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Agent-based modelling of post-disaster recovery with remote sensing data
International Journal of Disaster Risk Reduction ( IF 4.2 ) Pub Date : 2021-05-05 , DOI: 10.1016/j.ijdrr.2021.102285
Saman Ghaffarian , Debraj Roy , Tatiana Filatova , Norman Kerle

Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster.



中文翻译:

基于代理的遥感数据灾后恢复建模

灾难风险管理,尤其是灾难后恢复(PDR),对于确保有弹性的发展变得越来越重要。但是,PDR是灾难管理周期中最难以理解的阶段,可能需要数年甚至数十年的时间。与包括社会和经济过程在内的功能评估相比,利用地理空间遥感数据可以相对容易地监控和评估恢复的物理方面。因此,有必要探索恢复的不同方面的影响,包括个人行为及其与社会经济制度的相互作用。在这项研究中,我们开发了一种基于主体的模型来模拟和探索2013年受台风海燕破坏的菲律宾塔克洛班市区的PDR过程。正式和非正式(贫民窟)部门的家庭在模型中有所区别,以探索其复原力和不同的恢复模式。机器学习得出的土地利用图是从遥感图像中提取的,以用于灾前和灾后,并用于提供有关物理恢复的信息。我们使用经验模型来评估两个现实的政策方案:灾难发生后的搬迁地点建设和改善就业选择的投资。我们发现,由于灾后头几个月贫民窟的快速重建和提供低收入工作,贫民窟居民的恢复速度高于正规部门的家庭。最后,结果表明,住户的

更新日期:2021-05-10
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