Save It for the “Hot” Day: An LLM-Empowered Visual Analytics System for Heat Risk Management
🤖 AI Summary
Current heat-risk assessments rely on numerical models, which suffer from low spatiotemporal resolution and inability to capture dynamic couplings among environmental, social, and behavioral factors—limiting actionable risk-informed decision-making. To address this, we propose *Havior*, the first integrated framework combining numerical simulation with news semantics for heat-risk visualization and analysis. It introduces two novel glyph paradigms: *thermoglyphs* for thermal dynamics and *news glyphs* for event semantics; employs an expert-guided, LLM-driven semantic extraction method for interpretable, dynamic risk-factor modeling; and unifies heterogeneous multi-source data within an interactive visualization interface. Evaluated on the 2022 China heatwave, Havior achieved a 92.3% F1-score in news-based risk information extraction. Six domain experts unanimously affirmed its substantial improvement in depth of risk perception and operational decision support.
Application Category
📝 Abstract
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. We conducted an experiment on information extraction, a case study on the 2022 China Heatwave, and an expert survey & interview collaborated with six domain experts, demonstrating the usefulness of our system in providing in-depth and actionable insights for heat risk management.
Problem
Research questions and friction points this paper is trying to address.
Addressing heat risk management challenges with limited spatiotemporal resolution
Integrating environmental, social, and behavioral factors in heat risk assessment
Translating heat risk assessments into effective mitigation actions
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM extracts insights from news reports
Integrates numerical models with news data
Uses thermoglyph and news glyph visualizations
🔎 Similar Papers
No similar papers found.
