A new hypervolume approach for assessing environmental risks | JoVE Visualize

Area of Science:

Ecology and Environmental Science
Risk Assessment and Management
Spatial Analysis

Background:

Assessing uncertain, potentially damaging environmental events (e.g., pest invasions) is crucial for effective response strategies.
Existing risk measures often struggle to incorporate data uncertainty, limiting their practical application in geographical domains.
The human-mediated spread of invasive species, like the Asian longhorned beetle (ALB), presents complex risk assessment challenges in urban environments.

Purpose of the Study:

To introduce a novel continuous risk measure for assessing and prioritizing environmental risks characterized by uncertain data.
To demonstrate the application of this measure in mapping and classifying risks associated with the spread of the Asian longhorned beetle (ALB) in the Greater Toronto Area.
To provide a more robust and practical method for risk assessment that explicitly incorporates data uncertainty.

Main Methods:

Utilized probabilistic spread rates for geographical locations, represented by cumulative distribution functions (CDFs).
Applied first-order stochastic dominance to identify ordered non-dominant subsets of CDFs, defining risk classes.
Developed a hypervolume-based approach using point clouds of CDF values to create a continuous risk measure, accounting for uncertainty.

Main Results:

Successfully defined distinct risk classes across the Greater Toronto Area based on the continuous risk measure.
Demonstrated that the hypervolume approach effectively incorporates uncertainty, yielding fewer defined risk classes where data is sparse.
The resulting hypervolumes provide a continuous risk depiction, offering a potentially more practical alternative to averaging or ordinal ranking methods.

Conclusions:

The proposed hypervolume-based continuous risk measure offers a rigorous method for depicting and prioritizing environmental risks with uncertain data in geographical domains.
This approach is adaptable and can be used to compare risk assessments derived from different datasets and assumptions.
The method enhances decision-making by providing a nuanced understanding of risk magnitude and associated uncertainty for events like invasive species spread.