Tracing Tourism Geographies with Google Trends: A Dutch Case Study

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Search engines make information about places available to billions of users, who explore geographic information for a variety of purposes. The aggregated, large-scale search behavioural statistics provided by Google Trends can provide new knowledge about the spatial and temporal variation in interest in places. Such search data can provide useful knowledge for tourism management, especially in relation to the current crisis of tourist (over)crowding, capturing intense spatial concentrations of interest. Taking the Amsterdam metropolitan area as a case study and Google Trends as a data source, this article studies the spatial and temporal variation in interest in places at multiple scales, from 2007 to 2017. First, we analyze the global interest in the Netherlands and Amsterdam, comparing it with hotel visit data. Second, we compare interest in municipalities, and observe changes within the same municipalities. This interdisciplinary study shows how search data can trace new geographies between the interest origin (what place users search from) and the interest destination (what place users search for), with potential applications to tourism management and cognate disciplines.


Interest geography Place search Web science Google Trends Tourism Amsterdam Netherlands 



The authors gratefully acknowledge Google for making some of its search data publicly available, Flavio Ponzio for providing insights on Search Engine Optimisation, and Stefano De Sabbata for his bi-variate choropleth library.


  1. Askitas N, Zimmermann KF (2015) The internet as a data source for advancement in social sciences. Int J Manpow 36(1):2–12CrossRefGoogle Scholar
  2. Ballatore A, Graham M, Sen S (2017) Digital hegemonies: the localness of search engine results. Ann Am Assoc Geogr 107(5):1194–1215Google Scholar
  3. Ballatore A, Jokar Arsanjani J (2018) Placing Wikimapia: an exploratory analysis. Int J Geogr Inf Sci 1–18Google Scholar
  4. Ballatore A, Scheider S, Lemmens R (2018) In: Mansourian A, Pilesjö P, Harrie L, van Lammeren R (eds) Patterns of consumption and connectedness in GIS web sources. Geospatial technologies for all. Agile 2018. Springer, Berlin, pp 129–148Google Scholar
  5. Choi H, Varian H (2012) Predicting the present with Google Trends. Econ Rec 88(1):2–9CrossRefGoogle Scholar
  6. Dergiades T, Mavragani E, Pan B (2018) Google trends and tourists’ arrivals: emerging biases and proposed corrections. Tour Manag 66:108–120CrossRefGoogle Scholar
  7. Goel S, Hofman JM, Lahaie S, Pennock DM, Watts DJ (2010) Predicting consumer behavior with web search. Proc Natl Acad Sci USA 107(41):17486–17490CrossRefGoogle Scholar
  8. Gospodini A (2006) Portraying, classifying and understanding the emerging landscapes in the post-industrial city. Cities 23(5):311–330CrossRefGoogle Scholar
  9. Graham M, De Sabbata S, Zook MA (2015) Towards a study of information geographies: (im) mutable augmentations and a mapping of the geographies of information. Geo Geogr Environ 2(1):88–105CrossRefGoogle Scholar
  10. Hall T, Barrett H (2017) Urban geography, 5th edn. Routledge, LondonGoogle Scholar
  11. Hendler J, Shadbolt N, Hall W, Berners-Lee T, Weitzner D (2008) Web science: an interdisciplinary approach to understanding the web. Commun ACM 51(7):60–69CrossRefGoogle Scholar
  12. Jun S-P, Yoo HS, Choi S (2018) Ten years of research change using Google Trends: from the perspective of big data utilizations and applications. Technol Forecast Soc Chang 130:69–87CrossRefGoogle Scholar
  13. Kitchin R (2014) Big data, new epistemologies and paradigm shifts. Big Data Soc 1(1):1–12CrossRefGoogle Scholar
  14. Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of Google Flu: traps in big data analysis. Science 343(6176):1203–1205CrossRefGoogle Scholar
  15. Li X, Pan B, Law R, Huang X (2017) Forecasting tourism demand with composite search index. Tour Manag 59:57–66CrossRefGoogle Scholar
  16. Neuts B, Vanneste D (2018) Contextual effects on crowding perception: an analysis of Antwerp and Amsterdam. J Econ Soc Geogr 109(3):402–419CrossRefGoogle Scholar
  17. Novy J, Colomb C (2017) Protest and resistance in the tourist city. Routledge, LondonGoogle Scholar
  18. Önder I, Gunter U (2016) Forecasting tourism demand with Google Trends for a major European city destination. Tour Anal 21(2–3):203–220CrossRefGoogle Scholar
  19. Pan B, Chenguang Wu D, Song H (2012) Forecasting hotel room demand using search engine data. J Hosp Tour Technol 3(3):196–210Google Scholar
  20. Popp M (2012) Positive and negative urban tourist crowding: Florence, Italy. Tour Geogr 14(1):50–72CrossRefGoogle Scholar
  21. Siliverstovs B, Wochner DS (2018) Google trends and reality: Do the proportions match?: appraising the informational value of online search behavior: evidence from swiss tourism regions. J Econ Behav Organ 145:1–23CrossRefGoogle Scholar
  22. Singleton AD, Spielman S, Brunsdon C (2016) Establishing a framework for open geographic information science. Int J Geogr Inf Sci 30(8):1507–1521CrossRefGoogle Scholar
  23. Spierings B (2013) Fixing missing links in shopping routes: reflections on intra-urban borders and city centre redevelopment in Nijmegen, The Netherlands. Cities 34:44–51CrossRefGoogle Scholar
  24. Stephens-Davidowitz SI (2013) Essays using Google Data. PhD thesis, Harvard University, Cambridge, MAGoogle Scholar
  25. Yang X, Pan B, Evans JA, Bendu Lv (2015) Forecasting Chinese tourist volume with search engine data. Tour Manag 46:386–397CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeographyBirkbeck, University of LondonLondonUK
  2. 2.Human Geography and PlanningUtrecht UniversityUtrechtThe Netherlands

Personalised recommendations