首页|旅游流网络结构韧性特征变化及其影响因素——以贵州省为例

旅游流网络结构韧性特征变化及其影响因素——以贵州省为例

扫码查看
原文链接
  • 万方数据
文章通过网络爬虫获取贵州省2010-2022年旅游流数据，构建旅游流网络及其结构韧性的综合评估及攻击指标体系，并借助复杂网络、指数随机图等方法，实证探讨了贵州省旅游流网络结构韧性的变化特征及其影响因素。结果表明:①贵州省旅游流网络结构韧性呈现先下降后上升的态势，旅游流网络联系的增强并未带来网络结构韧性的提升。韧性水平较高的地区多为东南部的旅游中心城市;②分维度来看，贵州省旅游流网络形成了较为典型的"核心—边缘"层级结构，网络的匹配性明显，传输性在不断提升，小团体集聚现象开始凸显;③旅游流网络结构在随机攻击下呈现出鲁棒性特征，而在蓄意攻击下脆弱性较为显著，且在蓄意攻击下网络承受能力随时间推移整体呈现上升趋势，旅游流核心网络不断拓展;④网络内生结构、个体属性以及外生网络变量共同影响着旅游流网络结构韧性变化。其中，网络互惠性、经济水平、产业结构、政府支持与接待能力对旅游流网络结构韧性变化具有显著正向影响，市场规模对旅游流网络结构韧性变化的正向作用不断凸显，而网络边数与空间距离对旅游流网络结构韧性变化具有明显的抑制作用。
Change of Structural Resilience Characteristics of Tourism Flow Network and Its Influencing Factors:A Case Study of Guizhou Province
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.

tourism flownetwork structural resiliencesimulation of attacktravelogue textmarket scaleindustrial structurerisk prevention

王坤、陈祥泰、黄震方

展开 >

贵州大学 旅游与文化产业学院,中国 贵州 贵阳 550025

南京师范大学 地理科学学院,中国 江苏 南京 210023

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

国家自然科学基金项目国家自然科学基金项目教育部人文社会科学研究青年项目贵州省教育厅高校人文社会科学研究项目

419610254207117519YJC8500042023GZGXRW068

2024

10.15957/j.cnki.jjdl.2024.03.023

经济地理
中国地理学会 湖南省经济地理研究所

经济地理

CSTPCDCSSCICHSSCD北大核心
影响因子：2.575
ISSN：1000-8462
年,卷(期)：2024.44(3)