Disaster Loss Data (DATA)

Co-Chairs: Virginia MURRAY and S.H.M FAKHRUDDIN

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The disaster data landscape is complex and the community that is dealing with loss data is a rapidly growing one. When human, monetary or environmental losses occur as a result of a disaster, extensive loss data are collected and stored by different organisations, but the thoroughness and accuracy of the data vary from country to country and even among local entities. Government agencies, private companies and other organisations may collect and manage data related to their own areas of interest using their own standards and procedures, without significant collaboration with other groups. This result in gaps in the data, inconsistent overlaps, and biases that ultimately affect the quality of research conducted and policies made based on the data.

The Disaster Loss Data (DATA) project, under the umbrella of the Integrated Research on Disaster Risk (IRDR) programme, brings together stakeholders from different disciplines and sectors to study issues related to the collection, storage, and dissemination of disaster loss data. The aim is to establish an overall framework for disaster loss data for all providers, to establish nodes and networks for databases, and to conduct sensitivity testing among databases to ensure some level of comparability. This is in furtherance of Goal 2 (characterisation of hazards, vulnerability and risk) in IRDR’s Strategic Plan (2013-2017), to which DATA’s activities are aligned.


The DATA project has identified the following specific project objectives:

  • Bring together loss data stakeholders and develop and utilise synergies.
  • Identify the quality of existing data and what data are needed to improve disaster risk management.
  • Develop recognised standards or protocols to reduce uncertainty in the data.
  • Define “losses” and create transparent methodologies for assessing them.
  • Advocate an increased downscaling of loss data to sub-national geographical levels for policy makers.
  • Educate users regarding data interpretation and data biases.
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