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.
ABSTRACT
The COVID-19 pandemic has seriously affected China's macroeconomy, industrial transformation, and high-quality development. Research on economic patterns and urban network systems can provide a reference for healthy development of the regional economic system. The evolution of the economic pattern and urban network system of Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2010 to 2020 is investigated using methods (e.g., the gravity center model, the gravitational force model, social network analysis, and geographic information system). (1) The gravity center of gross domestic product (GDP) of the GBA is located in Nansha district, Guangzhou, with a skewing direction northwest-east-northwest and a movement rate of "large-small-large." The center of import and export and the center of consumption show a "zigzagging migration" in which the center of investment shows an "irregular (random) migration". (2) The economic connection degree of cities in the GBA exhibits a high ascending velocity, and the whole area tends to be mature, with a significant effect of spatial proximity. With the steady increase in network density, there is significant polarization of network centrality in the region. The four major cohesive subgroups have been relatively stable and consistent with the degree of geographic proximity of the cities. The center-periphery structure is more significant, in which the core area is extended to the cities on the east coast of the Pearl River Estuary, thus forming the core cluster of "Hong Kong-Shenzhen-Guangzhou-Dongguan." In this study, the evolution of economic patterns and urban network systems in the GBA over the past decade is analyzed using multiple methods (i.e., gravity model, urban network system analysis, and geographic information system) based on urban socioeconomic data by starting from various spatial elements (e.g., "points, lines, and networks") to gain insights into and optimize research on regional economic development after the COVID-19 pandemic.
Subject(s)
COVID-19 , Humans , Hong Kong/epidemiology , Macau , COVID-19/epidemiology , Pandemics , CitiesABSTRACT
Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA's economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis;(2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems;(3) network resilience presented an obvious core-edge structure, and the spatial correlation and spillover effect between blocks were substantial;and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries.
ABSTRACT
In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.