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  1. Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '15)
  2. Spatio-temporal join technique for disaster estimation in large-scale natural disaster
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Semantics-Aware Warehousing of Symbolic Trajectories
ST-CRF: A Novel Map Matching Approach for Low-frequency Floating Car Data
A New Trajectory Similarity Measure for GPS Data
Component Moving Region Operations: Implementing Set Operations on Region Streams
Vector Map Constrained Path Bundling in 3D Environments
A hybrid approach combining real-time and archived data for mobility analysis
Spatio-temporal join technique for disaster estimation in large-scale natural disaster
MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data
REGULA: Utilizing the Regularity of Human Mobility for Location Recommendation
A Classification Schema for Fast Disambiguation of Spatial Prepositions
RendezView: Look at Meanings of an Encounter Region over Local Social Flocks

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Spatio-temporal join technique for disaster estimation in large-scale natural disaster

Content Provider ACM Digital Library
Author Sugaya, Natsuko Asahara, Akinori Ogawa, Yuichi Hayashi, Hideki Tomita, Hitoshi
Abstract When a large-scale natural disaster occurs, it is necessary to collect damage information within about 10 minutes so that disaster-relief operations and wide-area support (depending on the the scale of the natural disaster) can be initiated. A high-performance method for "spatio-temporal join" which joins time-series grid data (such as results of simulations of natural disasters like tsunamis and fire spreading after a large-scale earthquake) and time-series point data representing people flows is proposed and applied to estimate damage situations following a natural disaster. The results of a performance evaluation of the method show that the response time for joining 100,000 point data and 250,000 grid data is about 50 seconds. They also show that it is possible to apply the proposed method to a real environment in which it is necessary to join one-million point data and hundreds of thousands of grid data within 10 minutes.
Starting Page 49
Ending Page 58
Page Count 10
File Format PDF
ISBN 9781450339711
DOI 10.1145/2833165.2833171
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-03
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Grid data Spatio-temporal database Disaster management Moving objects Spatio-temporal join
Content Type Text
Resource Type Article
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