打印

作者

Jinzhou Cao;Qingquan Li;Wei Tu;Qili Gao;Rui Cao;Chen Zhong

来源

CITIES,Vol.110,Issue1,Article 103077

语言

英文

关键字

Spatial network;Urban mobility;Mobile phone tracking data;Complex Network analysis

作者单位

Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing & Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China;Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;Department of Geography, King's College London, London WC2R 2LS, UK;Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing & Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China;Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;Department of Geography, King's College London, London WC2R 2LS, UK

摘要

Human movements and interactions with cities are characterized by urban mobility networks. Many studies that address urban mobility are inspired by complex networks. The models of complex networks require a large amount of empirical data. However, current works relied on traditional survey data and were unable to take full advantage of the capabilities offered by complex networks; thus, the possibility of quantifying urban mobility networks by considering individual travel patterns has not yet been addressed. This study presents a data-driven approach for characterizing urban mobility networks based on massive-scale mobile phone tracking data. Individual travel motifs are first extracted using a graph-based approach. The global urban mobility network (G-UMN) and the motif-dependent urban mobility subnetworks (MD-UMNs) are then constructed. Next, network properties, including statistical measures and scaling relations between the basic measures, are proposed for characterizing mobility networks. We have conducted experiments focusing on Shenzhen, China. The results demonstrated that (1) the individual travel motifs are structurally and spatially heterogeneous, (2) the G-UMN exhibits a evolutionary hierarchical structure, and (3) the MD-UMNs show many behavioral differences in their spatial and topological properties, reflecting the impacts of the heterogeneity of the individual travel motifs. These results bridge the gap between complex network properties and urban mobility patterns and provide crucial implications and policies for data-informed urban planning.