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.

翻译：热相关气候事件（尤其是热浪）的频率和强度不断上升，凸显了制定先进热风险管理策略的紧迫性。当前主要依赖数值模型的方法，在时空分辨率以及捕捉影响热风险的环境、社会和行为因素之间的动态相互作用方面面临挑战。这导致将风险评估转化为有效缓解行动存在困难。认识到这些问题，我们引入了一种新方法，利用大型语言模型（LLMs）新兴的能力从新闻报道中提取丰富且具有上下文关联的洞察。因此，我们提出了一种LLM增强的可视化分析系统——Havior，它将数值模型精确、数据驱动的洞察与细致的新闻报道信息相结合。这种混合方法能够更全面地评估热风险，并更好地识别、评估和缓解热相关威胁。该系统融入了新颖的可视化设计，如“热力图符”和新闻图符，增强了对热风险的直观理解和分析。基于LLM技术的集成还实现了高级信息检索和语义知识提取，这些功能可由专家的分析需求引导。我们在两个曾经历重大热浪事件的城市进行的案例研究以及对五位专家的访谈，证明了我们的系统在为热风险管理提供深入且可操作的洞察方面的实用性。