Abstract
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.
Original language | English |
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Pages (from-to) | 51-60 |
Number of pages | 10 |
Journal | Computers and Graphics (Pergamon) |
Volume | 38 |
Issue number | 1 |
DOIs | |
State | Published - 2014 |
Externally published | Yes |
Funding
This work was partially funded by the U.S. Department of Homeland Security's VACCINE Center under Award Number 2009-ST-061-CI0003 . Jang's work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( NRF-2013R1A1A1011170 ). We would like to thank the reviewers for their valuable suggestions and comments, which helped to improve the presentation of this work.
Keywords
- Disaster management
- Social media analysis
- Visual analytics