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

Published: 31 October 2016 Publication History

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.

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      cover image ACM Other conferences
      SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
      October 2016
      649 pages
      ISBN:9781450345897
      DOI:10.1145/2996913
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 31 October 2016

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      Author Tags

      1. disaster management
      2. evacuation hotspot detection
      3. human mobility
      4. location data
      5. urban computing

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      SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;
      Overall Acceptance Rate 257 of 1,238 submissions, 21%

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      • (2024)Forecasting Lifespan of Crowded Events With Acoustic Synthesis-Inspired Segmental Long Short-Term MemoryIEEE Access10.1109/ACCESS.2024.341750912(87309-87322)Online publication date: 2024
      • (2024)Undesignated Evacuation Site Location Characteristics: Identification by Mobile Spatial Statistics and Consideration of Support MethodsInformation Technology in Disaster Risk Reduction10.1007/978-3-031-64037-7_14(219-229)Online publication date: 30-Jun-2024
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      • (2022)Event Detection and Event-Relevant Tweet Extraction with Human MobilityMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_1(3-23)Online publication date: 8-Feb-2022
      • (2021)VARIATION PROPERTIES OF TRIP GENERATION, TRIP ATTRACTION, INTRAZONAL TRIPS, AND TRAVEL TIME UNDER TRANSPORT NETWORK DISRUPTIONJournal of JSCE10.2208/journalofjsce.9.1_209:1(20-38)Online publication date: 2021
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