IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0128470.html
   My bibliography  Save this article

Quantifying International Travel Flows Using Flickr

Author

Listed:
  • Daniele Barchiesi
  • Helen Susannah Moat
  • Christian Alis
  • Steven Bishop
  • Tobias Preis

Abstract

Online social media platforms are opening up new opportunities to analyse human behaviour on an unprecedented scale. In some cases, the fast, cheap measurements of human behaviour gained from these platforms may offer an alternative to gathering such measurements using traditional, time consuming and expensive surveys. Here, we use geotagged photographs uploaded to the photo-sharing website Flickr to quantify international travel flows, by extracting the location of users and inferring trajectories to track their movement across time. We find that Flickr based estimates of the number of visitors to the United Kingdom significantly correlate with the official estimates released by the UK Office for National Statistics, for 28 countries for which official estimates are calculated. Our findings underline the potential for indicators of key aspects of human behaviour, such as mobility, to be generated from data attached to the vast volumes of photographs posted online.

Suggested Citation

  • Daniele Barchiesi & Helen Susannah Moat & Christian Alis & Steven Bishop & Tobias Preis, 2015. "Quantifying International Travel Flows Using Flickr," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0128470
    DOI: 10.1371/journal.pone.0128470
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128470
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0128470&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0128470?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    2. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    3. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    4. Delia Mocanu & Andrea Baronchelli & Nicola Perra & Bruno Gonçalves & Qian Zhang & Alessandro Vespignani, 2013. "The Twitter of Babel: Mapping World Languages through Microblogging Platforms," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    5. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bartosz Bursa & Markus Mailer & Kay W. Axhausen, 2022. "Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work," Transportation, Springer, vol. 49(5), pages 1465-1516, October.
    2. Ye, Zi & Clarke, Graham & Newing, Andy, 2021. "Estimating small-area demand of urban tourist for groceries: The case of Greater London," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    3. Huy Quan Vu & Shah Jahan Miah & Haiyang Xia & Gang Li & Birgit Muskat & Rob Law, 2023. "Advancing reliability assessment of venue-reference social media data for enhanced domestic tourism development," Information Technology & Tourism, Springer, vol. 25(3), pages 433-451, September.
    4. Spyridon Spyratos & Michele Vespe & Fabrizio Natale & Ingmar Weber & Emilio Zagheni & Marzia Rango, 2019. "Quantifying international human mobility patterns using Facebook Network data," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
    5. Tarmo Kalvet & Maarja Olesk & Marek Tiits & Janika Raun, 2020. "Innovative Tools for Tourism and Cultural Tourism Impact Assessment," Sustainability, MDPI, vol. 12(18), pages 1-30, September.
    6. Federico Botta & Helen Susannah Moat & H Eugene Stanley & Tobias Preis, 2015. "Quantifying Stock Return Distributions in Financial Markets," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-10, September.
    7. Tobias Preis & Federico Botta & Helen Susannah Moat, 2020. "Sensing global tourism numbers with millions of publicly shared online photographs," Environment and Planning A, , vol. 52(3), pages 471-477, May.
    8. Merve Alanyali & Tobias Preis & Helen Susannah Moat, 2016. "Tracking Protests Using Geotagged Flickr Photographs," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-8, March.
    9. Chua, Alvin & Servillo, Loris & Marcheggiani, Ernesto & Moere, Andrew Vande, 2016. "Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy," Tourism Management, Elsevier, vol. 57(C), pages 295-310.
    10. Yihong Yuan & Monica Medel, 2016. "Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.
    11. Xingshan Wang & Lu Tang & Wei Chen & Jianxin Zhang, 2022. "Impact and Recovery of Coastal Tourism Amid COVID-19: Tourism Flow Networks in Indonesia," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shota Saito & Yoshito Hirata & Kazutoshi Sasahara & Hideyuki Suzuki, 2015. "Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    2. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    3. Resce, Giuliano & Maynard, Diana, 2018. "What matters most to people around the world? Retrieving Better Life Index priorities on Twitter," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 61-75.
    4. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    5. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    6. Grechyna, Daryna, 2025. "Raising awareness of climate change: Nature, activists, politicians?," Ecological Economics, Elsevier, vol. 227(C).
    7. Letchford, Adrian & Preis, Tobias & Moat, Helen Susannah, 2016. "The advantage of simple paper abstracts," Journal of Informetrics, Elsevier, vol. 10(1), pages 1-8.
    8. Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020. "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers 20-046, ZEW - Leibniz Centre for European Economic Research.
    9. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    10. Kristina Gligorić & Arnaud Chiolero & Emre Kıcıman & Ryen W. White & Robert West, 2022. "Population-scale dietary interests during the COVID-19 pandemic," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    11. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    12. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    13. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    14. Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Papers 71, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
    15. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    16. Borondo, J. & Morales, A.J. & Benito, R.M. & Losada, J.C., 2014. "Mapping the online communication patterns of political conversations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 403-413.
    17. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    18. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    19. Junzhao Ma & Dewi Tojib & Yelena Tsarenko, 2022. "Sex Robots: Are We Ready for Them? An Exploration of the Psychological Mechanisms Underlying People’s Receptiveness of Sex Robots," Journal of Business Ethics, Springer, vol. 178(4), pages 1091-1107, July.
    20. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0128470. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.