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A platform for research: civil engineering, architecture and urbanism
Natural Disasters, Inequality, and Civil Infrastructure Systems: Developing Equitable Pathways toward Resilience
The connections between natural disasters and socioeconomic inequality are complex and dynamically change due to the vicious cycle of socioeconomic disadvantage and disaster impacts. Because of the inherent complexity and the evolving nature of vulnerability, it is very difficult to deduce general statements or a general theory of vulnerability. This paper presents a novel social sensing approach for near real-time and context-specific identification of the physical and socioeconomic vulnerabilities in areas affected by natural disasters. A variety of data analysis and data mining techniques such as sentiment analysis, geo and temporal coding, content analysis, and clustering were used to identify socioeconomic, infrastructure, and regional vulnerabilities to natural disasters. The proposed method was tested for the impacts of the 2019 Hurricane Dorian on South Carolina. The results showed the promising capabilities of the proposed method in near real-time detecting of events such as flooding and power outages as well as identifying different types of vulnerabilities among various socioeconomic groups. The findings of this study facilitate quick decision-making for prioritization and allocation of resources to vulnerable communities in areas where major disaster events or infrastructure failures are detected. In addition, the proposed method enables identifying the underlying factors that make a community or an individual most vulnerable to the impacts of natural disasters.
Natural Disasters, Inequality, and Civil Infrastructure Systems: Developing Equitable Pathways toward Resilience
The connections between natural disasters and socioeconomic inequality are complex and dynamically change due to the vicious cycle of socioeconomic disadvantage and disaster impacts. Because of the inherent complexity and the evolving nature of vulnerability, it is very difficult to deduce general statements or a general theory of vulnerability. This paper presents a novel social sensing approach for near real-time and context-specific identification of the physical and socioeconomic vulnerabilities in areas affected by natural disasters. A variety of data analysis and data mining techniques such as sentiment analysis, geo and temporal coding, content analysis, and clustering were used to identify socioeconomic, infrastructure, and regional vulnerabilities to natural disasters. The proposed method was tested for the impacts of the 2019 Hurricane Dorian on South Carolina. The results showed the promising capabilities of the proposed method in near real-time detecting of events such as flooding and power outages as well as identifying different types of vulnerabilities among various socioeconomic groups. The findings of this study facilitate quick decision-making for prioritization and allocation of resources to vulnerable communities in areas where major disaster events or infrastructure failures are detected. In addition, the proposed method enables identifying the underlying factors that make a community or an individual most vulnerable to the impacts of natural disasters.
Natural Disasters, Inequality, and Civil Infrastructure Systems: Developing Equitable Pathways toward Resilience
Batouli, Mostafa (author) / Joshi, Deepti (author)
Construction Research Congress 2022 ; 2022 ; Arlington, Virginia
Construction Research Congress 2022 ; 188-197
2022-03-07
Conference paper
Electronic Resource
English
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