Extensible data schemas for multiple hazards, exposure and vulnerability data
The data required for assessing disaster risk can generally be divided into three categories: hazard, exposure and vulnerability. To date there is no widely accepted approach for storing and sharing such risk-related data using a common data structure. As a result, using risk-related data often requires a significant amount of upfront work to collect, extract and transform data before it can be used for purposes such as a risk assessment. In addition, the lack of a consistent data structure hinders the development of tools that can be used for more than one set of data. In practice, this situation introduces a significant amount of friction in efforts to quantify and manage disaster risk.
This paper reports on an effort by three consortia to develop extensible, internally consistent schemas for hazard, exposure and vulnerability data. The consortia coordinated their efforts so the three schemas are compatible. For example, the intensity measure types used to define the hazard datasets are compatible with the intensity measures used by the vulnerability models. Similarly, the asset attributes used in the exposure data taxonomy are compatible with the asset attributes used for the vulnerability data. 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 associated with the hazard data and allows the scoring of vulnerability data for relevance and quality. As a proof of concept, the schemas were populated with data covering the three components for Tanzania and with additional exposure data for several other countries.
This paper is a contribution to the 2019 edition of the Global Assessment Report on Disaster Risk Reduction (GAR 2019).
- Murnane, Richard et al.
- 20 p.
- Information Management, Risk Identification & Assessment
- Tanzania, United Rep of