Acta Geographica Sinica ›› 2022, Vol. 77 ›› Issue (8): 2034-2049.doi: 10.11821/dlxb202208013

• Economic Geography and Regional Development • Previous Articles     Next Articles

Spatio-temporal pattern and influencing factors of cooperation network of China's inbound tourism cities

LIN Zhihui(), CHEN Ying(), LIU Xianfeng, MA Yaofeng   

  1. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
  • Received:2021-04-28 Revised:2021-12-31 Online:2022-08-25 Published:2022-10-12
  • Contact: CHEN Ying;
  • Supported by:
    National Natural Science Foundation of China(42171095);National Natural Science Foundation of China(41801124);Natural Science Foundation of Shaanxi Province(2020JQ-417);Social Science Foundation of Shaanxi Province(2020D039)


The high-quality development of inbound tourism is an important foundation for the high-quality development of China's tourism industry, and its inbound tourism city cooperation network has become a key support to achieving this goal. However, our current knowledge of the spatio-temporal characteristics of China's urban cooperation network of inbound tourism remains limited. This paper used economic development data of inbound tourism in 60 cities of China from 1997 to 2017, the revised gravity model, and social network analysis method. It first examines the intensity of China's inbound tourist city cooperation, and then systematically analyzes the spatiotemporal patterns of these networks. Finally, it applies the second assignment method to discuss the possible driving forces of these networks. The results show that: (1) These networks across the 60 cities have gradually increased in intensity. Simultaneously, a national-level network of tourism cooperation has emerged. The top five pairs of city groups participating in tourism cooperation are Guangzhou-Shenzhen, Beijing-Shanghai, Beijing-Tianjin, Shanghai-Suzhou, and Shanghai-Shenzhen. (2) With regard to overall network characteristics, network density is gradually increasing, overall network accessibility is improving, city "leader" status is declining, and the overall network balance is strengthening. There are significant differences between China's three major parts (eastern, central, and western regions): cities in the eastern region have the strongest cooperation, followed by cities in the central and western regions. (3) In terms of individual network characteristics, China's inbound tourist cities are divided into four categories: extroverted, cohesive, balanced, and isolated. Their power roles demonstrate a "core-periphery" pattern, showing the characteristics of core leaders, sub-core leaders, general collaborators, and marginal collaborators. (4) Geographical proximity, differences in tourism resources and reception capabilities, city-scale levels, and similarities in tourism-traffic conditions have a significant effect on the improvement of the network. Differences in foreign economic engagement and trade have a positive effect on urban-tourism cooperation in the central and western regions. However, these differences have a negative impact on eastern networks and the whole country. Our findings unravel the changes in China's inbound tourist city cooperation networks, and provide important reference for the optimization of the network.

Key words: inbound tourism, city cooperation network, spatio-temporal pattern, driving forces, social network, China