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

ABSTRACT

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. © 2022 Editorial Committee of Tropical Geography. All rights reserved.