MobiSeg: Interactive region segmentation using heterogeneous mobility data
Wu, Wenchao1; Zheng, Yixian1; Cao, Nan2; Zeng, Haipeng1; Ni, Bing1; Qu, Huamin1; Ni, Lionel M.3
Conference Name10th IEEE Pacific Visualization Symposium, PacificVis 2017
Source PublicationIEEE Pacific Visualization Symposium
Conference Date4 18, 2017 - 4 21, 2017
Conference PlaceSeoul, Korea, Republic of
Author of SourceIEEE Computer Society
AbstractWith 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. © 2017 IEEE.
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Hong Kong University of Science and Technology, Hong Kong;
2.Tongji University, China;
3.University of Macau, China
Recommended Citation
GB/T 7714
Wu, Wenchao,Zheng, Yixian,Cao, Nan,et al. MobiSeg: Interactive region segmentation using heterogeneous mobility data[C]//IEEE Computer Society,2017:91-100.
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