TY - JOUR
T1 - A spatially explicit model of postdisaster housing recovery
AU - Nejat, Ali
AU - Javid, Roxana J.
AU - Ghosh, Souparno
AU - Moradi, Saeed
N1 - Funding Information:
This research was supported in part by the National Science Foundation awards #1313946 and #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 specially expressed herein. The authors would like to thank Renee Hooper and Vance Pryor who helped with the review of literature.
Publisher Copyright:
© 2019 Computer-Aided Civil and Infrastructure Engineering
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Although postdisaster housing recovery is an important player in community recovery, its modeling is still in its infancy. This research aims to provide a spatial regression model for predicting households’ recovery decisions based on publicly available data. For this purpose, a hierarchical Bayesian geostatistical model with random spatial effects was developed. To calibrate the model, households’ data that were collected from Staten Island, New York, in the aftermath of Hurricane Sandy were used. The model revealed that on the scale of census tract, residents with higher income or larger household size were significantly less likely to reconstruct. In contrast, odds of reconstruction rose with increase of long-term residents. The model outputs were also employed to develop a reconstruction propensity score for each census tract. The score predicts probability of reconstruction/repair in each tract versus others. The model was validated through comparison of the propensity scores with the distribution of Community Development Block Grant Disaster Recovery assistance and its resultant reconstruction. The validation indicated capability of the model to predict the potential hotspots of reconstruction. Accordingly, the propensity score can serve as a decision-support tool to tailor recovery policies.
AB - Although postdisaster housing recovery is an important player in community recovery, its modeling is still in its infancy. This research aims to provide a spatial regression model for predicting households’ recovery decisions based on publicly available data. For this purpose, a hierarchical Bayesian geostatistical model with random spatial effects was developed. To calibrate the model, households’ data that were collected from Staten Island, New York, in the aftermath of Hurricane Sandy were used. The model revealed that on the scale of census tract, residents with higher income or larger household size were significantly less likely to reconstruct. In contrast, odds of reconstruction rose with increase of long-term residents. The model outputs were also employed to develop a reconstruction propensity score for each census tract. The score predicts probability of reconstruction/repair in each tract versus others. The model was validated through comparison of the propensity scores with the distribution of Community Development Block Grant Disaster Recovery assistance and its resultant reconstruction. The validation indicated capability of the model to predict the potential hotspots of reconstruction. Accordingly, the propensity score can serve as a decision-support tool to tailor recovery policies.
UR - http://www.scopus.com/inward/record.url?scp=85070694555&partnerID=8YFLogxK
U2 - 10.1111/mice.12487
DO - 10.1111/mice.12487
M3 - Article
AN - SCOPUS:85070694555
SN - 1093-9687
VL - 35
SP - 150
EP - 161
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
IS - 2
ER -