Comparative Examination of Network Clustering Methods for Extracting Community Structures of a City From Public Transportation Smart Card Data

Human mobility data such as global positioning system (GPS) data from mobile phones and smart card data of public transportation have been used for the analysis of a city. Those studies have attempted to create networks whose nodes represent areas of the city and whose edges represent human flows between areas. Network clustering methods are applied to those networks for extracting community structures of cities. Although many studies have attempted to extract community structures of cities using human mobility data and network clustering methods, little has been explored about which method is the most effective, in terms of the accuracy and the amount of information of extracted clusters. In this paper, we propose evaluation metrics for evaluating network clustering methods based on the geographical cohesiveness, the regularity, and the amount of information of extracted clusters. We use smart card data of public transportation collected in Japan for testing the effectiveness of each method. We compare two types of origin-destination matrices for constructing networks and four types of network clustering methods including two types of bipartite network clustering methods. The results confirmed that a bipartite network clustering method considering multi-facet characteristics of each cluster and the functional relations between areas of a city shows the most accurate and richest information about community structures of cities.