Citation

Mayer J, Moradi S, Nejat A, Ghosh S, Cong Z, Liang D. Int. J. Disaster Risk Reduct. 2020; 49: e101643.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2020.101643

PMID

unavailable

Abstract

Post-disaster relocation may occur in the aftermath of major disasters, either unplanned or through buyout programs. There is an extensive research on recovery of affected population. However, research on the factors which cause the households to decide in favor of relocation or reconstruction is still in its infancy. This paper suggests a framework for classification and prediction of households' recovery decisions based on their attributes. To achieve this objective, data was collected through a telephone survey of residents affected by the 2013 Moore, Oklahoma and Hattiesburg, Mississippi tornados. The analysis was implemented using conditional inference trees and revealed the significance of level of damage, pre- and post-disaster homeownership, and housing type on the households' relocation decisions. The model can help with devising data-driven recovery policies tailored to a community's characteristics.

Language: en

Keywords

Conditional inference trees; Disaster recovery; Relocation