Data-Driven Approaches to Integrated Disaster Risk Management
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EDITORIAL
Data-Driven Approaches to Integrated Disaster Risk Management Special Issue of the 9th Conference of the International Society for Integrated Disaster Risk Management Vincent Lemiale1 • Mahesh Prakash1 • Ana Maria Cruz2
Accepted: 17 October 2020 Ó The Author(s) 2020
The 9th Conference of the International Society for Integrated Disaster Risk Management (IDRiM) was held in Sydney on 2–4 October 2018. The event was hosted by Data61, the data innovation hub of Australia’s National Science Agency CSIRO. The IDRiM annual conference series traditionally brings together researchers and practitioners across all disciplines of disaster risk management (DRM) and the Australian instalment was no exception. More than 120 participants from 20 countries attended the congress, representing all sectors of DRM including academia, industry, and governmental agencies, to exchange on ideas and best practices through a mix of plenaries, presentations, panel discussions, and poster sessions. This special issue, entitled Data-Driven Approaches to Integrated Disaster Risk Management, mirrors the overarching theme of the 2018 IDRiM conference, which set out to explore the ever-increasing role of data in all areas of disaster risk and emergency management. The articles included in this special issue have been selected to represent the diversity of data-driven approaches under active development for DRM. They are also illustrative of the multidisciplinary nature of DRM, its applications ranging from natural hazards (such as earthquakes, tsunamis, hurricanes) to man-made disasters or even a combination of the two. All four phases of DRM, namely prevention (or mitigation), preparedness, response, and recovery are covered in this volume, and many of the themes that were & Vincent Lemiale [email protected] 1
CSIRO Data61, Clayton South, VIC 3169, Australia
2
Disaster Prevention Research Institute, Kyoto University, Kyoto 611-0011, Japan
extensively discussed during the conference are further explored here. In the prevention phase, data science and analytics can inform us on the potential impact of both natural hazards and man-made disasters to help protect the ecological diversity of our environment and make better long-term choices and investments, which in turn will improve the safety and welfare of our communities, as discussed in Liu et al. In this context, the ability to access extensive databases is a critical element of building evidence-based strategies for prevention and preparedness purposes (Elwood et al.; Pinelli, Esteva, et al.). Important challenges remain, however, when dealing with heterogeneous sources of data and several of the technologies presented in the special issue aim to address the issues of data collection, data curation, and data sharing to dramatically improve such cross-disciplinary collaboration (Elwood et al.; Pinelli, Esteva, et al.; Luo et al.). Digital technologies such as smartphones have become ubiquitous in modern life, but their application to DR
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