Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Human behaviour in fire: Knowledge foundation and temporal evolution
    (Elsevier B.V., 2024-02-13) Haghani M; Lovreglio R; Button ML; Ronchi E; Kuligowski E
    Understanding human behaviour in fires (HBiF), whether in building or wildland fire contexts, is crucial for saving lives and managing evacuations. However, existing research lacks a comprehensive analysis of HBiF knowledge from both perspectives. To address this issue, we examined nearly 1900 HBiF-related research papers and their references, identifying around 6600 frequently cited references as the HBiF knowledge foundation. We focused on highly prominent items using metrics like citation frequency, burst, and centrality. By analysing co-citation patterns among these references, we unveiled current trends and waning areas of HBiF research. This study identifies knowledge gaps and potential future directions for the field, enabling both mapping of the research concerning our fundamental understanding of behavioural decision-making in fires as well as developing more effective life-saving strategies.
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    Social vulnerabilities and wildfire evacuations: A case study of the 2019 Kincade fire
    (Elsevier B.V., 2024-05-31) Sun Y; Forrister A; Kuligowski ED; Lovreglio R; Cova TJ; Zhao X
    Vulnerable populations (e.g., populations with lower income or disabilities) are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their unique needs. While existing studies offer valuable insights, we need to improve our understanding of how vulnerabilities affect wildfire evacuation decision-making, as well as how this varies spatially. The goal of this study is to conduct an in-depth analysis of the impacts of social vulnerabilities on aggregated evacuation decisions, including evacuation rates, delay in departure time, and evacuation destination distance by leveraging large-scale GPS data generated by mobile devices. Specifically, we inferred evacuation decisions at the level of the census block group, a geographic unit defined by the U.S. Census, utilizing GPS data. We then employed ordinary least squares and geographically weighted regression models to investigate the impacts of social vulnerabilities on evacuation decisions. We also used Moran's I to test if these impacts were consistent across different block groups. The 2019 Kincade Fire in Sonoma County, California, was used as the case study. The impacts of social vulnerabilities on evacuation rates show significant spatial variations across block groups, whereas their effects on the other two decision types do not. Additionally, unemployment, a factor under-explored in previous studies, was identified as contributing to both an increased delay in departure time and a reduction in destination distance of evacuees at the aggregate level. Furthermore, upon comparing the significant factors across different models, we observed that some of the vulnerabilities contributing to evacuation rates for all residents differed from those affecting the delay in departure time and destination distance, which only applied to evacuees. These new insights can guide emergency managers and transportation planners to enhance equitable wildfire evacuation planning and operations.