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Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach

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Abstract

Advancements of information, communication and location-aware technologies have made collections of various passively generated datasets possible. These datasets provide new opportunities to understand human mobility patterns at a low cost and large scale. This study presents a home-based approach to understanding human mobility patterns based on a large mobile phone location dataset from Shenzhen, China. First, we estimate each individual’s “home” anchor point, and a modified standard distance (\(S_{D}^{\prime }\)) is proposed to measure the spread of each individual’s activity space centered at this “home” anchor point. We then derive aggregate mobility patterns at mobile phone tower level to describe the distance distribution of \(S_{D}^{\prime }\) for people who share the same “home” anchor point. A hierarchical clustering algorithm is performed and the spatial distributions of the derived clusters are analyzed to highlight areas with similar aggregate human mobility patterns. The results suggest that 43 % of the population sample travelled within a short distance (\(S_{D}^{\prime } \le 1 \;{\text{km}}\)) during the 13-day study period while 23.9 % of them were associated with a large activity space (\(S_{D}^{\prime } \ge 5 \;{\text{km}}\)). The geographical differences of people’s mobility patterns in Shenzhen are evident. Areas with a large proportion of people who have a small activity space mainly locate in the northern part of Shenzhen such as Baoan and Longgang districts. In the southern part where the economy is highly developed, the percentage of people with a larger activity space is higher in general. The findings could offer useful implications on policy and decision making. The proposed approach can also be used in other studies involving similar spatiotemporal datasets for travel behavior and policy analysis.

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Notes

  1. There have been a number of publications (Phithakkitnukoon et al. 2010; Song et al. 2010; Yuan et al 2012; Yuan and Raubal 2012; Becker et al. 2013) that used mobile phone location datasets for studying human mobility patterns. The mobile phone location dataset used in this study was acquired through research collaboration with Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences and the research was approved through an Institutional Review Board (IRB) process.

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Acknowledgments

This study was jointly supported by National Natural Science Foundation of China (41231171), National Key Technology R&D Program of China (#2012AA12A403-4), Arts and Sciences Excellence Professorship and Alvin and Sally Beaman Professorship at the University of Tennessee, Excellent Talents Funds at Wuhan University, Shenzhen Scientific Research and Development Funding Program (ZDSY20121019111146499, JSGG20121026111056204), and Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (JCYJ20121019111128765).

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Authors and Affiliations

  1. Department of Geography, University of Tennessee, Knoxville, TN, 37996, USA

    Yang Xu, Shih-Lung Shaw & Ziliang Zhao

  2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, People’s Republic of China

    Shih-Lung Shaw & Zhixiang Fang

  3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People’s Republic of China

    Ling Yin

  4. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Qingquan Li

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Xu, Y., Shaw, SL., Zhao, Z. et al. Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach. Transportation 42, 625–646 (2015). https://doi.org/10.1007/s11116-015-9597-y

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