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Research on the Spatial Structure of the European Union’s Tourism Economy and Its Effects

Author

Listed:
  • Wujie Xie

    (College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China
    School of History Culture and Tourism, Jiangsu Normal University, Xuzhou 221116, China)

  • Haijian Li

    (School of History Culture and Tourism, Jiangsu Normal University, Xuzhou 221116, China)

  • Yufang Yin

    (College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China
    Tourism College, Shanghai Normal University, Shanghai 201418, China)

Abstract

With the implementation of European integration policies such as the single market, the Euro and the Schengen Visa, the EU member states are developing closer economic ties through tourism, and their level of tourism integration is constantly improving. Taking the 28 EU member states as research objects, this paper constructs a tourism economic connection network among them, measures the strength of their tourism economic connections from 1995 to 2018 by using the modified gravity model and social network method, and analyzes the spatial structure characteristics and effects of the EU tourism economy. The results are as follows: (1) The tourism economic ties of EU member states are growing increasingly close, enhancing network stability. (2) Germany, France, Italy, Austria and the United Kingdom are the top five countries in the degree centrality and closeness centrality rankings, meaning that they are located in the center of the network and have great influence, and the network is becoming increasingly concentrated. Germany, Italy, Sweden, Austria and France play an important intermediary role in the network, and the centrality of most member states has increased. (3) The core areas are mainly concentrated in Western Europe, Southern Europe, Mediterranean mainland countries and Central Europe, while the marginal areas are mainly concentrated in Eastern Europe, Northern Europe and Mediterranean island countries; the network connection density of the core area, the network connection density of the marginal area, and the network connection density between the core and marginal area overall show an increasing trend. (4) Improvements in the complete network connectedness and a reduction in graph efficiency can significantly reduce differences in EU tourism economic development levels and improve spatial equity.

Suggested Citation

  • Wujie Xie & Haijian Li & Yufang Yin, 2021. "Research on the Spatial Structure of the European Union’s Tourism Economy and Its Effects," IJERPH, MDPI, vol. 18(4), pages 1-33, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1389-:d:492260
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    References listed on IDEAS

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    2. Irene Daskalopoulou & Anastasia Petrou, 2009. "Urban Tourism Competitiveness: Networks and the Regional Asset Base," Urban Studies, Urban Studies Journal Limited, vol. 46(4), pages 779-801, April.
    3. Yuan, Fong-Ching, 2020. "Intelligent forecasting of inbound tourist arrivals by social networking analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    4. Kádár, Bálint & Gede, Mátyás, 2021. "Tourism flows in large-scale destination systems," Annals of Tourism Research, Elsevier, vol. 87(C).
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