A social network analysis of international tourism flow

Seok, Hwayoon; Barnett, George A.; Nam, Yoonjae

doi:10.1007/s11135-020-01011-8

Cited by 37 publications

(26 citation statements)

References 43 publications

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“…The module can analyze different roles in the spatial network, depict the internal structure of urban resilience relational network, and find out the relationship between different plates and the linkage mode. First, using the method of CONCOR blocks the city in the network (Seok et al, 2021). Second, we should determine each block as 1-block or 0-block.…”

Section: Analysis Of Spatial Network Modulementioning

confidence: 99%

Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

2021

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Based on 2012-2017 panel data of 282 China's cities, this paper uses the entropy method to calculate an urban resilience index, uses spatial cold-hot spots model to explore spatial characteristics of urban resilience, uses revised the gravity model to construct urban resilience spatial network characteristics, and uses the social network analysis method to analyze spatial network characteristics of urban resilience. The results show that: (1) Urban resilience of China's cities has been gradually improved, and there is a geographical aggregation effect, with significant changes in hot spots and insignificant changes in cold spots. (2) Urban resilience has obvious spatial correlation and linkage effects and strong temporal fluctuation. The cities with higher degree centrality and closeness centrality are consistent in spatial distribution, mostly located in Bohai Rim, Pan-Yangtze River Delta, Pearl River Delta and some central regions. The centrality obviously shows non-equilibrium in spatial distribution. The cities with high centrality are mostly provincial capitals. (3) The "club effect" has not yet been

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Section: Analysis Of Spatial Network Modulementioning

confidence: 99%

Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

2021

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“…QAP is a quantitative analysis of the relationships between matrices. Given that, tourism flow is a kind of network relational data, using the traditional regression analysis method for the study of its influencing factors encounters the data multicollinearity problem, whereas the QAP method has been proved to be better able to avoid this problem [32,33].…”

Section: Qap Regression Analysismentioning

confidence: 99%

Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective

Wang

Chen

et al. 2022

Sustainability

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This study’s purpose was to analyze the network structural characteristics, nodal roles, spatial structure, and evolution laws from the dual network perspective, and apply the Quadratic Assignment Procedure (QAP) to conduct correlation analysis and regression analysis on the influencing factors of tourism flow networks in the Yangtze River Delta urban agglomerations. Using a mixed-method of social network and spatial analysis, Ucinet and ArcGIS software were used to comprehensively analyze the nodes in the travel routes covered in travel notes. The results show that the density of tourism flow network increases on the whole, while the spatial difference decreases, and the overall network density value is much lower than the average of the network density of provinces. Degree centrality, closeness centrality, betweenness centrality and the core–periphery structure analysis were used to examine the tourism function, distribution function, connection function and the position of nodes in the network, and nodes were divided into various types of roles according to their function. Meanwhile, the role changes of each node in different periods were also investigated. This study also builds an evaluation model of the influencing factors of the evolution of tourism flow network structure and uses QAP to find that the tourism network is affected by factors such as tourism resource endowment, transportation convenience, economic development level, tourism reception and service capacity. The research results are helpful for the Chinese government and tourism enterprises to understand the spatial behavior of tourists and its evolution rules, and to clarify the role and status of node cities in the tourism flow network and their influencing factors. It is of great significance for the formulation of joint marketing measures and promotion of the sustainable development of tourism in the Yangtze River Delta urban agglomeration.

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“…The global tourism network is a comprehensive system to show how tourists move among countries and explain the various phenomena in international tourism [58]. A larger volume of flow connecting between two countries in the network always demonstrates a closer relationship, which may be affected by population, economy, cultural context, international policies, environment, transportation, employees in tourism, travel services, and others [59,60].…”

Section: The Global Tourism Networkmentioning

confidence: 99%

Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

Zhu

Liu

2022

Applied Sciences

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Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions’ statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson’s correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node’s characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.

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A social network analysis of international tourism flow

Cited by 37 publications

References 43 publications

Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China

Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective

Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

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