后疫情时期粤港澳大湾区“城市－郊野”旅游流网络结构特征——基于网络数字足迹的分析

为探究后疫情时期“城市―郊野”旅游流移动态势及旅游发展趋势，以粤港澳大湾区旅游网络为对象，搜集疫情发生前后各两年（2018年1月―2021年12月）的网络游记数据，借助社会网络分析法，对疫情前后粤港澳大湾区“城市―郊野”的旅游流分布状况及游客选择偏好进行比较。研究表明：1）疫情后大湾区整体旅游网络密度相对疫情前大幅度下降，特大城市尤其境外城市旅游受疫情影响更为明显；旅游网络联通性弱化严重，聚集效应变差，更趋向于分散和割裂。2）疫情后旅游网络“核心-边缘”结构弱化，核心区和边缘区的界限变得模糊和淡化，部分郊区、乡野景区在网络中的重要性明显增强，成为新的核心区。3）疫情后港珠澳及广州等传统核心城市节点在网络的联通性、控制力弱化，景点凝聚子群从疫情前呈现广州与佛山、珠海和澳门等珠江口西侧旅游城市群的高凝聚性，转变为疫情后多点化发展的趋势，郊野凝聚子群强化，旅游呈现郊野化趋势。4）景点网络从疫情前的广州―香港―澳门3核心的发展模式转变为疫情后的广州―澳门―深圳－佛山4核心的“四驱多动”关系，港珠澳大桥在网络中的联动性下降。流动性限制与旅游驱动力是后疫情期旅游结构变迁的双向动力，疫情后大湾区旅游流空间网络是在疫情作用下发生的“城市―郊野”双向动态变化的双环结构，可能会逐渐呈现去中心化的散点发展态势。最后，提出后疫情时期应结合乡村振兴发展郊野旅游，加强城市―郊野旅游合作、打造联动性旅游融合圈，加强境内外旅游合作、建立旅游营销数据库精准引导出游等对策建议。

The "City-Suburb" Tourist Flow Network Structure Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area in the Post-Pandemic Era: Analysis Based on the Network Digital Footprint

To explore the movement of "city-suburb" tourism flow in the post-pandemic period, this study examines the tourist flow network of the Guangdong-Hong Kong-Macao Greater Bay Area from 2018 to 2021 based on online travel data. After screening and deduplication, 4882 valid travelogues were chosen and divided into pre-pandemic data (3,967 articles) and post-pandemic data (915 articles) using November 2019 as the dividing line. A total of 4,461 attractions on Ctrip.com were selected to build a scenic spot database of the Guangdong-Hong Kong-Macao Greater Bay Area, including the full names, aliases (common names), and city names of scenic spots. After matching the travelogues with the scenic spots in the attraction database, it was found that 1848 attractions appeared in the travel notes, and the top 300 attractions were chosen for the generation of tourism routes according to the number of matches. After converting travel routes to a directed connectivity matrix and the following dichotomization procedure, a social network analysis (SNA) was conducted to investigate the distribution of tourism flows and preferences in the Greater Bay Area. Using the SNA software Ucinet 6.0, the network density, centrality, and relevant metrics of the structural holes and cohesive subgroups were calculated. The node characteristics and network structure were analyzed, and the distribution characteristics of attractions and tourist intention trends in the Greater Bay Area were obtained. The study results indicate the following: 1) The tourism network density of the Greater Bay Area has decreased substantially since the COVID-19 outbreak. Megacities, especially overseas cities, were more affected by the pandemic. Tourism network connectivity and aggregation effects were severely weakened, and the network structure was more scattered and fragmented. 2) After the pandemic, the "core-periphery" structure of tourism networks weakened, and the boundaries between core and periphery areas blurred. Some suburban and rural scenic spots have become new core areas and their importance in the network has been significantly enhanced. 3) After the pandemic, the connectivity and control power of traditional core urban nodes, such as Hong Kong, Zhuhai, Macao, and Guangzhou, weakened. The cohesive subgroups of scenic points show a high cohesion of Guangzhou with cities on the west side of the Pearl River Estuary, such as Foshan, Zhuhai, and Macao, before the pandemic. After the pandemic, the cohesive subgroup of rural attractions was strengthened and tourism showed a development trend of multi-point and ruralization. 5) The tourism network has changed from the three-core development mode of Guangzhou-Hong Kong-Macao before the pandemic to the "four-wheel drive" mode of Guangzhou-Macao-Shenzhen-Foshan after the outbreak with the declined linkage of the Hong Kong-Zhuhai-Macao Bridge in the network. It is believed that travel mobility restrictions and tourism drivers are two-way driving forces for the structural change in tourism in the post-pandemic period. The Greater Bay Area's tourist flow network presents a two-loop structure with two-way dynamics owing to some pandemic factors, and may gradually show a decentralized and scattered development trend. Based on network analysis, it is proposed that more efforts be made to integrate city-suburb-countryside resources in the post-pandemic period.