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DisasterMapper: A CyberGIS framework for disaster management using social media data
| Content Provider | ACM Digital Library |
|---|---|
| Author | Huang, Qunying Cervone, Guido Jing, Duangyang Chang, Chaoyi |
| Abstract | Traditional GIS tools and systems are powerful for analyzing geographic information for various applications but they are not designed for processing dynamic streams of data. This paper presents a CyberGIS framework that can automatically synthesize multi-sourced data, such as social media and socioeconomic data, to track disaster events, to produce maps, and to perform spatial and statistical analysis for disaster management. Within our framework, Apache Hive, Hadoop, and Mahout are used as scalable distributed storage, computing environment and machine learning library to store, process and mine massive social media data. The proposed framework is capable of supporting big data analytics of multiple sources. A prototype is implemented and tested using the 2011 Hurricane Sandy as a case study. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450339742 |
| DOI | 10.1145/2835185.2835189 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Gis Social media Disaster management Social networks Big data |
| Content Type | Text |
| Resource Type | Article |
