Integrating Place Attachment into Housing Recovery Simulations to Estimate Population Losses
Publication: Natural Hazards Review
Volume 23, Issue 4
Abstract
Following a disaster, residents of a community may be displaced from their damaged homes, leading to expensive and lengthy disruption, with many choosing to move away permanently. Population losses may hinder recovery and exacerbate inequalities across neighborhoods. This study considered household place attachment and identified groups with low place attachment along with expensive and slow postdisaster recovery. We developed a framework to integrate place attachment considerations into housing recovery simulations. We used data from the American Housing Survey to develop housing and neighborhood satisfaction models and identify the neighborhoods with the least-attached residents. A computational simulation framework was used to simulate postearthquake housing recovery for a community and assess expected costs and time frames. We used the triad of low place attachment, high cost, and slow recovery to identify households prone to permanently moving away from their communities. A case study of housing recovery after a hypothetical earthquake near San Francisco demonstrated the application of the methodology. We found that about 10% of the population in some neighborhoods are prone to moving away after a large earthquake. Households with low income, renters, and those in older buildings are most likely to have low place attachment and experience costly and slow recovery. Whereas existing approaches rely on heuristics, the approach and results in this paper provide quantitative means to assess potential population losses and inform efforts to reduce them. The framework to integrate place attachment into housing recovery simulations is versatile and employs publicly available information making it transferable to other communities.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. These include the cleaned and recoded data and scripts used to fit the models and create the figures.
Acknowledgments
Funding for this for this work was provided by the Stanford Urban Resilience Initiative.
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Published In
Natural Hazards Review
Volume 23 • Issue 4 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Sep 3, 2021
Accepted: Apr 8, 2022
Published online: Jun 20, 2022
Published in print: Nov 1, 2022
Discussion open until: Nov 20, 2022
ASCE Technical Topics:
- Benefit cost ratios
- Business management
- Case studies
- Computer models
- Disaster preparedness
- Disaster recovery
- Disaster risk management
- Earthquakes
- Engineering fundamentals
- Financial management
- Frames
- Geohazards
- Geotechnical engineering
- Housing
- Infrastructure
- Methodology (by type)
- Models (by type)
- Practice and Profession
- Research methods (by type)
- Residential location
- Structural engineering
- Structural members
- Structural systems
- Urban and regional development
- Urban areas
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