Abstract
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.
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Notes
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https://web.archive.org/web/20181204153621/https://www.internetlivestats.com/google-search-statistics. All URLs were accessed in November 2018, and are stored in the Internet Archive.
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The data was retrieved in tabular form from the Google Trends API using the R package gtrendsR: https://cran.r-project.org/web/packages/gtrendsR.
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Map based on R package: https://github.com/sdesabbata/BivariateTMap.
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Acknowledgements
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.
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Ballatore, A., Scheider, S., Spierings, B. (2020). Tracing Tourism Geographies with Google Trends: A Dutch Case Study. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D., Mansourian, A. (eds) Geospatial Technologies for Local and Regional Development. AGILE 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-14745-7_9
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