## 5 Clustering Results  
In this section, we conduct experiments using proposed clustering refinement framework on SFD data. The input settings of Algorithm 1 are described as follows: (1) SFD-out and SFD-in data; (2) K-means, ProClus and HDDC clustering algorithms; (3) Optimal \(K\)s for SFD-out data are \(K=2,3,4,5\), as suggested in Figures 2 and 6; similarly, optimal \(K\) for SFD-in data is determined as \(K=2,3,9,3,3\) by WSS, Silhouette, Gap statistics, Calinski criterion respectively; (4) Maximum number of iterations as 100. The agglomeration property drawn from out-station passenger flow reflects human mobility destination, and the clustering in the light of in-station passenger flow reveals where people come from. According to execution results of Algorithm 1, Figure 8 shows final clustering results over both out-station and in-station passenger flows, from which five clusters of stations are detected. Basically, the final cluster labels are generated by consolidating the overlaps between in-station and out-station clusterings, while the inconsistent parts contribute to outliers. For the convenience of narration, we highlight each cluster from Figure 8a-e.  
Now we explore the implications of clustering results in Figure 8 with reference to government policy and regulatory documents ([[http://www.sz.gov.cn/cn/xxgk/zfxxgj/ghih/](http://www.sz.gov.cn/cn/xxgk/zfxxgj/ghih/)]([http://www.sz.gov.cn/cn/xxgk/zfxxgj/ghih/](http://www.sz.gov.cn/cn/xxgk/zfxxgj/ghih/))), such as \"Shenzhen City Master Plan\", \"Shenzhen City Land Utilization Master Plan\" and \"Urban Construction and Land Use in Shenzhen for the Thirteenth Five-year Plan\". **Cluster 1**: Strategic development core area--highlighted as blue in Figure 8a. Specifically, stations in Cluster 1 are located in: (1) Baoan central area for high-tech industry operations, research and development headquarters, business services, and cultural and creative industries, such as Baohua (Baohua Station) and Xingdong (Xingdong Station); (2) high-tech zone north district for emerging and future industries, such as Shenda (Shenzhen University Station) and Gaoxinyuan (Hi-Tech Park Station); (3) city government resident areas, such as Shiminzhongxin (Civic Center Station) and Huizhanzhongxin (Convention & Exhibition Center Station). **Cluster 2**: Suburb area and transportation junctions--highlighted as orange in Figure 8b. Generally, Cluster 2 is distributed in suburb area, such as teleneurons of each line, and interfaces to other transit modes (i.e., airport, train and checkpoint), such as Shenzhenbeizhan (Shenzhen North Station), Luohu (Luohu Station) and Futiankouan (Futian Checkpoint Station). However, exceptions include: (1) Metro Airport Area, i.e., Jichangdong (Airport East Station), Houri (Hourui Station) and Gushu (Gushu Station); (2) Qianhai Shekou Free Trade Zone, including Chiwan (Chiwan Station), Shekougang (Shekou Port Station) and Haishangshijie (Sea World Station); (3) Ban Xue Gang Tech area, such as Longhua (Longhua Station) and Longsheng (Longsheng Station); (4) Shenzhen airport Longgang terminal, i.e., Ailian (Ailian Station). **Cluster 3**: Technology and industrial development area--highlighted as green in Figure 8c. Specifically, regions around Cluster 3 are: (1) Ban Xue Gang Tech area for new generation of information industry such as high-end software and intelligent terminal, such as Longsheng (Longsheng Station) and Shangtang (Shangtang Station); (2) Shenzhen North Station business center area for commercial business, e-commerce and service industry, such as Minzhi (Minzhi Station) and Baishilong (Baishilong  
Figure 8: Map-based visualization of station clusters. The marks are labeled by station names, shape shows details about administrative region, and color of stations shows final cluster labels produced by proposed method. The maps are plotted using Tableau Desktop. Station); (3) Luohu Sungang-Qingshuihe district, primarily for commercial commerce, cultural and creative industries, such as Caopu (Caopu Station).