TY - JOUR
T1 - Drivers of post-disaster relocations
T2 - The case of Moore and Hattiesburg tornados
AU - Mayer, Joshua
AU - Moradi, Saeed
AU - Nejat, Ali
AU - Ghosh, Souparno
AU - Cong, Zhen
AU - Liang, Daan
N1 - Funding Information:
This research was supported in part by the National Science Foundation award # 1454650 for which the authors express their appreciation. Publication of this paper does not necessarily indicate acceptance by the funding entities of its contents, either inferred or especially expressed herein.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Conditional inference trees
KW - Disaster recovery
KW - Relocation
UR - http://www.scopus.com/inward/record.url?scp=85085043996&partnerID=8YFLogxK
U2 - 10.1016/j.ijdrr.2020.101643
DO - 10.1016/j.ijdrr.2020.101643
M3 - Article
AN - SCOPUS:85085043996
VL - 49
JO - International Journal of Disaster Risk Reduction
JF - International Journal of Disaster Risk Reduction
SN - 2212-4209
M1 - 101643
ER -