Multi-scale cross-city community detection of urban agglomeration using signaling big data

Abstract

Many existing efforts have taken advantage of large-scale spatial-temporal data to partition cities via constructed human interaction networks. However, few studies focus on communities emerging between adjacent cities in big urban agglomerations, which we call “cross-city” communities. In this study, we introduce a novel framework to detect cross-city communities in urban agglomerations under different scales leveraging a large number of fine-grained mobile signaling data aiming to break the original administrative boundaries. Taking the Pearl River Delta (PRD) urban agglomeration in China as study area, we investigate the existence of potential communities at three scales, i.e. city-group level, city level and sub-city level. The partition results are expected to benefit transportation planning, urban zoning and administrative boundary re-delineation. The results from our study highlight the necessity of considering cross-city communities and their scale effects when examining urban spatial interactions.

Keywords

• Cross-city communities
• community detection
• mobile big data
• human interaction network
• scale effect