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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
GADM: https://gadm.org/index.html - 386,735 administrative areas (country, region, department, district, city and town).
- 2.
Neo4j 4, GDS: https://neo4j.com/docs/graph-data-science/1.2/.
- 3.
MAE: Mean Absolute Error, MSE: Mean Squared Error, MAPE: Mean Absolute Percentage Error.
References
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)
Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)
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
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)
Cooper, C., Hall, C.M.: Contemporary Tourism. Routledge, Abingdon (2007)
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
Endriss, U., Grandi, U.: Graph aggregation. Artif. Intell. 245, 86–114 (2017)
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)
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)
Graham, R.L., Hell, P.: On the history of the minimum spanning tree problem. Ann. Hist. Comput. 7(1), 43–57 (1985)
Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4–6), 175–181 (2000)
Huan, J., Wang, W., Prins, J., Yang, J.: Spin: mining maximal frequent subgraphs from graph databases. In: ACM SIGKDD 2004, pp. 581–586 (2004)
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
Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)
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)
Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Math. 1(3), 335–380 (2004)
March, R., Woodside, A.G.: Tourism Behaviour: Travellers’ Decisions and Actions. Cabi, Wallingford (2005)
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)
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)
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
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)
Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: CNSR 2004, pp. 305–314, May 2004
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
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)