A framework for spatial heat risk assessment using a generalized similarity measure
This work addresses spatial heat risk assessment for public health planning in Maryland, representing an incremental improvement by generalizing existing similarity measures.
The study tackled the problem of assessing health risks from heat hazards across localities in Maryland by developing a framework that quantifies exposure and vulnerability indicators using feature vectors and clustering to compute reference vectors for high-risk environments, resulting in a method that generalizes risk valuation with cosine similarity and avoids subjective entropy-based aggregation.
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.