Characterizing Construction Demand Surge Using Spatial Panel Data Models

Bibliographic Data

ID21738220
AuthorsNavid Ahmadi (0000-0002-1202-9121, The University of Texas at Arlington), Mohsen Shahandashti (0000-0002-2373-7596, Assistant Professor, Dept. of Civil Engineering, Univ. of Texas at Arlington, 416 S. Yates St., Arlington, TX 76010 (corresponding author), corresponding author)
Year2020
Volume21
Issue2
Publication date2020-05-01
Open AccessNo
TypeARTICLE
VenueNatural Hazards Review (JOURNAL)
Journal identifiersISSN: 1527-6988 • E-ISSN: 1527-6996
PublisherAmerican Society of Civil Engineers (ASCE) (PUBLISHER • US)
DOI10.1061/(asce)nh.1527-6996.0000368
OpenAlexW3004341607
LanguageEN
Citations received2
References cited17

The socioeconomic losses in the aftermath of large-scale natural disasters have increased dramatically over the last few decades. The postdisaster survival of cities and communities depend on their capabilities to reconstruct and repair damage to buildings and other infrastructure systems following large-scale natural disasters. The significant postdisaster increases in the repair costs, also referred to as demand surge, slows down the repair process and lengthens the recovery time for many touched by large-scale natural disasters. The objective of this research was to explore spatiotemporal relationships in modeling construction demand surge using spatial panel data models. We obtained the data of predisaster construction economic indicators from the Bureau of Labor Statistics (BLS). These indicators had been shown to affect the postdisaster demand surge through cross-sectional studies. The results of this study showed that a positive spatial autocorrelation among the values of the dependent variable exists. In other words, an increase (decrease) in the construction labor wage in a county will lead to an increase (decrease) in the construction labor wage in neighboring counties. The results of this study also showed that spatiotemporal autocorrelations exist among the values of the dependent variable (labor wage change) and the values of error terms across the neighboring counties (counties within 100 km distance from each other). It is expected that the results of this study and the methodology used to create spatial paned data model (SPDM) demand surge models will help demand surge modelers create more accurate models

Cartography · Econometrics · Economics · Geography · Labour economics · Meteorology · Natural disaster · Panel data · Spatial analysis · Statistics · Surge · Wage · Computer Science · Disaster Management and Resilience · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis

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