A study on the evolution of economic patterns and urban network system in Guangdong-Hong Kong-Macao greater bay area - Tang, Bo; Chen, Zehui; Zhang, Yuanyuan; Sun, Hua (Frontiers in Public Health, 2022)
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.