Many observers believe that cities and places subject to frequent natural and human disturbance will reach "tipping points" that inhibit their resilience to the level that recovery may not be possible. Understanding the complex relationships among human and natural systems and the thresholds of tipping points are critical to ensuring sustainable development of cities and regions, especially when they are faced with the threats of both long-term climate change and short-term, large-scale disturbance. Until recently, most of the research in the field of post disaster recovery focused on households or whole communities. The empirical study of business responses to disasters is relatively scarce, despite the fact that businesses are a fundamental part of the cities, providing services, jobs, and taxes that are essential for urban sustainability. This doctoral dissertation research project will develop an agent-based simulation model to represent and understand the businesses reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The research has two objectives. First, it will identify the main predictors associated with reopening and estimate their relative importance over time, using an empirical data set collected during studies conducted over the previous five years. A computer simulation model based on the parameters derived from the first objective then will be developed and validated to represent the business-reopening process.
This project will contribute to geography, related spatial sciences, and other fields that examine disaster economy recovery and dynamic modeling. The use of simulation will enhance understanding of the complex recovery process and how a business opening in one location at one point in time will affect business reopening probabilities in other locations in later time periods. The validated simulation model supported by real data will allow researchers and policy makers to design and test recovery policies so that resources can be better allocated. Although the simulation model will be based on post-Katrina New Orleans data, it will be adaptable for other locations affected by disasters of a similar nature. The simulation and visualization computer program developed in this research will be made available to the open source community. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.
The empirical study of business responses to disasters is relatively scarce, despite that businesses are a fundamental part of the cities, providing services, jobs, and taxes that are essential for urban sustainability. This dissertation research developed an agent-based simulation model to represent and understand the business reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The objectives were two-fold: 1) To identify the main reopening predictors involved and estimate their relative importance through time, using an empirical data set collected from a previous study; 2) To represent the business reopening process through a computer simulation model, using the parameters derived from the first objective. The results show that businesses located in flooded areas had lower reopening probabilities. However the effect was significant only in the first nine months after the disaster. Larger businesses had better reopening probabilities than smaller ones, although this variable stopped being significant after six months. Variables traditionally associated with higher social vulnerability, such as percent non-white population and percent population under 18 years old, had a negative effect on the business reopening probabilities at different points of time. The influence of neighboring firms using 1-km buffer was found significantly positive only immediately after the disaster; it became significantly negative one year after the disaster. The simulation model developed proved to mimic the actual reopening process at a suitable level. The model was used to simulate two scenarios: 1) First, the flood depth was reduced by 1 meter as a way to represent the implementation of measures designed to increase the buildings and infrastructure resistance to floods. The simulation results indicate that there are specific areas that would obtain greater benefit from these measures, however ten months after the disaster the effect of the measures tends to diminish. 2) Second, the spatial effects of aids were simulated by making a limited number of businesses in specific locations totally resilient to the disaster. The results indicate that the beneficial effect is influenced by variables such as business density and socio-economic conditions of the area. The positive effect is perceivable until four months after the disaster, after this point it diminishes. This research contributes to an increase in understanding in vulnerability and sustainability science and helps develop methods for spatial dynamic modeling. The research is innovative, as it is among the first to simulate the business recovery process using businesses as agents in a simulation model. The use of simulation has helped in understanding the complex recovery process and how a business opens in one location at one point in time will affect the business reopening probabilities in other locations in the second time period. The research is significant, because the simulation model developed has been validated by real data. A validated simulation model allows researchers to design and test recovery policies so that resources can be better allocated, as shown by the two scenarios simulated in this research. Although the simulation model is based on post-Katrina New Orleans data, the methods developed in this research could be applied to study other locations affected by disasters of the same and other nature.
