  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.
