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Mapping time series into networks as a tool to assess the complex dynamics of tourism systems

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  • Baggio, Rodolfo
  • Sainaghi, Ruggero

Abstract

This paper contributes to filling two gaps: i) the presence of a limited amount of studies focused on tourism demand turning points, ii) the prevalent recourse to linear models in demand analysis, disregarding the complex structure of tourism destinations. The paper uses the Horizontal Visibility Graph Algorithm, a technique able to transform a time series of observations into a network whose topology preserves some fundamental characteristics of the system examined. The empirical work focuses on Livigno, an Italian alpine destination.

Suggested Citation

  • Baggio, Rodolfo & Sainaghi, Ruggero, 2016. "Mapping time series into networks as a tool to assess the complex dynamics of tourism systems," Tourism Management, Elsevier, vol. 54(C), pages 23-33.
    • Handle: RePEc:eee:touman:v:54:y:2016:i:c:p:23-33
    DOI: 10.1016/j.tourman.2015.10.008
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    1. Andriana S L O Campanharo & M Irmak Sirer & R Dean Malmgren & Fernando M Ramos & Luís A Nunes Amaral, 2011. "Duality between Time Series and Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-13, August.
    2. Bertuglia, Cristoforo Sergio & Vaio, Franco, 2005. "Nonlinearity, Chaos, and Complexity: The Dynamics of Natural and Social Systems," OUP Catalogue, Oxford University Press, number 9780198567912.
    3. Joe S. Bain, 1942. "Market Classifications in Modern Price Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 56(4), pages 560-574.
    4. Egon Smeral, 2007. "Research Note: World Tourism Forecasting – Keep It Quick, Simple and Dirty," Tourism Economics, , vol. 13(2), pages 309-317, June.
    5. George F. R. Ellis, 2005. "Physics, complexity and causality," Nature, Nature, vol. 435(7043), pages 743-743, June.
    6. Henry Mintzberg & James A. Waters, 1985. "Of strategies, deliberate and emergent," Strategic Management Journal, Wiley Blackwell, vol. 6(3), pages 257-272, July.
    7. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    8. Barros, Carlos Pestana & Butler, Richard & Correia, Antónia, 2010. "The length of stay of golf tourism: A survival analysis," Tourism Management, Elsevier, vol. 31(1), pages 13-21.
    9. Wang, Na & Li, Dong & Wang, Qiwen, 2012. "Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6543-6555.
    10. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    11. Martín Gómez Ravetti & Laura C Carpi & Bruna Amin Gonçalves & Alejandro C Frery & Osvaldo A Rosso, 2014. "Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-15, September.
    12. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    13. Woodside, Arch G. & Hsu, Shih-Yun & Marshall, Roger, 2011. "General theory of cultures' consequences on international tourism behavior," Journal of Business Research, Elsevier, vol. 64(8), pages 785-799, August.
    14. Tang, Jinjun & Wang, Yinhai & Liu, Fang, 2013. "Characterizing traffic time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4192-4201.
    15. Crouch, Geoffrey I., 1996. "Demand elasticities in international marketing : A meta-analytical application to tourism," Journal of Business Research, Elsevier, vol. 36(2), pages 117-136, June.
    16. Francesco Pizzitutti & Carlos F. Mena & Stephen J. Walsh, 2014. "Modelling Tourism in the Galapagos Islands: An Agent-Based Model Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-14.
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    7. Olga Chkalova & Marina Efremova & Vladimir Lezhnin & Anna Polukhina & Marina Sheresheva, 2019. "Innovative mechanism for local tourism system management: a case study," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(4), pages 2052-2067, June.
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    9. Hernández, Juan M. & Kirilenko, Andrei P. & Stepchenkova, Svetlana, 2018. "Network approach to tourist segmentation via user generated content," Annals of Tourism Research, Elsevier, vol. 73(C), pages 35-47.
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    12. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    13. Jamshid Ardalankia & Jafar Askari & Somaye Sheykhali & Emmanuel Haven & G. Reza Jafari, 2020. "Mapping Coupled Time-series Onto Complex Network," Papers 2004.13536, arXiv.org, revised Aug 2020.
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