## 3 Methodology and Data
### Study Area and Data Sources  
This study focuses on seven national-level urban agglomerations in China, encompassing 142 cities and their district attributes (GDP, population, and area), as shown in Table 3. These urban agglomerations received formal government approval in 2018, reflecting their status as China's most strategically significant economic zones. These urban agglomerations demonstrate distinct spatial structural and developmental characteristics, as shown in Figure 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Urban** & **Constituent Cities** & **Number of Cities** & **GDP (Billion Yuan)** & **Population (Million Person)** & **Area (km2)** \\ \hline Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) & Beijing, Tianjin, Baoding, Tangshan, Qinhuangdao, Handan, Xingtai, Zhangjiakou, Chengde, Cangzhou, Hengshui, Dingzhou, Xinji, Anyang & 16 & 867.05 & 108.96 & 218,000 \\ \hline Yangtze River Delta Urban Agglomeration (YRDUA) & Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Shaoxing, Wenzhou, Huzhou, Jiaxing, Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Ma’anshan, Tongling, Anqing, Xuancheng, Chizhou, Chuzhou & 27 & 1973.49 & 132.63 & 211,700 \\ \hline Guangdong–Hong Kong–Macao Greater Bay Area (GBA) & Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, Zhongshan, Jiangmen, Huizhou, Zhaoqing, Hong Kong, Macao & 11 & 869.00 & 37.77 & 55,900 \\ \hline Chengdu–Chongqing Urban Agglomeration (CCUA) & Chengdu, Chongqing, Zigong, Luzhou, Deyang, Suining, Neijiang, Leshan, Nanchong, Meishan, Yibin, Guang’an, Ziyang, Mianyang, Dazhou, Ya’an & 16 & 650.60 & 101.11 & 185,000 \\ \hline Middle Reaches of Yangtze River Urban Agglomeration (MRYRUA) & Wuhan, Huangshi, Ezhou, Huanggang, Xiaogan, Xianning, Xiantao, Qianjiang, Tianmen, Xiangyang, Yichang, Jingzhou, Jingmen, Changsha, Zhuzhou, Xiangtan, Yueyang, Yiyang, Changde, Hengyang, Loudi, Nanchang, Jiujiang, Jingdezhen, Yintan, Xinyu, Yichun, Pingxiang, Shangrao, Fuzhou, Ji’an & 31 & 915.01 & 131.42 & 326,100 \\ \hline Central Plains Urban Agglomeration (CPUA) & Zhengzhou, Luoyang, Kaifeng, Nanyang, Anyang, Shangqiu, Xinxiang, Pingdingshan, Xuchang, Jiaozuo, Zhoukou, Xinyang, Zhumadian, Hebi, Puyang, Luohe, Sanmenxia, Jiyuan, Changzhi, Jincheng, Yuncheng, Xingtai, Handan, Liaocheng, Heze, Suzhou, Huaibei, Bengbu, Fuyang, Bozhou & 30 & 785.19 & 190.26 & 287,000 \\ \hline Guanzhong Plain Urban Agglomeration (GPUA) & Xi’an, Baoji, Xianyang, Tongchuan, Weinan, Shangluo, Yuncheng, Linfen, Tianshui, Pingliang, Qingyang & 11 & 216.09 & 44.85 & 107,100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of seven national-level urban agglomerations in China in 2018. Based on the defined scope of urban agglomerations and their constituent cities, this study selected data from the year before and after this policy (2017-2019) as our study period. This timeframe ensures consistency in both data availability and resolution across all analyzed datasets. During this timeframe, four distinct types of intercity relationship data were collected and analyzed: information flow, investment ties, and transportation infrastructure (high-speed rail operation frequency and highway networks). To ensure our analysis captures genuine intercity relationships, this study excluded administrative and governmental connections from our network construction. This enabled the study to focus on the connectivity between cities through information, economic, and infrastructural flows, rather than relationships defined by administrative boundaries or policy frameworks. The specific data sources for each urban network are detailed below:  
(1) Baidu index data: The average search index between cities from the Baidu Index during the period from January 2017 to December 2019 is used to represent the informational relationships between cities, serving as the weight for the information network relationships. These data are used to construct a directed and weighted information network.