DisasterMapper: A CyberGIS framework for disaster management using social media data

Qunying Huang, Guido Cervone, Duangyang Jing, Chaoyi Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
EditorsVarun Chandola, Ranga Raju Vatsavai
PublisherAssociation for Computing Machinery, Inc
Pages1-6
Number of pages6
ISBN (Electronic)9781450339742
DOIs
StatePublished - Nov 3 2015
Event4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015 - Seattle, United States
Duration: Nov 3 2015 → …

Publication series

NameProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015

Other

Other4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
CountryUnited States
CitySeattle
Period11/3/15 → …

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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