Computational Modeling of Household Level Damage and Resilience Based on Location Data

A critical aspect of community resilience to disasters that has not been addressed by traditional coarse-scale resilience engineering is within-community inequities in resilience – who is resilient to what?

The goal of this project is to develop an integrated approach for assessing household-level resilience and inequities in resilience during coastal flooding events, specifically by improving building-level flood and fragility estimates for coastal flooding events, and developing a new approach for estimating what essential services are the main constraints on individuals returning to a more normal life post-hazard and assess inequities in resilience to coastal flooding events.

Maximum water depth as a function of model resolution for a hypothetical flood in the Netherlands, from Brusseeet al. (2021).

A critical aspect of community resilience to disasters that has not been addressed by traditional coarse-scale resilience engineering is within-community inequities in resilience – who is resilient to what?

The goal of this project is to develop an integrated approach for assessing household-level resilience and inequities in resilience during coastal flooding events, specifically by improving building-level flood and fragility estimates for coastal flooding events, and developing a new approach for estimating what essential services are the main constraints on individuals returning to a more normal life post-hazard and assess inequities in resilience to coastal flooding events.

This work leverages existing computational research approaches to address a problem of societal importance – estimating damage, recovery, and inequities in recovery for coastal flooding events at a scale not previously attempted. We also plan to make improvements in the computational approach to using cell phone location data through more memory-efficient spatial parallelization of the data processing.

U-M Researchers

Seth Guikema

Jeremy Bricker