A spatially explicit model of postdisaster housing recovery. (12th August 2019)

• Record Type: Journal Article
• Authors: Nejat, Ali; Javid, Roxana J.; Ghosh, Souparno; Moradi, Saeed
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 35:Number 2(2020:Feb.)
• Page Start: 150
• Page End: 161
• Publication Date: 2019-08-12
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12487
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: 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.