## 2 Community Detection across Space and Its Variation across Time
### Community Detection across Space
#### 2.2.2 Algorithm
In order to extract the aggregation patterns of land uses, the method then uses the community detection techniques based on the volume of traffic flows and structural properties of the network. Furthermore, our method employs a graph structure matching measure to uncover the regularities of the transport system across time. The proposed method is also adaptive to multi-scale applications in space and time. Through the case study, we found that the interactions of land uses show different characteristics in different time periods, and the aggregation patterns of functional areas is dynamic across the time. This result is highly associated with the travel behaviors of residents in the city, and thus can be used further in social science research. In addition, the result can provide references for the dispatching of the traffic system. For example, we can plan for the prevention of traffic jams in regions which have intense interactions of traffic flows. Moreover, it can be used to assist urban structure analysis. As presented in our case study, Beijing has a polycentric form with significant loop structure. In this paper, we took the taxi trajectory data into consideration because taxi accounts for a large proportion of public transport in Beijing city. Taxi drivers are very familiar with the city of concern, and thus there are increasingly more studies focusing on the use of taxi trajectory data for urban analysis [3; 12; 33]. In addition, since the taxi is a common mode of transport, our method could be adaptable to other cities. We would like to regard this research as a beginning of detecting spatial interaction communities based on vehicle datasets. With the rapid development of big data, more traffic data (e.g., bus trajectories, passenger car data, and biking trajectories) can be introduced into our framework for exploring city structures comprehensively. Further study can also use more methods (e.g., complex system) to understand the mechanisms of the traffic flow space. It would be interesting to analyze the associations between the physical space and virtual space of a city using social media data. Methodology Development & Implementation, Data Acquisition, and Analysis: [PERSON]; M.G. ; Z.C. ; Writing & Revision: led by [PERSON] with contributions from all other co-authors. The project was supported by the National Natural Science Foundation of China (No. 41701440, 41871305), the Natural Science Foundation of Hubei Province (No. 2018 CFB513), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170640), and a grant from State Key Laboratory of Resources and Environmental Information System. The authors declare no conflict of interest. ## References

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