What is a key benefit of using spatial catastrophe models to quantify potential maximum loss (PML) and expected shortfall (CED) in geospatial risk management?

• A. They ignore regional differences and apply a uniform model.
• B. They provide non-spatial estimates of risk.
• C. They quantify PML and CED across locations by modeling hazard, exposure, and vulnerability in a geospatial framework.
• D. They only consider historical disasters in one location.

Answer: (C)

Spatial catastrophe modeling tests the idea that risk varies across geography and can be quantified location by location by weaving hazard, exposure, and vulnerability into a geospatial framework. By mapping where dangerous events can occur, how valuable assets sit in those areas, and how damage scales with the hazard, these models produce location-specific measures of potential loss. This lets you estimate potential maximum loss and tail risk (the expected shortfall) for each location or region, and then aggregate or compare across the landscape. The strength of this approach is that it captures spatial heterogeneity and interactions between where the hazard hits and what assets are there, rather than giving a single, uniform risk figure. The other ideas fall short because they ignore spatial differences, offer non-spatial risk estimates, or rely only on past events from a single location, missing how risk distributes and compounds across a broader geospatial context.