  The escalating frequency and intensity of heat-related climate events,
particularly heatwaves, emphasize the pressing need for advanced heat risk
management strategies. Current approaches, primarily relying on numerical
models, face challenges in spatial-temporal resolution and in capturing the
dynamic interplay of environmental, social, and behavioral factors affecting
heat risks. This has led to difficulties in translating risk assessments into
effective mitigation actions. Recognizing these problems, we introduce a novel
approach leveraging the burgeoning capabilities of Large Language Models (LLMs)
to extract rich and contextual insights from news reports. We hence propose an
LLM-empowered visual analytics system, Havior, that integrates the precise,
data-driven insights of numerical models with nuanced news report information.
This hybrid approach enables a more comprehensive assessment of heat risks and
better identification, assessment, and mitigation of heat-related threats. The
system incorporates novel visualization designs, such as "thermoglyph" and news
glyph, enhancing intuitive understanding and analysis of heat risks. The
integration of LLM-based techniques also enables advanced information retrieval
and semantic knowledge extraction that can be guided by experts' analytics
needs. Our case studies on two cities that faced significant heatwave events
and interviews with five experts have demonstrated the usefulness of our system
in providing in-depth and actionable insights for heat risk management.
