Paul, Nicole;
Galasso, Carmine;
Baker, Jack;
Silva, Vitor;
(2025)
A predictive model for household displacement duration after disasters.
Risk Analysis
10.1111/risa.17710.
(In press).
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Abstract
According to recent Household Pulse Survey data, roughly 1.1% of households were displaced due to disasters in the United States in recent years. Although most households returned relatively quickly, 20% were displaced for longer than 1 month, and 14% had not returned by the time of the survey. Protracted displacement creates enormous hardships for affected households and communities, yet few disaster risk analyses account for the time component of displacement. Here, we propose predictive models for household displacement duration and return for practical integration within disaster risk analyses, ranging in complexity and predictive power. Two classification tree models are proposed to predict return outcomes with a minimum number of predictors: one that considers only physical factors (TreeP) and another that also considers socioeconomic factors (TreeP&S). A random forest model is also proposed (ForestP&S), improving the model's predictive power and highlighting the drivers of displacement duration and return outcomes. The results of the ForestP&S model highlight the importance of both physical factors (e.g., property damage and unsanitary conditions) and socioeconomic factors (e.g., tenure status and income per household member) on displacement outcomes. These models can be integrated within disaster risk analyses, as illustrated through a hurricane scenario analysis for Atlantic City, NJ. By integrating displacement duration models within risk analyses, we can capture the human impact of disasters more holistically and evaluate mitigation strategies aimed at reducing displacement risk.
| Type: | Article |
|---|---|
| Title: | A predictive model for household displacement duration after disasters |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/risa.17710 |
| Publisher version: | https://onlinelibrary.wiley.com/doi/10.1111/risa.1... |
| Language: | English |
| Additional information: | © 2025 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Science & Technology, Social Sciences, Life Sciences & Biomedicine, Physical Sciences, Public, Environmental & Occupational Health, Mathematics, Interdisciplinary Applications, Social Sciences, Mathematical Methods, Mathematics, Mathematical Methods In Social Sciences, disaster displacement, disaster risk, displacement duration, household displacement, machine learning, population return, HURRICANE KATRINA, HOUSING RECOVERY, NATURAL DISASTERS, UNITED-STATES, NEW-ORLEANS, NORTHRIDGE EARTHQUAKE, SOCIAL VULNERABILITY, PLACE ATTACHMENT, DECISION-MAKING, RETURN |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205868 |
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