JDR Vol.11 No.2 p. 163
doi: 10.20965/jdr.2016.p0163


Special Issue on Disaster and Big Data

Shunichi Koshimura

Project Leader of JST CREST "Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data" assimilation"
Professor, International Research Institute of Disaster Science (IRIDeS), Tohoku University
Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845, Japan

Online released:
March 18, 2016
March 1, 2016

In the years that have passed since the 2011 Great East Japan earthquake, many new findings, insights and suggestions have been made in disaster observation, sensing, simulation, and damage determination on the damage scene. Based on the lessons, challenges for disaster mitigation against future catastrophic natural disasters such as the anticipated Tokyo metropolitan and Nankai Trough earthquakes are made on how we will share visions of potential impact and how we will maximize society’s disaster resilience. Much of the “disaster big data” obtained is related to the dynamic flow of large populations, vehicles and goods inside and outside affected areas. This has dramatically facilitated our understanding of how society has responded to unprecedented catastrophes. The key question is how we will use big data in establishing social systems that respond promptly, sensibly and effectively to natural disasters how this understanding will affect adversity and resilience. Researchers from a wide variety of fields are now working together under the collaborative JST CREST project entitled “Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation.” One objective of this project is to identify potential disaster scenarios related to earthquake and tsunami progress in a chained or compound manner and to create new techniques for responsive disaster mitigation measures enabling society to recover. This special issue on disaster and big data consists of 11 papers detailing the recent progress of this project. As an editor of this issue, I would like to express our deep gratitude for the insightful comments and suggestions made by the reviewers and the members of the editorial committee.

Cite this article as:
S. Koshimura, “Special Issue on Disaster and Big Data,” J. Disaster Res., Vol.11, No.2, p. 163, 2016.
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Last updated on May. 27, 2020