From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910898841452544 |
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| author | Ma, Chenzhi Du, Hongru Luan, Shengzhi Dong, Ensheng Gardner, Lauren M. Gernay, Thomas |
| author_facet | Ma, Chenzhi Du, Hongru Luan, Shengzhi Dong, Ensheng Gardner, Lauren M. Gernay, Thomas |
| contents | 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 Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk Ma, Chenzhi Du, Hongru Luan, Shengzhi Dong, Ensheng Gardner, Lauren M. Gernay, Thomas Machine Learning Data Analysis, Statistics and Probability Applications 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. |
| title | From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk |
| topic | Machine Learning Data Analysis, Statistics and Probability Applications |
| url | https://arxiv.org/abs/2503.22689 |