NDLI: A framework for evacuation hotspot detection after large scale disasters using location data from smartphones: case study of Kumamoto earthquake

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A framework for evacuation hotspot detection after large scale disasters using location data from smartphones: case study of Kumamoto earthquake

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A framework for evacuation hotspot detection after large scale disasters using location data from smartphones: case study of Kumamoto earthquake

Content Provider	ACM Digital Library
Author	Sudo, Akihito Sekimoto, Yoshihide Yabe, Takahiro Tsubouchi, Kota
Abstract	Large scale disasters cause severe social disorder and trigger mass evacuation activities. Managing the evacuation shelters efficiently is crucial for disaster management. Kumamoto prefecture, Japan, was hit by an enormous (Magnitude 7.3) earthquake on 16th of April, 2016. As a result, more than 10,000 buildings were severely damaged and over 100,000 people had to evacuate from their homes. After the earthquake, it took the decision makers several days to grasp the locations where people were evacuating, which delayed of distribution of supply and rescue. This situation was made even more complex since some people evacuated to places that were not designated as evacuation shelters. Conventional methods for grasping evacuation hotspots require on-foot field surveys that take time and are difficult to execute right after the hazard in the confusion. We propose a novel framework to efficiently estimate the evacuation hotspots after large disasters using location data collected from smartphones. To validate our framework and show the useful analysis using our output, we demonstrated the framework on the Kumamoto earthquake using GPS data of smartphones collected by Yahoo Japan. We verified that our estimation accuracy of evacuation hotspots were very high by checking the located facilities and also by comparing the population transition results with newspaper reports. Additionally, we demonstrated analysis using our framework outputs that would help decision makers, such as the population transition and function period of each hotspot. The efficiency of our framework is also validated by checking the processing time, showing that it could be utilized efficiently in disasters of any scale. Our framework provides useful output for decision makers that manage evacuation shelters after various kinds of large scale disasters.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2997014
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Disaster management Evacuation hotspot detection Urban computing Location data Human mobility
Content Type	Text
Resource Type	Article

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