Paper Info
Title
Emergency Situation Awareness During Natural Disasters Using Density-Based Adaptive Spatiotemporal Clustering
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.
Year
DOI
Venue
2015
10.1007/978-3-319-22324-7_13
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2015
Keywords
Field
DocType
Emergency situation awareness, Density-based spatiotemporal clustering, Natural disaster, Geotagged tweet, Naive bayes classifier
Data mining,Social media,Naive Bayes classifier,Computer science,Situation awareness,Popularity,Georeference,Natural disaster,Cluster analysis,Spatiotemporal Analysis
Conference
Volume
ISSN
Citations 
9052
0302-9743
2
PageRank 
References 
Authors
0.40
15
3
Name
Order
Citations
PageRank
Tatsuhiro Sakai1104.71
Keiichi Tamura23713.86
H. Kitakami39449.68