基于百万起火灾数据，识别高风险区域与人群，提出精准防控方案。
From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
- 整合超百万火灾记录与社会、建筑、气象等多源数据，构建风险分析框架。
- 老旧空置建筑和弱势社区火灾风险更高，安装探测器可显著降低伤亡。
- 适合政策制定者、城市规划者及公共安全研究人员参考使用。
建筑火灾持续威胁生命、财产与基础设施，亟需先进风险缓解策略。本研究通过整合超过一百万起火灾事件报告与多种相关数据集（包括社会决定因素、建筑清单、气象条件及事件特异性因素），构建数据驱动的风险分析框架。利用机器学习模型，识别影响火灾发生与后果的关键因素。研究发现，受经济差异影响或存在老旧、空置建筑的弱势社区面临更高火灾风险；火灾起因与消防设施配置等事件特异性因素显著影响后果严重程度。配备火灾探测器与自动灭火系统的建筑，其火势蔓延和人员伤亡风险明显降低。研究可精准定位高风险地区与人群，支持针对性干预措施，如强制安装消防系统、为弱势群体提供补贴，从而提升火灾预防能力，保护脆弱群体，推动更安全、更公平的社会建设。
原文摘要 · Abstract (English)
Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.
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