# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### Synergistic Development Characteristics of Logistics and Capital Flows  
#### 4.4.1 Spatial Fluidity of Logistics and Capital Flows  
The intercity linkages within the Greater Bay Area are categorized into three tiers--strong, sub-strong, and weak--based on the directionality and intensity of liner shipping and enterprise investment flows. A detailed analysis of the logistics and capital flow networks (see Figures 4 and 5) reveals their structural attributes and spatial distribution patterns, thereby enabling the identification of interaction dynamics among cities across different network levels. The strong linkage network denotes the most intensive core-node relationships, forming the backbone of economic, trade, and logistics activities within the region. Sub-strong linkages represent substantial intercity connections--rooted in strong relational pathways--but with relatively lower proximity and interaction strength. By contrast, weak linkages reflect sparse, low-frequency logistics flows, indicative of marginal investment and transportation activity within the Greater Bay Area. The logistics and capital flow networks across the region reveal a multi-layered hierarchical structure, exposing disparities in cities' functional roles, resource concentration, and industrial synergy. This stratification further illustrates the organizational mechanisms and polycentric nature of the Greater Bay Area's flow networks. The strong linkage network constitutes the structural backbone of the Greater Bay Area's regional economy. Within the logistics network, Guangzhou, Shenzhen, and Hong Kong form a core contact cluster characterized by high-frequency, high-density, and high-intensity interactions. These intercity connections underpin the region's logistical framework. Specifically, the connection intensity from Hong Kong to Shenzhen reached 31.7%, 26.01%, and 46.2% across the selected years, indicating a consistent upward trend and highlighting Hong Kong's expanding hub role in cross-border logistics. In the capital flow network, the linkage weights between Hong Kong and Shenzhen were 12.6%, 11.6%, \begin{table}
\begin{tabular}{c c c c c} \hline
**2016** & \multicolumn{3}{c}{**2019**} & \multicolumn{2}{c}{**2021**} \\ \hline
**Flow Direction** & **Flow Intensity** & **Flow Direction** & **Flow Direction** & **Flow Intensity** & **Flow Direction** \\ \hline (Hong Kong\(\rightarrow\) Guangzhou) & 14.885\% & (Shenzhen \(\rightarrow\) Guangzhou) & 15.794\% & (Shenzhen \(\rightarrow\) Guangzhou) & 15.657\% \\ (Shenzhen\(\rightarrow\) Guangzhou) & 13.947\% & (Hong Kong \(\rightarrow\) Guangzhou) & 13.802\% & (Hong Kong \(\rightarrow\) Guangzhou) & 14.825\% \\ (Guangzhou\(\rightarrow\) Shenzhen) & 6.754\% & (Guangzhou \(\rightarrow\) Shenzhen) & 6.798\% & (Guangzhou \(\rightarrow\) Shenzhen) & 6.740\% \\ (Foshan \(\rightarrow\) Guangzhou) & 4.034\% & (Foshan \(\rightarrow\) Guangzhou) & 3.533\% & (Zhuhu \(\rightarrow\) Guangzhou) & 3.508\% \\ (Zhuhu\(\rightarrow\) Guangzhou) & 3.522\% & (Zhuhu \(\rightarrow\) Guangzhou) & 3.266\% & (Foshan \(\rightarrow\) Guangzhou) & 2.714\% \\ (Dongguan \(\rightarrow\) Guangzhou) & 2.767\% & (Dongguan \(\rightarrow\) Guangzhou) & 2.424\% & (Dongguan \(\rightarrow\) Guangzhou) & 2.023\% \\ (Macao \(\rightarrow\) Hong Kong) & 1.971\% & (Macao \(\rightarrow\) Hong Kong) & 1.828\% & (Macao \(\rightarrow\) Hong Kong) & 1.359\% \\ (Huizhou \(\rightarrow\) Shenzhen) & 0.979\% & (Huizhou \(\rightarrow\) Shenzhen) & 1.109\% & (Huizhou \(\rightarrow\) Shenzhen) & 1.100\% \\ (Zhongshan \(\rightarrow\) Guangzhou) & 0.813\% & (Zhongshan \(\rightarrow\) Guangzhou) & 0.801\% & (Zhongshan \(\rightarrow\) Guangzhou) & 0.860\% \\ (Jiangmen \(\rightarrow\) Guangzhou) & 0.435\% & (Jiangmen \(\rightarrow\) Guangzhou) & 0.493\% & (Jiangmen \(\rightarrow\) Guangzhou) & 0.489\% \\ (Zhaoqing \(\rightarrow\) Guangzhou) & 0.229\% & (Zhaoqing \(\rightarrow\) Guangzhou) & 0.226\% & (Zhaoqing \(\rightarrow\) Guangzhou) & 0.243\% \\ \hline \end{tabular}
\end{table} 
Table 4: City dominance flows in the capital flows network.