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.

Highlights

  • Regional integration forms a network space that is based on relationships and driven by national power

  • Based on different spatial scales, this paper studies the spatial structure of the tourism economy

  • This paper studies the 28 member states of the regional EU, including Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom) from 1995 to 2018, calculates the tourism economic connection based on the modified gravity model, constructs an EU tourism economic spatial network, and analyzes its structural characteristics and effects with the help of the social network method

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Summary

Introduction

Regional integration forms a network space that is based on relationships and driven by national power. EU policies aimed to enhance the European single market and support industrial competitiveness, sustainable innovation and entrepreneurship This inevitably led to the emergence of new policies in the field of tourism and promoted the development of regional tourism economic networks in the EU. The theoretical value and practical significance of this paper are reflected in the following two areas: (1) analyzing the tourismrelated network across countries and exploring its laws at the social network level and (2) analyzing the characteristics and evolution process of the tourism economic network of EU member states, measuring the effect of the tourism economic network structure on EU tourism economic development, clarifying the positioning and role of each member state, and making recommendations to promote regional tourism cooperation and improve the specialization level of the tourism industry.

Literature Review
Research on Spatial Structure Studies of European Tourism
Research on the Network Structure of Scenic Spots in Tourism Destination
The Modified Tourism Gravity Model
Complete Network Characteristic Index
Ego Network Characteristic Index
Core-Periphery Model
Study Area
Data Source
Analysis of Tourism Economic Connection Strength
Construction of Cyberspace Structure
Network Density
Network Relevance
Network
Characteristics of the Ego Network
Betweenness Centrality
Closeness Centrality
Core Periphery Analysis
EU Accession
Effect Analysis of the Complete Network Structure
Effect of the Complete Network Structure on Tourism Industry Specialization
Effect Analysis of the Ego Network Structure
Findings
Conclusions
Full Text
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