The Journal of Machine Learning Research
Geovisual analytics for spatial decision support: Setting the research agenda
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Describing Temporal Correlation Spatially in a Visual Analytics Environment
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Forecasting Hotspots—A Predictive Analytics Approach
IEEE Transactions on Visualization and Computer Graphics
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Space-time dynamics of topics in streaming text
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Semantics + filtering + search = twitcident. exploring information in social web streams
Proceedings of the 23rd ACM conference on Hypertext and social media
Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
VAST '12 Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)
Editorial: Foreword to the special section on visual analytics
Computers and Graphics
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Analysis of public behavior plays an important role in crisis management, disaster response, and evacuation planning. Unfortunately, collecting relevant data can be costly and finding meaningful information for analysis is challenging. A growing number of Location-based Social Network services provides time-stamped, geo-located data that opens new opportunities and solutions to a wide range of challenges. Such spatiotemporal data has substantial potential to increase situational awareness of local events and improve both planning and investigation. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, our system provides the analysts with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning and disaster management. We demonstrate how to improve investigation by analyzing the extracted public behavior responses from social media before, during and after natural disasters, such as hurricanes and tornadoes.