Advertisement

Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) Data

  • Joey Ying Lee
  • Ming-Hsiang Tsou
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Social media services and location-based photo-sharing applications, such as Flickr, Twitter, and Instagram, provide a promising opportunity for studying tourist behaviors and activities. Researchers can use public accessible geo-tagged photos to map and analyze hotspots and tourist activities in various tourist attractions. This research studies geo-tagged Flickr photos collected from the Grand Canyon area within 12 months (2014/12/01–2015/11/30) using kernel density estimate (KDE) mapping, Exif (Exchangeable image file format) data, and dynamic time warping (DTW) methods. Different spatiotemporal movement patterns of tourists and popular points of interests (POIs) in the Grand Canyon area are identified and visualized in GIS maps. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. We found that winter tourists in the Grand Canyon explore fewer POIs comparing to summer tourists based on their Flickr data. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. Weekend tourists are more likely to stay around the lodge area comparing to weekday tourists who have visited more remote areas in the park, such as the north of Pima Point. These tourist activities and spatiotemporal patterns can be used for the improvement of national park facility management, regional tourism, and local transportation plans.

Keywords

Spatiotemporal Hotspot analysis Geo-tagged photos Tourism Flickr Grand Canyon 

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation, under Grant No. 1416509 and Grant No. 163464. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

  1. Ashley C, De Brine P, Lehr A, Wilde H (2007) The role of the tourism sector in expanding economic opportunity. John F. Kennedy School of Government, Harvard University, CambridgeGoogle Scholar
  2. Birenboim A (2016) New approaches to the study of tourist experiences in time and space. Tour Geogr 18(1):9–17CrossRefGoogle Scholar
  3. Chen CF, Chen PC (2012) Research note: exploring tourists’ stated preferences for heritage tourism services—the case of Tainan city, Taiwan. Tour Econ 18(2):457–464CrossRefGoogle Scholar
  4. Chen X, Kwan MP (2012) Choice set formation with multiple flexible activities under space–time constraints. Int J Geogr Inf Sci 26(5):941–961CrossRefGoogle Scholar
  5. Cranshaw J, Schwartz R, Hong JI, Sadeh N (2012) The livehoods project: utilizing social media to understand the dynamics of a cityGoogle Scholar
  6. Cullen IG (1972) Space, time and the disruption of behaviour in cities. Environ Plann A 4(4):459–470CrossRefGoogle Scholar
  7. Gao H et al (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systemsGoogle Scholar
  8. García-Palomares JC, Gutiérrez J, Mínguez C (2015) Identification of tourist hot spots based on social networks: a comparative analysis of European metropolises using photo-sharing services and GIS. Appl Geogr 63:408–417CrossRefGoogle Scholar
  9. Girardin F, Calabrese F, Dal Fiore F, Ratti C, Blat J (2008a) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4)Google Scholar
  10. Girardin F et al (2008b) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4):36–43CrossRefGoogle Scholar
  11. Han SY, Tsou MH, Clarke KC (2015) Do global cities enable global views? Using Twitter to quantify the level of geographical awareness of U.S. cities. PLoS ONE 10(7):e0132464.  https://doi.org/10.1371/journal.pone.0132464 CrossRefGoogle Scholar
  12. Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41(3):260–271CrossRefGoogle Scholar
  13. Issa E, Tsou MH, Nara A, Spitzberg B (2017) Understanding the spatio-temporal characteristics of Twitter data with geotagged and nongeotagged content: two case studies with the topic of flu and Ted (movie). Ann GIS 23:219–235CrossRefGoogle Scholar
  14. Kádár B (2014) Measuring tourist activities in cities using geotagged photography. Tour Geogr 16(1):88–104CrossRefGoogle Scholar
  15. Kennedy LS, Naaman M (2008) Generating diverse and representative image search results for landmarks. In: Proceedings of the 17th international conference on world wide web. ACM, pp 297–306Google Scholar
  16. Kisilevich S, Mansmann F, Keim D (2010) P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In: Proceedings of the 1st international conference and exhibition on computing for geospatial research and application. ACM, p 38Google Scholar
  17. Majid A et al (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. Int J Geogr Inf Sci 27(4):662–684CrossRefGoogle Scholar
  18. McKercher B, Shoval N, Ng E, Birenboim A (2012) First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tour Geogr 14(1):147–161CrossRefGoogle Scholar
  19. Okabe A, Satoh T, Sugihara K (2009) A kernel density estimation method for networks, its computational method and a GIS-based tool. Int J Geogr Inf Sci 23(1):7–32CrossRefGoogle Scholar
  20. Önder I, Koerbitz W, Hubmann-Haidvogel A (2016) Tracing tourists by their digital footprints: the case of Austria. J Travel Res 55(5):566–573CrossRefGoogle Scholar
  21. Palm R, Pred AR (1974) A time-geographic perspective on problems of inequality for women (No. 236). Institute of Urban & Regional Development, University of CaliforniaGoogle Scholar
  22. Popescu A, Grefenstette G (2011) Mining social media to create personalized recommendations for tourist visits. In: Proceedings of the 2nd international conference on computing for geospatial research and applicationsGoogle Scholar
  23. Sauer CO (1974) The fourth dimension of geography. Ann Assoc Am Geogr 64(2):189–192CrossRefGoogle Scholar
  24. Sun YY, Budruk M (2015) The moderating effect of nationality on crowding perception, its antecedents, and coping behaviours: a study of an urban heritage site in Taiwan. Curr Issues Tour 1–19Google Scholar
  25. Sun Y, Fan H (2014) Event identification from georeferenced images. In: Connecting a digital Europe through location and place. Springer International Publishing, pp 73–88Google Scholar
  26. Taaffe EJ (1974) The spatial view in context. Ann Assoc Am Geogr 64(1):1–16CrossRefGoogle Scholar
  27. Tan PN, Steinbach M, Kumar V (2005). Introduction to data mining, 1st ednGoogle Scholar
  28. Tsou MH (2015) Research challenges and opportunities in mapping social media and big data. Cartogr Geogr Inf Sci 42(sup1):70–74CrossRefGoogle Scholar
  29. Tsou MH, Kim IH, Wandersee S, Lusher D, An L, Spitzberg B, Gupta D, Gawron JM, Smith J, Yang JA, Han SY (2013a) Mapping ideas from cyberspace to realspace: visualizing the spatial context of keywords from web page search results. Int J Digit Earth 7:4.  https://doi.org/10.1080/17538947.2013.781240 Google Scholar
  30. Tsou MH, Yang JA, Lusher D, Han SY, Spitzberg B, Gawron JM, Gupta D, An L (2013b) Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election. Cartogr Geogr Inf Sci 40(4):337–348.  https://doi.org/10.1080/15230406.2013.799738 CrossRefGoogle Scholar
  31. Vu HQ, Li G, Law R, Ye BH (2015) Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tour Manag 46:222–232CrossRefGoogle Scholar
  32. Yuan M, Nara A (2015) Space-time analytics of tracks for the understanding of patterns of life. In: Space-time integration in geography and GIScience. Springer Netherlands, pp 373–398Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.The Center for Human Dynamics in the Mobile Age, Department of GeographySan Diego State UniversitySan DiegoUSA

Personalised recommendations