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Cited by 38Human Mobility and Location-Based AnalysisDiverse Aspects of Tourism ResearchDigital Marketing and Social Media

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An Exploratory Analysis of Geotagged Photos From Instagram for Residents of and Visitors to Vienna

Gunter, Ulrich, Önder, Irem·2021·Journal of Hospitality & Tourism Research
DOI 10.1177/1096348020963689
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Abstract

This study uses geotagged photos from Instagram to identify differences between the popular places in Vienna for residents and visitors. Moreover, we explore whether geotagged data can be useful in determining tourism demand in Vienna. The spatial analysis of 627,632 geotagged photos reveals the top-50 locations in Vienna for all-, local-, and visiting-Instagram users based on three popularity indicators (numbers of likes, comments, and photos). The results show that the top locations unique to local users are closely linked to activities residents usually pursue or location types they usually visit at their place of dwelling. In using geotagged photos to predict actual tourist arrivals to Vienna, we conclude that only the popularity indicators number of likes and number of comments based on the location ID “Vienna, Austria” for visitors to Vienna should be used and not the number of photos, since this indicator does not automatically generate engagement.

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Crossref · 10.1177/1096348020963689

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Citations

Cites

Research works printed but not linked: 53

  1. 10.2307/1911894 · DOI
  2. 10.1016/j.jeconom.2010.01.004 · DOI
  3. 10.20955/r.92.521-36 · DOI
  4. 10.1016/0167-6245(94)90037-X · DOI
  5. Askitas N., Zimmermann K. F. (2009). Google econometrics and unemployment forecasting (DIW Berlin Discussion Paper No. 4201). http://ftp.iza.org/dp4201.pdf · DOI
  6. 10.1016/j.tourman.2014.07.014 · DOI
  7. Chatfield C., 2001, Time series forecasting
  8. 10.2307/41703503 · DOI · Paper page
  9. 10.1145/1631272.1631415 · DOI
  10. 10.1016/j.tourman.2016.06.013 · DOI · Paper page
  11. 10.1198/073500108000000015 · DOI
  12. 10.1145/1810617.1810626 · DOI
  13. 10.1177/1536867X1301300202 · DOI
  14. European Cities Marketing & MODUL University Vienna. (2018). The European Cities Marketing benchmarking report, 14th official edition 2017-2018. https://www.europeancitiesmarketing.com/european-cities-marketing-benchmarking-report-2018-shows-the-continuous-growth-of-european-city-tourism/
  15. EViews, 2017, EViews 10 user’s guide
  16. 10.1016/j.tele.2010.03.003 · DOI
  17. Frechtling D. C., 2001, Forecasting tourism demand: Methods and strategies
  18. 10.1016/j.apgeog.2015.08.002 · DOI
  19. 10.2307/2986645 · DOI
  20. 10.1016/j.jfineco.2004.03.008 · DOI
  21. 10.1016/j.jeconom.2005.01.004 · DOI
  22. 10.1109/MPRV.2008.71 · DOI
  23. 10.2307/1912791 · DOI · Paper page
  24. 10.1016/j.tourman.2014.06.017 · DOI
  25. 10.1016/j.annals.2016.10.007 · DOI
  26. 10.1177/1354816618793765 · DOI
  27. 10.1016/j.compenvurbsys.2015.09.001 · DOI
  28. 10.18637/jss.v027.i03 · DOI
  29. 10.1016/j.neucom.2012.02.049 · DOI
  30. 10.3138/carto.48.2.1839 · DOI
  31. 10.1109/IV.2010.94 · DOI
  32. 10.1007/978-3-540-27752-1 · DOI
  33. 10.1109/IE.2010.34 · DOI
  34. Mas-Colell A., 1995, Microeconomic theory
  35. 10.1111/j.2517-6161.1948.tb00012.x · DOI
  36. 10.1016/j.tmp.2016.11.002 · DOI
  37. 10.3727/108354216X14559233984773 · DOI
  38. 10.1177/0047287514563985 · DOI
  39. 10.1093/acprof:oso/9780198509882.001.0001 · DOI
  40. Pisati M., 2010, Stata Technical Bulletin, 60, 21
  41. 10.1145/1645953.1646211 · DOI
  42. 10.1016/0169-2070(94)90019-1 · DOI
  43. 10.1007/978-3-319-44263-1_2 · DOI
  44. Song H., 2009, The advanced econometrics of tourism demand
  45. 10.17265/2159-5313/2016.09.003 · DOI · Paper page
  46. 10.2307/143141 · DOI
  47. 10.1177/1354816618811558 · DOI
  48. 10.1016/j.tourman.2014.07.003 · DOI
  49. 10.1007/978-3-319-51168-9_4 · DOI
  50. 10.1177/0047287513500391 · DOI
  51. 10.1371/journal.pone.0154885 · DOI
  52. 10.1016/j.tmp.2014.01.001 · DOI
  53. 10.1145/2168752.2168770 · DOI

Cited by

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Metadata

Authors
Gunter, U., & Önder, I.
Affiliations
  • MODUL University Vienna (AT)
  • MODUL University Vienna
ORCID
Gunter, Ulrich
Venue
Journal of Hospitality & Tourism Research
Publisher
SAGE Publications
Published
2020-10-13
Volume
45
Issue
2
Pages
373-398
ISSN
  • 0741-5095 (ISSN-L)
  • 1096-3480
  • 1557-7554
DOI
10.1177/1096348020963689
OpenAlex
W3093030249
Cited by
38
References
1
Citations by year
2026: 2 · 2025: 10 · 2024: 9 · 2023: 4 · 2022: 8 · 2021: 4 · 2020: 1
FWCI
3.79
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
Journal of Hospitality & Tourism Research (publishedVersion)
Funders
Hochschuljubiläumsstiftung der Stadt Wien (H-117734/2017)
Topics
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
Keywords
geotagged photos, Instagram, tourism demand, tourist arrivals, residents versus visitors, social media engagement, likes and comments, Vienna, urban tourism, location-based data

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