Abstract
The 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.
Original language | English |
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Title of host publication | EuroVA 2015 - EuroVis Workshop on Visual Analytics |
Editors | Dieter Fellner |
Publisher | Eurographics Association |
Pages | 43-47 |
Number of pages | 5 |
ISBN (Electronic) | 9783905674866 |
DOIs | |
State | Published - 2015 |
Externally published | Yes |
Event | 6th International EuroVis Workshop on Visual Analytics, EuroVA 2015 at EuroVis 2015 - Cagliari, Italy Duration: May 25 2015 → May 26 2015 |
Publication series
Name | International Workshop on Visual Analytics |
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ISSN (Electronic) | 2664-4487 |
Conference
Conference | 6th International EuroVis Workshop on Visual Analytics, EuroVA 2015 at EuroVis 2015 |
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Country/Territory | Italy |
City | Cagliari |
Period | 05/25/15 → 05/26/15 |
Funding
This material is based upon work supported by the U.S. Department of Homeland Security under Award Number 2009-ST-061-CCI001-06. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. Jang’s work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF-2013R1A1A1011170).