Hierarchical community detection and functional area identification with OSM roads and complex graph theory

ABSTRACT

An in-depth analysis of the urban road network structure plays an essential role in understanding the distribution of urban functional area. To concentrate topologically densely connected road segments, communities of urban roads provide a new perspective to study the structure of the network. In this study, based on OpenStreetMap (OSM) roads and points-of-interest (POI) data, we employ the Infomap community detection algorithm to identify the hierarchical community in city roads and explore the shaping role roads play in urban space and their relation with the distribution of urban functional areas. The results demonstrate that the distribution of communities at different levels in Guangzhou, China reflects the urban spatial relation between the suburbs and urban centers and within urban centers. Moreover, the study explored the functional area characteristics at the community scale and identified the distribution of various functional areas. Owing to the structure information contained in the identification process, the detected community can be used as a basic unit in other urban studies. In general, with the community-based network, this study proposes a novel method of combining city roads with urban space and functional zones, providing necessary data support and academic guidance for government and urban planners.

KEYWORDS:

• Network science
• geospatial big data
• road network
• urban functional area
• community detection

Acknowledgments

The authors thank the editor and anonymous reviewers for their constructive comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key R&D Program of China [2017YFB0503800]; the National Natural Science Foundation of China [Grant No. 41801306, 41671408];Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University [Grant No. 18S01] and Natural Science Foundation of HubeiProvince [2017CFA041].

Notes on contributors

Ye Hong

Ye Hong is a master student at Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Switzerland. His research interest is geospatial big data analysis and applications.

Yao Yao

Yao Yao is an Associate Professor at the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China. At the same time, he worked as a visiting scholar and senior algorithm engineer at Alibaba Group's data center. His main research interests comprise multi-source geospatial big data mining, machine learning applications and fine-scale simulation of urban land-use dynamic changes.