Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146634
Title: Data schemas for multiple hazards, exposure and vulnerability
Authors: Murnane, Richard J.
Allegri, Giovanni
Bushi, Alphonce
Dabbeek, Jamal
de Moel, Hans
Duncan, Melanie
Fraser, Stuart
Galasso, Carmine
Giovando, Cristiano
Henshaw, Paul
Horsburgh, Kevin
Huyck, Charles
Jenkins, Susanna F.
Johnson, Cassidy
Kamihanda, Godson
Kijazi, Justice
Kikwasi, Wilberforce
Kombe, Wilbard
Loughlin, Susan
Løvholt, Finn
Masanja, Alex
Mbongoni, Gabriel
Minas, Stelios
Msabi, Michael
Msechu, Maruvuko
Mtongori, Habiba
Nadim, Farrokh
O’Hara, Mhairi
Pagani, Marco
Phillips, Emma
Rossetto, Tiziana
Rudari, Roberto
Sangana, Peter
Silva, Vitor
Twigg, John
Uhinga, Guido
Verrucci, Enrica
Keywords: Science::General
Issue Date: 2019
Source: Murnane, R. J., Allegri, G., Bushi, A., Dabbeek, J., de Moel, H., Duncan, M., . . . Verrucci, E. (2019). Data schemas for multiple hazards, exposure and vulnerability. Disaster Prevention and Management, 28(6), 752-763. doi:10.1108/DPM-09-2019-0293
Journal: Disaster Prevention and Management
Abstract: Purpose: Using risk-related data often require a significant amount of upfront work to collect, extract and transform data. In addition, the lack of a consistent data structure hinders the development of tools that can be used with more than one set of data. The purpose of this paper is to report on an effort to solve these problems through the development of extensible, internally consistent schemas for risk-related data. Design/methodology/approach: The consortia coordinated their efforts so the hazard, exposure and vulnerability schemas are compatible. Hazard data can be provided as either event footprints or stochastic catalogs. Exposure classes include buildings, infrastructure, agriculture, livestock, forestry and socio-economic data. The vulnerability component includes fragility and vulnerability functions and indicators for physical and social vulnerability. The schemas also provide the ability to define uncertainties and allow the scoring of vulnerability data for relevance and quality. Findings: As a proof of concept, the schemas were populated with data for Tanzania and with exposure data for several other countries. Research limitations/implications: The data schema and data exploration tool are open source and, if widely accepted, could become widely used by practitioners. Practical implications: A single set of hazard, exposure and vulnerability schemas will not fit all purposes. Tools will be needed to transform the data into other formats. Originality/value: This paper describes extensible, internally consistent, multi-hazard, exposure and vulnerability schemas that can be used to store disaster risk-related data and a data exploration tool that promotes data discovery and use.
URI: https://hdl.handle.net/10356/146634
ISSN: 0965-3562
DOI: 10.1108/DPM-09-2019-0293
Rights: © 2019 Emerald Publishing Limited. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EOS Journal Articles

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