A framework for spatial heat risk assessment using a generalized similarity measure

TL;DR

This paper introduces a new framework for assessing heat-related health risks across Maryland using a generalized similarity measure that combines exposure and vulnerability indicators without relying on subjective entropy-based methods.

Contribution

It presents a novel risk assessment framework that employs cosine similarity for risk valuation, avoiding subjective entropy aggregation and enabling flexible, data-driven risk analysis.

Findings

Framework effectively quantifies heat risk across localities.

Uses cosine similarity for risk comparison, generalizing traditional methods.

Provides a scalable approach adaptable to different regions and indicators.

Abstract

In this study, we develop a novel framework to assess health risks due to heat hazards across various localities (zip codes) across the state of Maryland with the help of two commonly used indicators i.e. exposure and vulnerability. Our approach quantifies each of the two aforementioned indicators by developing their corresponding feature vectors and subsequently computes indicator-specific reference vectors that signify a high risk environment by clustering the data points at the tail-end of an empirical risk spectrum. The proposed framework circumvents the information-theoretic entropy based aggregation methods whose usage varies with different views of entropy that are subjective in nature and more importantly generalizes the notion of risk-valuation using cosine similarity with unknown reference points.