Latest publications

Predicting population displacements after earthquakes

Rodrigo Costa, Terje Haukaas, & Stephanie Chang

An agent-based object-oriented model for household displacements is presented and used to analyze household decision-making after a hypothetical earthquake in the City of Vancouver, Canada. Temporary displacements and permanent relocation are accounted for. The model for households include considerations of socioeconomic demographics, social networks, and disaster preparedness. The analysis results indicate that nearly 70,000 persons are expected to be displaced by the earthquake. Of those, close to 19,000 will need public sheltering. In addition, nearly 40,000 persons are expected to relocate in the years following the earthquake. Among the displaced persons, occupants of multi-family pre-code and low-code buildings are over-represented. Among those needing public shelter or relocation, there is a disproportionately high number of renters and low income households. The models in this paper can help the development of pre-disaster plans by suggesting optimal location of public shelters, and by identifying decisions that reduce the number of households relocating.

Rodrigo Costa, Terje Haukaas & Stephanie E. Chang (2020) Predicting population displacements after earthquakes, Sustainable and Resilient Infrastructure, DOI: 10.1080/23789689.2020.1746047

Quantification of disaster impacts through household well-being losses

Maryia Markhvida, Brian Walsh, Stephane Hallegatte & Jack Baker

Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households’ well-being. However, for a given asset loss, a wealthy household might quickly recover, while a poor household might suffer major, long-lasting impacts. This research proposes a methodology to quantify disaster impacts more equitably by integrating the three pillars of sustainability: environmental (hazard and asset damage), economic (macro-economic changes in production and employment) and social (disaster recovery at the household level). The model innovates by assessing the impacts of disasters on people’s consumption, considering asset losses and changes in income, among other factors. We apply the model to a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on households of varying wealth. The analysis reveals that poorer households suffer 19% of the asset losses but 41% of the well-being losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities (depending on their built environment and social and economic profiles) and income groups.

Markhvida, M., Walsh, B., Hallegatte, S. et al. Quantification of disaster impacts through household well-being losses. Nat Sustain (2020). https://doi-org.stanford.idm.oclc.org/10.1038/s41893-020-0508-7

G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage

Sabine Loos, David Lallemant, Jack Baker, Jamie McCaughey, Sang-Ho Yun, Nama Budhathoki, Feroz Khan, & Ritika Singh

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially-distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets–and downweight uninformative sources–reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering
forecast.

All publications