Drivers of post-disaster relocations: The case of Moore and Hattiesburg tornados

被引:10
|
作者
Mayer, Joshua [1 ]
Moradi, Saeed [2 ]
Nejat, Ali [2 ]
Ghosh, Souparno [3 ]
Cong, Zhen [4 ]
Liang, Daan [2 ]
机构
[1] Texas Tech Univ, Dept Math & Stat, Lubbock, TX 79409 USA
[2] Texas Tech Univ, Dept Civil Environm & Construct Engn, Box 41023, Lubbock, TX 79409 USA
[3] Univ Nebraska, Dept Stat, Lincoln, NE USA
[4] Univ Texas Arlington, Sch Social Works, Arlington, TX 76019 USA
基金
美国国家科学基金会;
关键词
Disaster recovery; Relocation; Conditional inference trees; REGRESSION TREES; DECISION-MAKING; NEIGHBORHOOD BOUNDARIES; HOUSING RECOVERY; PLACE ATTACHMENT; DISASTER; CLASSIFICATION; RESILIENCE; VULNERABILITY; MIGRATION;
D O I
10.1016/j.ijdrr.2020.101643
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页数:9
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