International Day for Disaster Risk Reduction 2026: interview with Matt Haasz

Tell us about your role

I lead a research and development team focused on developing tools and datasets for property data enrichment, model interpretation, and process automation.

After studying geography at undergraduate level, and completing an MSc in Risk, I began my career in the insurance industry for six years and have valued how my geography background provided the foundation for applying a range of advanced analytics, like GIS and statistics, to data engineering, data science, and AI. It demonstrates how geography can underpin careers well beyond what many people traditionally associate with the subject.

How does geographic location influence disaster risk, vulnerability, and resilience, and why is it fundamental to effective disaster risk management (DRM)?

A common equation used in DRM to quantify risk is Risk = Hazard x Exposure x Vulnerability. Hazard relates to the magnitude and frequency of a physical hazard, exposure is the human component of the built environment and people, and vulnerability measures resilience and capacity to recover. For any analysis, inaccurate inputs lead to inaccurate results, and for hazard and exposure variables - accurate location data is fundamental.

An example of this can be found in flood risk. A flood model representing elevation in five-metre grid cells will better capture details of a river channel and local topography than a one-kilometre grid. By understanding this flood model's impact on properties, representing houses by their building footprint would generate more representative results than if the only spatial information available is their postcode.

How do spatial factors such as hazard exposure, population distribution, infrastructure, and environmental conditions interact with other elements of DRM, including preparedness, mitigation, response, and recovery?

Vulnerability can exacerbate or completely negate the impacts of hazard on exposed assets and communities. My favourite example is that much of New Orleans sits below sea level and, without human intervention, would be highly susceptible to storm surge and riverine flooding. However, due to a system of levees and pumps much of this risk is managed. Failing to consider this fact would create a fundamental misunderstanding of the risk in the area.

While these levees are permanent, however, vulnerability and resilience can also vary temporally which adds a level of complexity to analysis. For example, the 2023 Türkiye-Syria occurred early in the morning when locals were sleeping, reducing preparedness and contributing to a deadlier event. This shows how a holistic view of hazard, exposure, and vulnerability is key to DRM - omitting one will never give the full picture.