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Issue No. 01 - Jan. (2016 vol. 22)
ISSN: 1077-2626
pp: 270-279
Siming Chen , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Xiaoru Yuan , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Zhenhuang Wang , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Cong Guo , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Jie Liang , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Zuchao Wang , Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University
Xiaolong Luke Zhang , College of Information Sciences and Technology, Pennsylvania State University
Jiawan Zhang , School of Computer Science and Technology, and School of Computer Software, Tianjin University
ABSTRACT
Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.
INDEX TERMS
Media, Semantics, Uncertainty, Transportation, Reliability, Data models, Data mining
CITATION

S. Chen et al., "Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 22, no. 1, pp. 270-279, 2016.
doi:10.1109/TVCG.2015.2467619
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