Date of Award
Open Access Dissertation
The recovery process after a major disaster or disruption, is impacted by the inequality of risk prior to and post event. In the past decades there has been few efforts to model the recovery process and the focus is mainly on staged models (i.e. emergency, restoration, and reconstruction). The overarching research question asks how a non-stage-like model could apply to the recovery process. This study poses three broad questions: 1) what are the indicators suitable for monitoring the recovery process; 2) what are the driving factors of differential recovery trends; and 3) what are the predicted development trajectories for communities if there was no disruption?
To address the research questions, a new model is proposed for tracking the recovery process as the “Tempo-variant Model of Disaster Recovery” (TMDR), which is implemented for six case studies of recoveries post-earthquakes, in a continuous trend through time (case studies from: Chile, New Zealand, India, Iran, China, and Italy). The recovery process is monitored through a set of proposed indicators representing the changes in six main categories of housing, socio-economic, agriculture, infrastructural, institutional, and development. Satellite imagery is used as a congruent data source to monitor urban land surface change that is implemented with a new model and conditional algebra for change detection. A new method is then developed by combining the satellite imagery data with social indicators, which provides quantitative/relative measure of recovery trend (spatially and temporally) where ground assessments are impractical.
The results of implementing the new TMDR model in this cross-cultural comparative study, further highlights the drivers of recovery process across time and nations. The difference between post-event and pre-event trends (i.e. recovery progress) shows significant association with instantaneous impact of the event on community development dynamics in all cases. The spatio-temporal analysis shows majority of the study area in Chile is recovered, but there are regions in the other cases that are still recovering. The comparative view on TMDR results indicates that impact of event is more significant for recovery progress in the initial years post-event, while additional indicators of access to basic infrastructure is more predictive in the long-term. Therefore, this new model provides a case-dependent baseline and an operational tool for monitoring the recovery process.
Derakhshan, S.(2020). Spatio-Temporal Modeling of Earthquake Recovery. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6036