Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data

Siming Chen, Xiaoru Yuan, Zhenhuang Wang, Cong Guo, Jie Liang, Zuchao Wang, Xiaolong Luke Zhang, Jiawan Zhang

Research output: Contribution to journalArticlepeer-review

106 Citations (SciVal)


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.

Original languageEnglish (US)
Article number7192688
Pages (from-to)270-279
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
StatePublished - Jan 31 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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