Abstract
The housing sector is an important part of every community. It directly affects people, constitutes a major share of the building market, and shapes the community. Meanwhile, the increase of developments in hazard-prone areas along with the intensification of extreme events has amplified the potential for disaster-induced losses. Consequently, housing recovery is of vital importance to the overall restoration of a community. In this relation, recovery models can help with devising data-driven policies that can better identify pre-disaster mitigation needs and post-disaster recovery priorities by predicting the possible outcomes of different plans. Although several recovery models have been proposed, there are still gaps in the understanding of how decisions made by individuals and different entities interact to output the recovery. Additionally, integrating spatial aspects of recovery is a missing key in many models. The current research proposes a spatial model for simulation and prediction of homeowners’ recovery decisions through incorporating recovery drivers that could capture interactions of individual, communal, and organizational decisions. RecovUS is a spatial agent-based model for which all the input data can be obtained from publicly available data sources. The model is presented using the data on the recovery of Staten Island, New York, after Hurricane Sandy in 2012. The results confirm that the combination of internal, interactive, and external drivers of recovery affect households’ decisions and shape the progress of recovery.
Highlights
The model was run using the facilities of Texas Tech High-Performance Computing Center (HPCC )
The reason for this configuration was to evaluate the predictions of the model on a wide range of values and accommodate the HPCC runtime limitations
In RecovUS, do factors such as level of damage, financial resources, and a ordability and availability of rental properties a ect households’ decisions in favor of repair/reconstruction, waiting, or selling, and their perception of their neighborhood and its restoration play a critical role. The model separates this perception for di erent residents such that heterogeneous households prefer community features dissimilar in terms of type and distance
Summary
. Population growth in hazard-prone areas together with the increase in severity of extreme events (Bergholt & Lujala ; Guha-Sapir et al ; Pravettoni ; Smith ) has raised the potential for disaster losses (Cutter et al ; Schwartz ). A better understanding of the recovery process is necessary. Recovery process is a continuum of interdependent and mostly concurrent activities during pre-disaster preparedness and post-disaster short-term, intermediate, and long-term recovery. Among the components of this continuum, early-decided policies have a significant e ect on the progress of recovery (FEMA ). Analysis and modeling capabilities could help with capturing the dynamics of recovery and underpinning recovery plans
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