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Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network

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  • Haijian Li

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

  • Wujie Xie

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

Abstract

The paper uses the revised gravity model to measure the intensity of tourism investment spatial correlation, constructs the spatial correlation matrix of tourism investment, and uses the social network method to analyze the structural characteristics and evolutionary process of tourism investment spatial correlation network based on 31 provinces in China from 2000 to 2016. The findings revealed: (1) The spatial correlation quantity of interprovincial tourism investment continues to grow, with Beijing, Jiangsu, Zhejiang, Shanghai, Shandong, and Guangdong at the top of the list. (2) Overall network density and correlation are rising, and the spatial correlation of interprovincial tourism investment is increasingly close. Network hierarchy and network efficiency are decreasing, and network stability has been enhanced. (3) Degree centrality and closeness centrality of each province have shown a significant increase; Beijing, Shandong, Guangdong, Jiangsu, Zhejiang, and Shanghai are the top six and in the center of the network. Most provinces have improved betweenness centrality, Beijing, Guangdong, Shandong, Liaoning, Shaanxi, and Hunan have a strong betweenness centrality with strong intermediary capacity. (4) The core area mainly includes eastern and central provinces, and the periphery areas mainly include western and northeastern provinces. The network connection density of the core and periphery areas shows an increasing trend, while the network linkage density between the core and periphery areas shows a downward trend.

Suggested Citation

  • Haijian Li & Wujie Xie, 2022. "Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15661-:d:983636
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    1. Piervito Bianchi & Giulio Mario Cappelletti & Elisabetta Mafrolla & Edgardo Sica & Roberta Sisto, 2020. "Accessible Tourism in Natural Park Areas: A Social Network Analysis to Discard Barriers and Provide Information for People with Disabilities," Sustainability, MDPI, vol. 12(23), pages 1-14, November.
    2. Li, Hengyun & Chen, Jason Li & Li, Gang & Goh, Carey, 2016. "Tourism and regional income inequality: Evidence from China," Annals of Tourism Research, Elsevier, vol. 58(C), pages 81-99.
    3. Hwayoon Seok & George A. Barnett & Yoonjae Nam, 2021. "A social network analysis of international tourism flow," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 419-439, April.
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