Data schemas for multiple hazards, exposure and vulnerability. Issue 6 (4th November 2019)

• Record Type: Journal Article
• 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; 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;
• Is Part Of: Disaster prevention and management. Volume 28:Issue 6(2019)
• Page Start: 752
• Page End: 763
• Publication Date: 2019-11-04
• Subjects: Disaster risk -- Fragility -- Natural hazards -- Exposure -- Vulnerability -- Damage -- Multi-hazard; Emergency management -- Periodicals; Disaster relief -- Periodicals; 363.3405
• DOI: 10.1108/DPM-09-2019-0293
• Languages: English
• ISSNs: 0965-3562

Abstract

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.