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


Social media information with geotags can be used to track individuals's movements in their daily lives. By providing both made text and movement data, visual analysis on social media data will be both fascinating and challenging. In distinction to traditional movement knowledge, the sparseness and irregularity of social media information increase the difficulty of extracting movement patterns. To facilitate the understanding of individuals's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory information from social media. We tend to propose a heuristic model to scale back the uncertainty caused by the nature of social media knowledge. In the proposed system, users can filter and choose reliable knowledge from every derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users will explore the semantics of movements, together with the transportation ways, frequent visiting sequences and keyword descriptions. We offer 2 cases to demonstrate how our system can facilitate users to explore the movement patterns.

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