Emergency Situation Awareness During Natural Disasters Using Density-Based Adaptive Spatiotemporal Clustering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9052)

Abstract

With the increase in the popularity of social media as well as the emergence of easy-to-use geo-mobile applications on smartphones, a huge amount of geo-annotated data is posted on social media sites. To enhance emergency situation awareness, these geo-annotated data are expected to be used in a new medium. In particular, geotagged tweets on Twitter are used by local governments to determine the situation accurately during natural disasters. Geotagged tweets are referred to as georeferenced documents; they include not only a short text message but also the posting time and location. In this paper, we propose a new spatiotemporal analysis method for emergency situation awareness during natural disasters using \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clustering. Such clustering can identify bursty local areas by using adaptive spatiotemporal clustering criteria considering local spatiotemporal densities. Extracting \((\epsilon ,\tau )\)-density-based adaptive spatiotemporal clusters allows the proposed method to analyze emergency situations such as natural disasters in real time. The experimental results showed that the proposed method can analyze emergency situations related to the weather in Japan more sensitively compared with our previous method.

Keywords

Emergency situation awareness Density-based spatiotemporal clustering Natural disaster Geotagged tweet Naive bayes classifier 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tatsuhiro Sakai
    • 1
  • Keiichi Tamura
    • 1
  • Hajime Kitakami
    • 1
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan

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