# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 1 Introduction
Previous research on spatial structures relied heavily on traditional data collection methods and analytical tools, utilizing statistical techniques to study spatial differentiation in population and industry, qualitative methods for describing functional zoning and socio-economic relations, and government statistical data based on administrative divisions, which often inadequately measured interregional interactions. The emergence of geographic information systems enhances the analysis of regional spatial structures, facilitating a shift from static to dynamic research and from local to holistic perspectives. Furthermore, the rapid development of geospatial big data allows for a deeper understanding of the dynamics within regional spatial structures. The integration of flow space theory and geospatial big data offers a novel methodology for studying regional spatial structures. Flow space theory highlights the dynamic nature of linkages and factor flows, while geospatial big data provides robust, multidimensional data support. However, this approach faces limitations, such as fragmented research and inadequate exploration of the interconnected characteristics of regional spatial structures. There is a particular need for a deeper focus on transportation patterns and the interactions between economic linkages and the coordinated development of regions. Amid global economic integration and regionally coordinated development strategies, the Greater Bay Area has evolved into a world-class urban agglomeration characterized by increasingly integrated inter-city transportation systems and economic linkages, exhibiting prominent network attributes. Yet, critical questions remain: Are the region's transportation and economic networks both stable and complementary? Do logistics and capital flows exhibit a synergistic evolutionary dynamic? How are these flows spatially distributed, and how intense are they? This study seeks to address these issues through a systematic examination of the structural and synergistic characteristics of the Greater Bay Area's transportation-economic network within the framework of flow space, applying complex network analysis methodologies. The study proposes the following research hypotheses:  
**Hypothesis 1 (H1). **: _The logistics and capital flow networks within the Greater Bay Area exhibit a polycentric hierarchical configuration, anchored by Hong Kong, Shenzhen, and Guangzhou. However, notable disparities exist in the centrality levels and spheres of influence between the two networks._  
**Hypothesis 2 (H2). **: _There is a significant positive correlation between urban transportation accessibility and capital connectivity within the capital flow network. Higher accessibility correlates with enhanced capital absorption capacity._  
**Hypothesis 3 (H3). **: _With the progression of regional integration, spatial coupling between the logistics and capital flow networks in the Greater Bay Area intensifies. This is reflected in increasing synergy across node distribution, connection strength, and evolutionary trajectories._  
The study employs flow space theory to construct a city economic linkage network based on multidimensional economic indicators, emphasizing transportation and economic connections. It further develops a city logistics and capital flow linkage network utilizing liner schedules and enterprise investment data. Through complex network analysis methods, the research examines the network structure of cities in the Greater Bay Area and their synergy mechanisms. This research aims to elucidate the spatial positioning, dynamic interactions, and economic flow patterns among Bay Area cities, thereby promoting regional economic integration and optimizing the cooperative development model. Investigating the relationship between transportation and the economy in the Greater Bay Area underscores the vital role of transportation networks in facilitating cooperation and resource integration.