Skip to main content

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

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-71470-7_16
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-71470-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Hardcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  • 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, Cambridge

    Google Scholar 

  • Birenboim A (2016) New approaches to the study of tourist experiences in time and space. Tour Geogr 18(1):9–17

    CrossRef  Google Scholar 

  • 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):457–464

    CrossRef  Google Scholar 

  • Chen X, Kwan MP (2012) Choice set formation with multiple flexible activities under space–time constraints. Int J Geogr Inf Sci 26(5):941–961

    CrossRef  Google Scholar 

  • Cranshaw J, Schwartz R, Hong JI, Sadeh N (2012) The livehoods project: utilizing social media to understand the dynamics of a city

    Google Scholar 

  • Cullen IG (1972) Space, time and the disruption of behaviour in cities. Environ Plann A 4(4):459–470

    CrossRef  Google Scholar 

  • 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 systems

    Google Scholar 

  • 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 63:408–417

    CrossRef  Google Scholar 

  • 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 

  • Girardin F et al (2008b) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4):36–43

    CrossRef  Google Scholar 

  • 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

    CrossRef  Google Scholar 

  • 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):260–271

    CrossRef  Google Scholar 

  • 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 23:219–235

    CrossRef  Google Scholar 

  • Kádár B (2014) Measuring tourist activities in cities using geotagged photography. Tour Geogr 16(1):88–104

    CrossRef  Google Scholar 

  • 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 297–306

    Google Scholar 

  • 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 38

    Google Scholar 

  • 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):662–684

    CrossRef  Google Scholar 

  • 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):147–161

    CrossRef  Google Scholar 

  • 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):7–32

    CrossRef  Google Scholar 

  • Önder I, Koerbitz W, Hubmann-Haidvogel A (2016) Tracing tourists by their digital footprints: the case of Austria. J Travel Res 55(5):566–573

    CrossRef  Google Scholar 

  • Palm R, Pred AR (1974) A time-geographic perspective on problems of inequality for women (No. 236). Institute of Urban & Regional Development, University of California

    Google Scholar 

  • 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 applications

    Google Scholar 

  • Sauer CO (1974) The fourth dimension of geography. Ann Assoc Am Geogr 64(2):189–192

    CrossRef  Google Scholar 

  • 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 1–19

    Google Scholar 

  • Sun Y, Fan H (2014) Event identification from georeferenced images. In: Connecting a digital Europe through location and place. Springer International Publishing, pp 73–88

    Google Scholar 

  • Taaffe EJ (1974) The spatial view in context. Ann Assoc Am Geogr 64(1):1–16

    CrossRef  Google Scholar 

  • Tan PN, Steinbach M, Kumar V (2005). Introduction to data mining, 1st edn

    Google Scholar 

  • Tsou MH (2015) Research challenges and opportunities in mapping social media and big data. Cartogr Geogr Inf Sci 42(sup1):70–74

    CrossRef  Google Scholar 

  • 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 

  • 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

    CrossRef  Google Scholar 

  • Vu HQ, Li G, Law R, Ye BH (2015) Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tour Manag 46:222–232

    CrossRef  Google Scholar 

  • 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 373–398

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming-Hsiang Tsou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lee, J.Y., Tsou, MH. (2018). Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) Data. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_16

Download citation