Identifying city communities in China by fusing multisource flow data

Figures & data

Figure 1. Study Area.

Figure 2. The distribution of intercity (a) population flows, (b) traffic flows, (c) information flows, and (d) fusion flows.

Figure 3. Diamond structure formed by the flows.

Figure 4. Silhouette coefficient of each step.

Figure 5. The results of city community detection based on the fusion flow.

Table 1. Correlation between population flow, traffic flow, information flow, and fusion flow.

	Population flow	Traffic flow	Information flow	Fusion flow
Population flow	1	0.612*	0.309*	0.801*
Traffic flow	0.612*	1	0.384*	0.817*
Information flow	0.309*	0.384*	1	0.746*
Fusion flow	0.801*	0.817	0.746*	1

Figure 6. The results of city community detection based on population flow.

Figure 7. The results of city community detection based on traffic flow.

Figure 8. The results of city community detection based on information flow.

Figure 9. The results of city community detection based on (a) Louvain algorithm, (b) spectral clustering algorithm.

Data availability statement

The data that support the findings of this study are openly available in Figshare (https://figshare.com/articles/dataset/Multisource_flow_data/22598074; DOI: 10.6084/m9.figshare.22598074).