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RecovUS: An Agent-Based Model of Post-Disaster Household Recovery

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  • Saeed Moradi
  • Ali Nejat

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.

Suggested Citation

  • Saeed Moradi & Ali Nejat, 2020. "RecovUS: An Agent-Based Model of Post-Disaster Household Recovery," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-13.
    • Handle: RePEc:jas:jasssj:2020-37-3
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    1. Drago Bergholt & Päivi Lujala, 2012. "Climate-related natural disasters, economic growth, and armed civil conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 49(1), pages 147-162, January.
    2. C. G. Kaufman & B. A. Shaby, 2013. "The role of the range parameter for estimation and prediction in geostatistics," Biometrika, Biometrika Trust, vol. 100(2), pages 473-484.
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    Cited by:

    1. Eduardo Landaeta & Jesse Richman, 2023. "A Model of Build Back Better Utilization: Long-Term Recovery Groups and Post-Disaster Housing Recovery," Sustainability, MDPI, vol. 15(23), pages 1-16, November.

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