  • Heribert Valero Lapaz
Original Research

Abstract

Domestic tourism is harder to analyse compared to international tourism due to its smaller data footprint generation, as most times private means of transport are used, no border is crossed, and no lodging is registered. Digital data sources can be a useful, but still underused, complement to official survey-based statistics to fill this lack of reliable information. The present paper covers a research gap in the use of card transactions data (on site payments and cash withdrawals) to provide an innovative methodology to enhance vision on domestic tourism dynamics. The chosen approach is based on the United Nations World Tourism Organization definition of ‘usual environment’: “the geographical area (though not necessarily a contiguous one) within which an individual conducts his/her regular life routines” Upon this premise, a methodology has been developed in order to use transactional footprints of cardholders to delineate their usual environment, and subsequently to classify transactions as ‘touristic’ or ‘non-touristic’. So as to ensure scalability, the resulting procedure is non- territory reliant, and can therefore be adapted to different geographies by varying one single parameter. Some practical applications are described in Sect. 5 through two use cases carried out in Spain and Mexico by BBVA.

Keywords

Usual environment Big data Domestic tourism Digital footprint Payments Applied data science 

Notes

Acknowledgements

The authors would like to thank the two main institutions whose partnership made possible the outcome of this paper: Banco Bilbao Vizcaya Argentaria (BBVA, a global financial corporation) for providing the dataset for this research. Special thanks to Elena Alfaro Martinez, Jon Ander Beracoechea and Fabien Girardin for their support to this applied research project. Exceltur (Alliance for Excellency in Tourism, a non-profit group formed by the Chairmen of the 23 leading Spanish tourist groups), and specially to Eva Hurtado and Óscar Perelli, for providing useful insights and stimulating discussions around current gaps in tourism intelligence, and inspiring suggestions about the methodology followed in this research.

References

  1. Baggio R (2019) Measuring tourism: methods, indicators, and needs. In: Fayos-Solà E, Cooper C (eds) The future of tourism. Springer, New York, pp 255–269CrossRefGoogle Scholar
  2. Bodas DJ, García J, Murillo Arias J, Pacce M, Rodrigo T, Ruiz de Aguirre P et al (2018) Measuring retail trade using card transactional data. Working paperGoogle Scholar
  3. BBVA Data & Analytics (2016) Tableau Public. https://public.tableau.com/profile/bbva.data.analytics. Accessed 1 Oct 2018
  4. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, no 34, pp 226–231Google Scholar
  5. European Commision (2014) Restructuring of Catalunya Banc S.A. through its acquisition by BBVA. Brussels. http://ec.europa.eu/competition/state_aid/cases/255638/255638_1647742_125_2.pdf. Accessed 1 Oct 2018
  6. Eurostat (2014) Feasibility study on the use of mobile positioning data for tourism statistics. http://mobfs.positium.ee/data/uploads/reports/consolidated-report.pdf. Accessed 1 Oct 2018
  7. González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 423:479–482Google Scholar
  8. Hahsler M, Piekenbrock M, Arya S, Mount D (2017) dbscan: density based clustering of applications with noise (DBSCAN) and related algorithms. R package version, 1-0Google Scholar
  9. Heerschap N, Ortega S, Priem A, Offermans M (2014) Innovation of tourism statistics through the use of new big data sources. In: 12th global forum on tourism statistics, Prague, CZGoogle Scholar
  10. Instituto Nacional de Estadística (2015) Metodología Encuesta de Turismo de Residentes (ETR/FAMILITUR). https://www.ine.es/daco/daco42/etr/etr_metodo_tasas_enlazadas.pdf. Accessed 1 Oct 2018
  11. Instituto Nacional de Estadística (2018) Página oficial Instituto Nacional de Estadística. From official population figures referring to revision of municipal register 1 January. http://www.ine.es/jaxiT3/Tabla.htm?t=2852&L=1. Accessed 1 Oct 2018
  12. Koerbitz W, Önder I, Hubmann-Haidvogel AC (2013) Identifying tourist dispersion in austria by digital footprints. In: Cantoni L, Xiang Z (eds) Information and communication technologies in tourism. Springer, Berlin, pp 495–506Google Scholar
  13. Kozak M, Rimmington M (2000) Tourist satisfaction with Mallorca, Spain, as an off-season holiday destination. J Travel Res 38(3):260–269CrossRefGoogle Scholar
  14. R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  15. Secretaría de Turismo de México (2016) Colaboración sobre Big Data y Turismo. http://www.datatur.sectur.gob.mx:81/Reportes/bigdata/bigdata.htm
  16. Sobolevsky S, Bojic I, Belyi A, Sitko I, Hawelka B, Murillo Arias J et al (2015) Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity. In: Proceedings—2015 IEEE international congress on big dataGoogle Scholar
  17. Tkacz G, Galbraith JW (2013) Nowcasting GDP: electronic payments, data vintages and the timing of data releases. CIRANO Working PapersGoogle Scholar
  18. UNWTO (2010) International Recommendations for Tourism Statistics 2008. United Nations, Department of Economic and Social Affairs, Statistics Division. United Nations Publications, New YorkGoogle Scholar
  19. World Travel & Tourism Council (WTTC) (2018) Travel & tourism economic impact 2018 Spain. https://www.wttc.org/-/media/files/reports/economic-impact-research/countries-2018/spain2018.pdf
  20. Xu R, Wunsch DC (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16:645–678CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Juan de Dios Romero Palop
    • 1
    Email author
  • Juan Murillo Arias
    • 1
  • Diego J. Bodas-Sagi
    • 1
  • Heribert Valero Lapaz
    • 1
  1. 1.BBVA Data & AnalyticsMadridSpain

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