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Analysis of the network structure characteristics and influencing factors of regional tourism economy

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  • Xiao-Bing Feng

Abstract

The tourism economic network is a crucial factor influencing regional tourism interrelations and economic behaviors. This study selects Sichuan Province, China, as the research area and employs social network analysis to evaluate the spatial network structure of its tourism economy from 2015 to 2022. Additionally, the Quadratic Assignment Procedure (QAP) analysis method is used to explain the influencing factors of the tourism economic spatial network structure. The results show that: (1) The volume of regional tourism economic connections is on a rapid growth trajectory, with the radiation capacity and spillover effects of core node areas continuously strengthening. (2) The density of spatial networks in regional tourism economics remains low, showing a trend from loose to tight connections, essentially forming a spatial distribution pattern of “one axis with multiple branches” in tourism economic connections. (3) There are differences in the centrality of tourism economic connection networks among various cities and prefectures within the region, with differing statuses and roles in the tourism economic spatial network. (4) Geographic proximity, tourism resource endowment, economic development levels, and tourism reception capacity positively promote inter-regional tourism economic connections, while the shortest road distance negatively impacts regional tourism economic connections.

Suggested Citation

  • Xiao-Bing Feng, 2025. "Analysis of the network structure characteristics and influencing factors of regional tourism economy," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-26, February.
    • Handle: RePEc:plo:pone00:0318243
    DOI: 10.1371/journal.pone.0318243
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    References listed on IDEAS

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    1. Park, Sangwon & Xu, Yang & Jiang, Liu & Chen, Zhelin & Huang, Shuyi, 2020. "Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data," Annals of Tourism Research, Elsevier, vol. 84(C).
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