APRIL 18-21, 2017
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MobiSeg: Interactive Region Segmentation Using Heterogeneous Mobility Data

  • Wenchao Wu
    Hong Kong University of Science and Technology
  • Yixian Zheng
    Hong Kong University of Science and Technology
  • Nan Cao
    Tongji University
  • Haipeng Zeng
    Hong Kong University of Science and Technology
  • Bing Ni
    Hong Kong University of Science and Technology
  • Huamin Qu
    Hong Kong University of Science and Technology
  • Lionel M. Ni
    University of Macau


With the acceleration of urbanization and modern civilization, more and more complex regions are formed in urban area. Although understanding these regions could provide huge insights to facilitate valuable applications for urban planning and business intelligence, few methods have been developed to effectively capture the rapid transformation of urban regions. In recent years, the widely applied location-acquisition technologies offer a more effective way to capture the dynamics of a city through analyzing people's movement activities based on mobility data. However, several challenges exist, including data sparsity and difficulties in result understanding and validation. To tackle these challenges, in this paper, we propose MobiSeg, an interactive visual analytics system, which supports the exploration of people's movement activities to segment the urban area into regions sharing similar activity patterns. A joint analysis is conducted on three types of heterogeneous mobility data (i.e., taxi trajectories, metro passenger RFID card data, and telco data), which can complement each other and provide a full picture of people's activities in a region. In addition, advanced analytical algorithms (e.g., non-negative matrix factorization (NMF) based method to capture latent activity patterns, as well as metric learning to calibrate and supervise the underlying analysis) and novel visualization designs are integrated into our system to provide a comprehensive solution to region segmentation in urban areas. We demonstrate the effectiveness of our system via case studies with real-world datasets and qualitative interviews with domain experts.