Skip to main content
SpringerLink
Log in

Indicators for Measuring Tourist Mobility

  • Conference paper
  • First Online:
Web Information Systems Engineering – WISE 2020 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12342))

Included in the following conference series:

  • 1341 Accesses

Abstract

Digital traces left by active users on social networks have become a popular means of analyzing tourist behavior. The large amount of data generated by tourists provides a key indicator for understanding their behavior according to various criteria. Analyses of tourists’ movement have a crucial role in tourism marketing to build decision-making tools for tourist offices. Those actors are faced with the need to discern tourists’ circulation both quantitatively and qualitatively. In this paper, we propose a measure to capture tourist mobility on various areas which relies on a flow network of data from TripAdvisor into a Neo4j graph database. Thanks to this representation, we produce aggregated graphs at various scales and apply deep tourists’ analysis. One centrality aspect of graphs is used to propose a key indicator of tourists mobility.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    GADM: https://gadm.org/index.html - 386,735 administrative areas (country, region, department, district, city and town).

  2. 2.

    Neo4j 4, GDS: https://neo4j.com/docs/graph-data-science/1.2/.

  3. 3.

    MAE: Mean Absolute Error, MSE: Mean Squared Error, MAPE: Mean Absolute Percentage Error.

References

  1. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  2. Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)

    Article  MathSciNet  Google Scholar 

  3. Chareyron, G., Quelhas, U., Travers, N.: Tourism analysis on graphs with Neo4Tourism. In: U, L.H., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds.) WISE 2020. CCIS, vol. 1155, pp. 37–44. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3281-8_4

    Chapter  Google Scholar 

  4. Chua, A., Servillo, L., Marcheggiani, E., Moere, A.V.: Mapping Cilento: using geotagged social media data to characterize tourist flows in southern Italy. Tour. Manag. 57, 295–310 (2016)

    Article  Google Scholar 

  5. Cooper, C., Hall, C.M.: Contemporary Tourism. Routledge, Abingdon (2007)

    Google Scholar 

  6. Das, K., Samanta, S., Pal, M.: Study on centrality measures in social networks: a survey. Soc. Netw. Anal. Min. 8(1), 13 (2018). https://doi.org/10.1007/s13278-018-0493-2

    Article  Google Scholar 

  7. Endriss, U., Grandi, U.: Graph aggregation. Artif. Intell. 245, 86–114 (2017)

    Article  MathSciNet  Google Scholar 

  8. Freeman, L.C., Borgatti, S.P., White, D.R.: Centrality in valued graphs: a measure of betweenness based on network flow. Soc. Netw. 13(2), 141–154 (1991)

    Article  MathSciNet  Google Scholar 

  9. Gössling, S., Scott, D., Hall, C.M.: Global trends in length of stay: implications for destination management and climate change. J. Sustain. Tour. 26(12), 2087–2101 (2018)

    Article  Google Scholar 

  10. Graham, R.L., Hell, P.: On the history of the minimum spanning tree problem. Ann. Hist. Comput. 7(1), 43–57 (1985)

    Article  MathSciNet  Google Scholar 

  11. Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4–6), 175–181 (2000)

    Article  MathSciNet  Google Scholar 

  12. Huan, J., Wang, W., Prins, J., Yang, J.: Spin: mining maximal frequent subgraphs from graph databases. In: ACM SIGKDD 2004, pp. 581–586 (2004)

    Google Scholar 

  13. Jacquot, S., Chareyron, G., Cousin, S.: Le tourisme de mémoire au prisme du “Big Data". Cartographier les circulations touristiques pour observer les pratiques mémorielles. Mondes du Tourisme, June 2018

    Google Scholar 

  14. Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)

    Article  Google Scholar 

  15. Keng, S.S., Su, C.H., Yu, G.L., Fang, F.C.: AK tourism: a property graph ontology-based tourism recommender system. In: KMICe, pp. 83–88. UUM (2018)

    Google Scholar 

  16. Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Math. 1(3), 335–380 (2004)

    Article  MathSciNet  Google Scholar 

  17. March, R., Woodside, A.G.: Tourism Behaviour: Travellers’ Decisions and Actions. Cabi, Wallingford (2005)

    Book  Google Scholar 

  18. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 1–11 (2007)

    Article  Google Scholar 

  19. Spyrou, E., Korakakis, M., Charalampidis, V., Psallas, A., Mylonas, P.: A geo-clustering approach for the detection of areas-of-interest and their underlying semantics. Algorithms 10(1), 35 (2017)

    Article  MathSciNet  Google Scholar 

  20. Sun, Y., Fan, H., Helbich, M., Zipf, A.: Analyzing human activities through volunteered geographic information: using Flickr to analyze spatial and temporal pattern of tourist accommodation. In: Krisp, J. (ed.) Progress in Location-Based Services. LNGC, pp. 57–69. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-34203-5_4

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  22. Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: CNSR 2004, pp. 305–314, May 2004

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Leonard de Vinci, Research Center, 92 916, Paris La Défense, France

    Sonia Djebali, Nicolas Loas & Nicolas Travers

Authors
  1. Sonia Djebali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Loas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicolas Travers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sonia Djebali , Nicolas Loas or Nicolas Travers .

Editor information

Editors and Affiliations

  1. VU Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  2. VU Amsterdam, Amsterdam, The Netherlands

    Wouter Beek

  3. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Melbourne, VIC, Australia

    Yanchun Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Djebali, S., Loas, N., Travers, N. (2020). Indicators for Measuring Tourist Mobility. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62005-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62004-2

  • Online ISBN: 978-3-030-62005-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation