中国入境旅游城市合作网络时空格局及驱动因素

基于1997—2017年中国60个城市入境旅游经济发展数据,辅以修正的引力模型和社会网络分析方法,系统分析了中国入境旅游城市合作网络时空格局,并借助二次指派程序方法对合作网络演变的驱动因素进行探讨。结果表明：① 中国60个入境旅游城市合作网络强度逐渐增强,国家层面合作网络结构逐渐显现,合作强度前5名的分别为广州—深圳、北京—上海、北京—天津、上海—苏州和上海—深圳。② 整体网络特征上,网络密度逐渐增加,整体网络通达度在提升,城市“领导者”地位在下降,网络整体均衡性在提升,且中国东、中、西三大地区间差异显著,东部地区城市合作最紧密,西部地区城市次之,中部地区城市紧密度最小。③ 个体网络特征上,将中国入境旅游城市分为外向型、内聚型、均衡发展型和孤立型4类,且权力角色上呈核心领导者、次核心领导者、一般合作者和边缘合作者的“核心—边缘”分布格局。④ 地理空间邻近性、旅游资源差异、旅游接待能力差异、城市规模等级和旅游交通情况的相近性对网络提升具有显著影响,对外经济贸易差异对中、西部地区城市旅游合作的影响是正向的,而对整体网络和东部网络的影响是负向的。

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

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.