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Tourist Cross-Flows of the Museum Clusters

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 246))

Abstract

In this article, the cross-flow phenomenon is considered from the approach of the analysis by digital footprint method, based on geotagging. The goal of the study is to test the cross-logistic approach to characterize the tourist flow of the museum clusters, based on TripAdvisor data. The empirical database included 221 museums united into 36 museums clusters. During the research, hypotheses concerning the dependence between the logistic flow value indicators and locations of the museums in the cluster were verified by Spearman's rank correlation test. The identification of the hub system type, based on the logistic flow model of each museum cluster, was determined by the graph method in Gephi. As a result, it was found that the intensity of cross-flow does not depend on the proximity of museums in the museum cluster. However, the average distance between cluster museums make potential effect on the cross-flow intensity. Moreover, it has been proven that the museum cluster is based on the hub system in terms of managing the tourist flow. Four models were identified: two-node, triangle, single and mixed hub. Two-node model of the museum cluster, based on the line strong connection between 2 museums, has been identified as the most common logistic structure.

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References

  1. Mings, R.C., McHugh, K.E.: The spatial configuration of travel to Yellowstone National Park. J. Travel Res. 30(4), 38–46 (1992)

    Article  Google Scholar 

  2. Oppermann, M.: A model of travel itineraries. J. Travel Res. 33(4), 57–61 (1995)

    Article  Google Scholar 

  3. Zhang, H., Xu F., Lu, L., Yu. P.: The spatial agglomeration of museums, a case study in London. Journal of Heritage Tourism 12(2), 172–190 (2017). https://doi.org/10.1080/1743873X.2016.1167213

  4. Janes, R. R.: The mindful museum. Curator: The Museum Journal 53(3), 325–338 (2010). https://doi.org/10.1111/j.2151-6952.2010.00032.x

  5. Nikolić, M.: City of museums: Museum Cluster as a manifesto of the paradigm shift. In: 6th Conference of the International Forum on Urbanism (IFoU): TOURBANISM, Barcelona. (2012).

    Google Scholar 

  6. Fennell, D.A.: A tourist space-time budget in the Shetland Islands. Ann. Tour. Res. 23(4), 811–829 (1996). https://doi.org/10.1016/0160-7383(96)00008-4

    Article  Google Scholar 

  7. Wang, B., Manning, R.E.: Computer simulation modeling for recreation management: A study on carriage road use in Acadia National Park, Maine, USA. Environ. Manage. 23(2), 193–203 (1999). https://doi.org/10.1007/s002679900179

    Article  Google Scholar 

  8. Dumont, B., Roovers, P., Gulinck, H.: Estimation of off-track visits in a nature reserve: a case study in central Belgium. Landsc. Urban Plan. 71(2–4), 311–321 (2005). https://doi.org/10.1016/j.landurbplan.2004.03.010

    Article  Google Scholar 

  9. O’Connor, A., Zerger, A., Itami, B.: Geo-temporal tracking and analysis of tourist movement. Math. Comput. Simul. 69(1–2), 135–150 (2005). https://doi.org/10.1016/j.matcom.2005.02.036

    Article  MathSciNet  MATH  Google Scholar 

  10. Arrowsmith, C., Chhetri, P., Zanon, D.: Monitoring visitor patterns of use in natural tourist destinations. In: Ryan, C., Page, S.J., Aicken, M. (eds.) Taking Tourism to the Limits, ch.4, pp. 33–52 (2005). https://doi.org/10.1016/B978-0-08-044644-8.50007-2

  11. Haritaoglu, I., Harwood, D., Davis, L. S.: April. W/sup 4: Who? when? where? what? a real time system for detecting and tracking people. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 222–227. IEEE (1998). https://doi.org/10.1109/AFGR.1998.670952

  12. Hadley, D., Grenfell, R., Arrowsmith, C.: September. Deploying location-based services for nature-based tourism in non-urban environments. In: Spatial Sciences Coalition Conference, Canberra. (2003).

    Google Scholar 

  13. Loiterton, D., Bishop, I. D.: Virtual environments and location-based questioning for understanding visitor movement in urban parks and gardens. Real-time Visualisation and Participation, Dessau, Germany. (2005).

    Google Scholar 

  14. Zhong, L., Sun, S., Law, R.: Movement patterns of tourists. Tour. Manage. 75, 318–322 (2019). https://doi.org/10.1016/j.tourman.2019.05.015

    Article  Google Scholar 

  15. Kaufmann, M., Siegfried, P., Huck, L., Stettler, J.: Analysis of Tourism Hotspot Behaviour Based on Geolocated Travel Blog Data: The Case of Qyer. ISPRS Int. J. Geo Inf. 8(11), 493 (2019). https://doi.org/10.3390/ijgi8110493

    Article  Google Scholar 

  16. Zheng, Y.T., Zha, Z.J., Chua, T.S.: Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology (TIST) 3(3), 1–18 (2012). https://doi.org/10.1145/2168752.2168770

    Article  Google Scholar 

  17. Lee, J. Y., Tsou, M. H.: January. Mapping spatiotemporal tourist behaviors and hotspots through location-based photo-sharing service (Flickr) data. In: LBS 2018: 14th International Conference on Location Based Services. pp. 315–334. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71470-7_16

  18. Hu, F., Li, Z., Yang, C., Jiang, Y.: A graph-based approach to detecting tourist movement patterns using social media data. Cartogr. Geogr. Inf. Sci. 46(4), 368–382 (2019). https://doi.org/10.1080/15230406.2018.1496036

    Article  Google Scholar 

  19. Shoval, N., Isaacson, M.: Tracking tourists in the digital age. Ann. Tour. Res. 34(1), 141–159 (2007). https://doi.org/10.1016/j.annals.2006.07.007

    Article  Google Scholar 

  20. Lew, A., McKercher, B.: Modeling tourist movements: A local destination analysis. Ann. Tour. Res. 33(2), 403–423 (2006). https://doi.org/10.1016/j.annals.2005.12.002

    Article  Google Scholar 

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Acknowledgements

This article is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University).

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Correspondence to Anastasia Polomarchuk .

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Polomarchuk, A. (2022). Tourist Cross-Flows of the Museum Clusters. In: Beskopylny, A., Shamtsyan, M. (eds) XIV International Scientific Conference “INTERAGROMASH 2021". Lecture Notes in Networks and Systems, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-030-81619-3_57

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  • DOI: https://doi.org/10.1007/978-3-030-81619-3_57

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