Trajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Media

dc.contributor.authorChae, Junghoonen_US
dc.contributor.authorCui, Yuchenen_US
dc.contributor.authorJang, Yunen_US
dc.contributor.authorWang, Guizhenen_US
dc.contributor.authorMalik, Abishen_US
dc.contributor.authorEbert, David S.en_US
dc.contributor.editorE. Bertini and J. C. Robertsen_US
dc.date.accessioned2015-05-24T19:45:50Z
dc.date.available2015-05-24T19:45:50Z
dc.date.issued2015en_US
dc.description.abstractThe rapid development and increasing availability of mobile communication and location acquisition technologies allow people to add location data to existing social networks so that people share location-embedded information. For human movement analysis, such location-based social networks have been gaining attention as promising data sources. Researchers have mainly focused on finding daily activity patterns and detecting outliers. However, during crisis events, since the movement patterns are irregular, a new approach is required to analyze the movements. To address these challenges, we propose a trajectory-based visual analytics system for analyzing anomalous human movements during disasters using social media. We extract trajectories from location-based social networks and cluster the trajectories into sets of similar sub-trajectories in order to discover common human movement patterns. We also propose a classification model based on historical data for detecting abnormal movements using human expert interaction.en_US
dc.description.sectionheadersTextual, Spatial and Uncertain Dataen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)en_US
dc.identifier.doi10.2312/eurova.20151102en_US
dc.identifier.pages43-47en_US
dc.identifier.urihttps://doi.org/10.2312/eurova.20151102en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectVisual Analyticsen_US
dc.subjectSocial Media Analysisen_US
dc.subjectTrajectory Analysisen_US
dc.subjectData Clusteringen_US
dc.subjectAnomaly Analysisen_US
dc.titleTrajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Mediaen_US
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