Based on the tourism flow data from 2010 to 2022 in Guizhou Province obtained through web crawler,this paper constructs the tourism flow network,its structural resilience evaluation and the simulation index system using the methods of the complex networks and the exponential random graph models(ERGM),and explores the evolution process of the resilience of tourism flow network structure(RTFNS)and its influencing factors.The results show that:1)The RTFNS in Guizhou Province initially decreased and then increased during the studied period.The strengthening of the tourism flow network connection did not necessarily improve the resilience of the tourism flow network structure.Regions with high resilience level were mainly tourism center cities with well-developed tourism and attractive core tourist attractions.2)In terms of dimensions,the tourism flow network in Guizhou Province has formed a typical"core-edge"hierarchical structure,with obvious matching of the network,increasing transmission.The phenomenon of small-group agglomeration has become prominent.3)The tourism flow network structure demonstrated robustness characteristics against random attacks.Under intentional attacks,the vulnerability is more significant.However,under intentional attacks,the network's tolerance displayed an overall upward trend over time,and the core network continued to expand.4)The endogenous structure of the network,the individual attributes,and the exogenous network variables all have a significant impact on the RTFNS.The reciprocity of network structure,the economic level,the industrial structure,the government support and the reception capacity have a significant positive impact on the RTFNS.The positive effect of market size on the RTFNS is increasingly prominent,while the number of network edges and spatial distance have a significant inhibitory impact on the RTFNS.
关键词
旅游流/网络结构韧性/攻击模拟/游记文本/市场规模/产业结构/风险防范
Key words
tourism flow/network structural resilience/simulation of attack/travelogue text/market scale/industrial structure/risk prevention
万方数据