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Spatio-Temporal Modeling of Earthquake Recovery

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Spatio-Temporal Modeling of Earthquake Recovery University of South Carolina Scholar Commons Theses and Dissertations Summer 2020 Spatio-Temporal Modeling of Earthquake Recovery Sahar Derakhshan Follow this and additional works at: https://scholarcommons.sc.edu/etd Part of the Geography Commons Recommended Citation Derakhshan, S.(2020). Spatio-Temporal Modeling of Earthquake Recovery. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6036 This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. SPATIO-TEMPORAL MODELING OF EARTHQUAKE RECOVERY by Sahar Derakhshan Bachelor of Science University of Science and Culture, 2005 Master of Science University of Tehran, 2009 Master of Public Policy University of California, Berkeley, 2013 Submitted in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Geography College of Arts and Sciences University of South Carolina 2020 Accepted by: Susan L.Cutter, Major Professor Cuizhen Wang, Committee Member Zhenlong Li, Committee Member Melanie Gall, Committee Member Cheryl L. Addy, Vice Provost and Dean of the Graduate School © Copyright by Sahar Derakhshan, 2020 All Rights Reserved. ii DEDICATION I want to thank and dedicate this work to my parents Mojgan and Hassan, and my brother Saman, who have supported me even from afar. I missed them through these years, and I could not have been here without their love and understanding. I also like to thank my uncle Javad, aunt Nezhat, cousins Mithra, Darius, and Nima; and all the family and friends who have encouraged and supported me. iii ACKNOWLEDGEMENTS I feel fortunate to have met so many wonderful people along this academic journey and I am sincerely thankful to everyone that have supported me. First, I like to thank Dr. Susan Cutter. I will be forever grateful for your guidance, support, leadership, and understanding. I was inspired every day, felt privileged to work with you and thankful for all that I have learned. I am thankful for the support and expert advice of my committee members Dr. Cuizhen Wang, Dr. Zhenlong Li, and Dr. Melanie Gall. I also like to acknowledge the support from Dr. Caroline Nagel, and all of the faculty, staff and students of the UofSC Geography department. I am appreciative of the UofSC graduate school’s Presidential fellowship program’s financial and professional development support in the past four years. I thank the Hazards and Vulnerability Research Institute, and Charlie Faucette for all the help. I am grateful for my great friends that I was lucky to work with at HVRI: Erika Pham, Raelene Campbell, Tyler Spires, Yago Martin, Rachel (Reeves) McCaster, Jessica Brugh, Bryan Bricker, Jake Ramthun, Tracy Whelen, Sarah Jackson, Qian Huang, Logan Lee, Andrew White, Amber Jackson, Julie Downs, Rebecca Baxley, and Olivia Williamson. I am also grateful for the support of the NSF INCLUDES Scholars from Under- Represented Groups in Engineering and the Social Sciences (SURGE) Capacity in Disasters project leadership Dr. DeeDee Bennett, Dr. Terri Norton, Dr. Hans Louis- Charles, Dr. Lori Peek, Dr. Nnenia Campbell, and Dr. Jenniffer Santos-Hernandez; and iv SRUGE scholars/friends, in the past two years, who provided both a motivational and educational experience. Last but not least, I like to thank everyone that helped me find my path: Dr. Yousef Bozorgnia, Dr. Tadahiro Kishida, Dr. Sifat Muin, Dr. Nicolas Kuehn, and Dr. Sharyl Rabinovici at PEER; and Marjorie Greene, Jay Berger, and Heidi Tremayne at EERI. I am grateful for the mentorship and support of Dr. Pierre Mouroux, Dr. Brenda Phillips, and Dr. Jessica Jensen. I also feel privileged for the expert advices and professional support I have received from Dr. Mary Comerio, Dr. Robert Olshansky, Dr. Judith Mitrani-Reiser and Dr. Lori Peek in the past years. v ABSTRACT 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. vi 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. vii TABLE OF CONTENTS Dedication .......................................................................................................................... iii Acknowledgements ............................................................................................................ iv Abstract .............................................................................................................................. vi List of Tables ..................................................................................................................... xi List of Figures .................................................................................................................. xiv List of Abbreviations ....................................................................................................... xix Chapter 1 Introduction ........................................................................................................ 1 1. 1 Conceptualizing Recovery ............................................................................... 3 1. 2 Research Questions .......................................................................................... 5 1. 3 Document Structure ......................................................................................... 8 Chapter 2 Literature on Recovery Process and Modeling ................................................. 9 2. 1 Recovery Models ............................................................................................. 9 2. 2 Measuring Recovery: Indicators and Drivers ................................................ 12 2. 3 Application of remote sensing for extracting urban features ......................... 22 2. 4 Summary ........................................................................................................ 28 Chapter 3 Tempo-variant Model of Disaster Recovery (TMDR) ..................................... 29 3. 1 Proposed Recovery Model ............................................................................. 29 3. 2 Proposed indicators and components ............................................................. 35 3. 3 How does the TMDR recovery model fit with previous models? ................. 39 3. 4 Relation of recovery components with vulnerability and resilience .............. 41 viii 3. 5 Successful recovery and “build back better” expectation .............................. 42 3. 6 Analysis tools and software ........................................................................... 44 3. 7 Summary ........................................................................................................ 46 Chapter 4 Study Areas and Their Characteristics ............................................................. 47 4. 1 The Events ..................................................................................................... 48 4. 2 Socio-economic country profiles ................................................................... 58 4. 3 Socioeconomic Change at Local Scales ........................................................ 63 4. 4 Land Use/Land
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