Chapter 10 Tracking Tourists’ Mobility via the Internet
DOI: 10.23912/9781911635383-4576 | ISBN: 9781911635383 |
Published: Nov 2020 | Component type: chapter |
Published in: Tracking Tourists | Parent DOI: 10.23912/9781911635383-4277 |
Abstract
Tracking tourists’ mobility and migratory patterns may be conducted by collating their digital footprints via the web. Data of this sort may be sourced via apps such as Google Maps, or websites that collate IP numbers and their proximity to mobile phone towers. It may also be collected via big datasets such as ticketing websites, via mini programs such as those used by WeChat, and via non-big data sources such as blogs. This form of location-based tracking is a highly efficient and cost- effective means of understanding where consumers are located. The devastating impacts of the COVID-19 pandemic upon the tourism industry have clearly indicated the potential for tracking via the internet to assist the tourism industry. Google’s analytical data that was released publicly in March 2020 provided an excellent example of this – both in terms of the insights that can emerge from data of this type, and consumers’ perceptions of the ethics of this form of data. This chapter will explore the technique, including the types of location-based data that can emerge from websites, the conceptual learnings that have emerged from this technique, and, importantly, the ethical implications of this form of data.
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Contributors
- Anne Hardy, University of Tasmania (Author) https://orcid.org/0000-0003-1461-2967
For the source title:
- Anne Hardy, University of Tasmania (Author) https://orcid.org/0000-0003-1461-2967
Cite as
Hardy, 2020
Hardy, A. (2020) "Chapter 10 Tracking Tourists’ Mobility via the Internet" In: Hardy, A. (ed) . Oxford: Goodfellow Publishers http://dx.doi.org/10.23912/9781911635383-4576
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References
Bangwayo-Skeete, P.F. and Skeete, R.W. (2015) Can Google data improve the forecasting performance of tourist arrivals? A mixed-data sampling approach, Tourism Management, 46, 454-464
https://doi.org/10.1016/j.tourman.2014.07.014
Cheng, A., Ren, G., Hong, T., Nam, K. and Koo, C. (2019) An exploratory analysis of travel-related WeChat mini program usage: Affordance Theory perspective, In Pesonen J. and Neidhardt J. (eds), Information and Communication Technologies in Tourism 2019, Springer, Cham, pp. 333-343.
https://doi.org/10.1007/978-3-030-05940-8_26
Cohen, E. (1972) Toward a sociology of international tourism, Social Research, 39(1), 164-182
Fesenmaier, D.R., Xiang, Z., Pan, B. and Law, R. (2010) An analysis of search engine use for travel planning, In: Gretzel, U., Law, R. and Fuchs, M. (eds.) Information and Communication Technologies in Tourism, Springer, New York, pp. 381-392.
https://doi.org/10.1007/978-3-211-99407-8_32
Google (2020a) FAQ about Google Trends data, Google Support, Available at: https://support.google.com/trends/answer/4365533?hl=en [Accessed 11 August, 2020]
Google (2020b) COVID-19 Community Mobility Report, Available at: https://www.gstatic.com/covid19/mobility/2020-03-29_AU_Mobility_Report_en.pdf [Accessed 6 June, 2020]
Gunter, U. and Önder, I. (2016) Forecasting city arrivals with Google Analytics, Annals of Tourism Research, 61, 199-212.
https://doi.org/10.1016/j.annals.2016.10.007
Höpken, W., Eberle, T. and Fuchs, M. (2018) Search engine traffic as input for predicting tourist arrivals, Information and Communication Technologies in Tourism 2018, Springer, Cham, pp. 381-393.
https://doi.org/10.1007/978-3-319-72923-7_29
Klepers, A. (2020) Online ticket purchase as source for tracking tourism mobilities. Proceedings of the Council of Australasian Tourism and Hospitality Educators Conference 2020 Conference. Auckland, pp. 21-2.
Kozinets, R. (2019) Netnography: The Essential Guide to Qualitative Social Media Research. Third Edition, SAGE Publications.
https://doi.org/10.4324/9781003001430-2
Letts, S. (2019) Google sued by the ACCC over the alleged misuse of personal data, ABC News, Available at: https://www.abc.net.au/news/2019-10-29/ google-faces-accc-federal-court-misleading-use-of-data/11649356 [Accessed 30 October, 2019].
Lew, A. and McKercher, B. (2006) Modeling tourist movements: a local destination analysis, Annals of Tourism Research, 33(2), 403-423
https://doi.org/10.1016/j.annals.2005.12.002
Leung, X. Y., Wang, F., Wu, B., Bai, B., Stahura, K. A. and Xie, Z. (2012) A social network analysis of overseas tourist movement patterns in Beijing: The impact of the Olympic Games, International Journal of Tourism Research, 14(5), 469-484.
https://doi.org/10.1002/jtr.876
Liao, S. (2018) Google still tracks you through the web if you turn off Location History, The Verge, Available at: https://www.theverge.com/2018/8/13/ 17684660/google-turn-off-location-history-data [Accessed 11 August, 2020]
Mobilewalla (2020) First, second and third-party data: Better together, Available at: https://www.mobilewalla.com/blog/first-second-and-third-party-data- differences [Accessed 11 August, 2020]
Pink, S., Ruckenstein, M., Willim, R. and Duque, M. (2018) Broken data: Conceptualising data in an emerging world, Big Data & Society, 5(1), 1-13.
https://doi.org/10.1177/2053951717753228
Pirolli, P. and Card, S. (1999) Information foraging, Psychological Review, 106 (4), 643-675.
https://doi.org/10.1037/0033-295X.106.4.643
Quanzhong, G. (2017) Mini program and its future, News Writ 3, 28-30
TechWeb (2018), Available at: http://www.techweb.com.cn/data/2018-07- 11/2684850.shtml [Accessed 11 August, 2020]
Van der Zee, E. and Bertocchi, D. (2018) Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews, Information Technology and Tourism, 20, 152-180.
https://doi.org/10.1007/s40558-018-0128-5
Volchek, K., Liu, A., Song, H. and Buhalis, D. (2018) Forecasting tourist arrivals at attractions: Search engine empowered methodologies, Tourism Economics, special issue: Tourism Forecasting, 1-23.
https://doi.org/10.1177/1354816618811558
Vosen, S. and Schmidt, T. (2011) Forecasting private consumption: survey-based indicators vs. Google trends, Journal of Forecasting, 30(6), 565-578.
https://doi.org/10.1002/for.1213
Wills, J. (2020) 7 ways Amazon uses big data to stalk you, Investopedia, Available at: https://www.investopedia.com/articles/insights/090716/7-ways- amazon-uses-big-data-stalk-you-amzn.asp [Accessed 11 August, 2020]
Xiang, Z. and Gretzel, U. (2010) Role of social media in online travel information search. Tour Management, 31(2), 179-188
https://doi.org/10.1016/j.tourman.2009.02.016
Zhu, X., Leung A., Lohmann, G. and de Oliveira Santos, G.E. (2020) What do we know about tourist dispersal? A systematic review of literature (1980-2019) Oral presentation at the Council for Australasian Tourism and Hospitality Education 2020 Conference, Auckland, February 2020.