Reviewing Geotagging Research in Tourism

  • Elise WongEmail author
  • Rob Law
  • Gang Li
Conference paper


Advanced medium-sharing service and mobile technologies create a large volume of geotagged data online. The characteristics of geotagged data provide a new method for tourism and hospitality researchers to analyse tourist movement and behaviour. To extend knowledge on utilizing geotagged data in the tourism and hospitality industry, this study aims to review existing geotagging research in tourism and hospitality and thus identify a potential research topic in this area. Five research categories and future geotagging research topics in tourism and hospitality are identified and discussed.


Geotagged data Hospitality Tourism Literature review 



The work described in this paper was this font size seems to be tiny supported by a grant funded by the Research Grants Council of the Hong Kong Special Administrative Region, China (GRF Project Number: 15503814). The project was also funded by the Hong Kong Polytechnic University.


  1. Asakura, Y., & Iryo, T. (2007). Analysis of tourist behavior based on the tracking data collected using a mobile communication instrument. Transportation Research Part A, 41, 684–690.CrossRefGoogle Scholar
  2. Cao, L., Luo, J., Gallagher, A. C., Jin, X., Han, J., & Huang, T. S. (2010). A worldwide tourism recommendation system based on geotaggedweb photos. ICASSP, 2274–2277.Google Scholar
  3. Chareyron, G., Da-Rugan, J., & Raimbault, T. (2014). Big data: A new challenge for tourism. In International Conference on Big Data (pp. 5–7). IEEE.Google Scholar
  4. Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., et al. (2013). Beyond the geotag: Situating ‘big data’ and leveraging the potential of the geoweb. Cartography and Geographic Information Science, 40(2), 130–139.CrossRefGoogle Scholar
  5. Crandall, D. J., Backstrom, L., Huttenlocher, D. & Kleinberg, J. (2009). Mapping the world’s photos. In Proceedings of the 18th ACM International Conference on World Wide Web (pp. 761–770). ACM.Google Scholar
  6. Da Rugna, J., Chareyron, G., & Branchet, B. (2012). Tourist behavior analysis through geotagged photographies: A method to identify the country of origin. In Computational Intelligence and Informatics (CINTI), 13th International Symposium (pp. 347–351). IEEE.Google Scholar
  7. Dickinger, A., Scharl, A., Stern, H., Weichselbraun, A., & Wöber, K. (2008). Acquisition and Relevance of Geotagged Information in Tourism. In P. Oconnor., W. Hpken & U. Gretzel (Eds.), Proceedings of Information and Communication Technologies in Tourism 2008 (pp 545–555). Springer.Google Scholar
  8. Digital Stat Articles, (2016). By the numbers: 14 interesting Flickr Stats. Retrieved Jul 31, 2016 from
  9. Donaire, J. A., Camprubí, R., & Gali, N. (2014). Tourist clusters from Flickr travel photography. Tourism Management Perspectives, 11, 26–33.CrossRefGoogle Scholar
  10. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Data Mining and Knowledge Discovery, 226–231.Google Scholar
  11. Farzanyar, X., & Cercone, N. (2015). Trip pattern mining using large scale geo-tagged photos. In Proceedings of the International Conference on Computer and Information Science and Technology (p. 113). Ottawa, Canada.Google Scholar
  12. Forer, P., & Simmons, D. (2002). Serial experiences: Monitoring, modelling and visualising the free independent traveller in new zealand at multiple scales with GIS. In A. Arnberger, C. Brandenburg, & A. Muhar (Eds.), Monitoring and management of visitor flows in recreational and protected areas (pp. 173–180). Vienna: Institute of Landscape Architecture and Landscape Management, Bodenkultur University.Google Scholar
  13. Garcia-Palomares, J. C., Gultiérrez, J., & Minguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408–417.CrossRefGoogle Scholar
  14. Girardin, F., Calabrese, F., Dal Fiore, F., Ratti, C. & Blat, J. (2008a). Digital footprinting: Uncovering tourists with user-generated content. IEEE Pervasive computing, 7(4), 36–43.Google Scholar
  15. Girardin, F., Dal Fiore, F., Ratti, C., & Blat, J. (2008b). Leveraging explicitly disclosed location information to understand tourist dynamics: A case study. Journal of Location Based Services, 2(1), 41–56.CrossRefGoogle Scholar
  16. Guo, L., Li, Z., & Sun, W. (2015). Understanding travel destination from structured tourism blogs. In Proceedings of 2015 Wuhan International Conference on e-Business (pp. 144–151).Google Scholar
  17. Instagram. (2016a). Press news. Retrieved July 25, 2016 from
  18. Instagram. (2016b). Instagram today: 500 million windows to the world. Retrieved July 31, 2016, from
  19. Jiang, K., Wang, P., & Yu, N. (2011). ContextRank: Personalized tourism recommendation by exploiting context information of geotagged web photos. In Proceedings of IEEE16th International Conference on Image and Graphics (pp. 931–937). IEEE.Google Scholar
  20. Jiang, K., Yin, H., Wang, P., & Yu, N. (2013). Learning from contextual information of geo-tagged web photos to rank personalized tourism attractions. Neurocomputing, 119, 17–25.CrossRefGoogle Scholar
  21. Kádár, B., & Gede, M. (2013). Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography. Cartographica. The International Journal of Geographic Information and Geovisualization, 48(2), 78–88.Google Scholar
  22. Kalogerakis, E., Vesselova, O., Hays, J., Efros, A. A., & Hertzmann. A. (2009). Image sequence geolocation with human travel priors. In Proceedings of 12th IEEE International Conference on Computer Vision (ICCV) (pp. 253–360). IEEE.Google Scholar
  23. Kisilevich, S., Krstajic, M., Keim, D., Andrienko, N., & Andrienko, G. (2010a). Event-based analysis of people’s activities and behavior using Flickr and Panoramio geotagged photo collections. In Proceedings of 14th IEEE International Conference Information Visualization (pp. 289–296). IEEE.Google Scholar
  24. Kisilevich, S., Mansmann, F., & Keim, D. (2010b). P-DBSCAN: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application (pp. 38–41). Maryland, USA: Bethesda.Google Scholar
  25. Kurashima, T., Iwata, T., Irie, G., & Fujimura, K. (2013). Travel route recommendation using geotagged photos. Knowledge and Information Systems, 37(1), 37–60.CrossRefGoogle Scholar
  26. Lau, G., & McKercher, B. (2006). Understanding tourist movement patterns in a destination: A GIS approach. Tourism and Hospitality Research, 7(1), 39–49.CrossRefGoogle Scholar
  27. Law, R., Qi, S., & Buhalis, D. (2010). Progress in tourism management: A review of website evaluation in tourism research. Tourism Management, 31, 297–313.CrossRefGoogle Scholar
  28. Lee, I., Cai, G., & Lee, K. (2013). Mining points-of-interest association rules from geo-tagged photos. In System Sciences, 46th Hawaii International Conference (pp. 1580–1588). IEEE.Google Scholar
  29. Leung, R., Vu, H. Q., Rong, J., & Miao, Y. (2016). Tourists visit and photo sharing behavior analysis: A case study of hong kong temples. In A. Inversini & E. Schegg (Eds.), Proceedings of Information and Communication Technologies in Tourism 2016 (pp. 197–209).Google Scholar
  30. Lew, A., & McKercher, B. (2006). Modeling tourist movements: A local destination analysis. Annals of Tourism Research, 33(2), 403–423.CrossRefGoogle Scholar
  31. Li, X., Meng, F., & Uysal, M. (2008). Spatial pattern of tourist flows among the Asia-Pacific countries: An examination over a decade. Asia Pacific Journal of Tourism Research, 13(3), 229–243.CrossRefGoogle Scholar
  32. Majid, A., Chen, L., Chen, G., Mirza, H. T., Hussain, I., & Woodward, J. (2012). A context-aware personalized travel recommendation system based on geotagged social media data mining. International Journal of Geographical Information Science, 27(4), 662–684.CrossRefGoogle Scholar
  33. Mamei, M., Rosi, A., & Zambonelli, F. (2010). Automatic analysis of geotagged photos for intelligent tourist services. In Proceedings of IEEE 16th International Conference on Intelligent Environments (pp. 146–151). IEEE.Google Scholar
  34. McKercher, B., & Lau, G. (2008). Movement patterns of tourists within a destination. Tourism Geographies., 10(3), 355–374.CrossRefGoogle Scholar
  35. Musante, M., Bojanic, D., & Zhang, J. (2009). An evaluation of hotel website attribute utilization and effectiveness by hotel class. Journal of Vacation Marketing, 15(3), 203–215.CrossRefGoogle Scholar
  36. O’Neill, E., Kostakos, V., Kindberg, T., Penn, A., Fraser, D. S., & Jones, T. (2006). Instrumenting the city: Developing methods for observing and understanding the digital cityscape. Proceedings of international conference on ubiquitous computing (pp. 315–332). Berlin Heidelberg: Springer.Google Scholar
  37. Okuyama, K., & Yanai, K. (2013). A travel planning system based on travel trajectories extracted from a large number of geotagged photos on the web. The era of interactive media (pp. 657–670). New York: Springer..Google Scholar
  38. Önder, I., Koerbitz, W., & Hubmann-Haidvogel, A. (2014). Tracing tourist by their digital footprints: The case of Austria. Journal of Travel Research, 55(5), 566–573.CrossRefGoogle Scholar
  39. Shoval, N., McKercher, B., Ng, E., & Birenboim, A. (2011). Hotel location and tourist activity in cities. Annals of Tourism Research, 38(4), 1594–1612.CrossRefGoogle Scholar
  40. Sun, Y., Fan, H., Helbich, M., & Zipf, A. (2013). Analyzing human activities through volunteered geographic information: Using Flickr to analyze spatial and temporal pattern of tourist accommodation. Progress in location-based services (pp. 57–69). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  41. Vu, H. Q., Leung, R., Rong, J., & Miao, Y. (2016). Exploring park visitors’ activities in Hong Kong using geotagged photos. In A. Inversini & E. Schegg (Eds.), Proceedings of Information and Communication Technologies in Tourism 2016 (pp. 183–196).Google Scholar
  42. Vu, H. Q., Li, G., Law, R., & Ye, B. H. (2015). Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tourism Management, 46, 222–232.CrossRefGoogle Scholar
  43. Wu, C. L., & Carson, D. (2008). Spatial and temporal tourist dispersal analysis in multiple destination travel. Journal of Travel Research, 46(3), 311–317.CrossRefGoogle Scholar
  44. Xia, J. C., Zeephongsekul, P., & Arrowsmith, C. (2009). Modelling spatio-temporal movement of tourists using finite Markov chains. Mathematics and Computers In Simulation, 79, 1544–1553.CrossRefGoogle Scholar
  45. Xu, Z., Chen, L., & Chen, G. (2015). Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing, 155, 99–107.CrossRefGoogle Scholar
  46. Zanker, M., Fuchs, M., Seebacher, A., Jessenitschnig, M., & Stromberger, M. (2009). An automated approach for deriving semantic annotations of tourism products based on geospatial information. In W. Höpken et al. (Eds.), Processings of Information and Communication Technologies in Tourism 2009. (pp. 211–221).Google Scholar
  47. Zheng, Y. T., Zha, Z. J., & Chua, T. S. (2011). Research and applications on georeferenced multimedia: A survey. Multimedia Tools Appl, 51, 77–98.CrossRefGoogle Scholar
  48. Zheng, Y. T., Zha, Z. J., & Chua, T. S. (2012). Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology, 3(3), 56–73.CrossRefGoogle Scholar
  49. Zheng, Y., Zhang, L., Xie, X., & Man, W. Y. (2009). Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th ACM International Conference on World Wide Web. (pp. 791–800). ACM.Google Scholar
  50. Zipf, A., & Malaka, R. (2001). Developing location based services for tourism—The service providers view information and communication technologies in tourism. In P. J. Sheldon, K. W. Wöber & D. R. Fesenmaier (Eds.), Proceedings of Information and Communication Technologies in Tourism 2001 (pp. 83–92). Springer.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Hotel and Tourism ManagementThe Hong Kong Polytechnic UniversityHong KongChina
  2. 2.School of Information TechnologyDeakin UniversityBurwoodAustralia

Personalised recommendations