Developing A "Living Laboratory" for Examining Community Recovery and Resilience After Disaster

这项研究建立在德克萨斯州沿岸几个现有的研究项目的基础上，为检查灾后社区的恢复和恢复能力提供了一个“生活实验室”。德克萨斯州海岸正迅速成为美国人口增长最快的地区，这可能使数百万人面临气象灾害的不利影响。最近一次，飓风艾克于2008年9月12日夜间在德克萨斯州加尔维斯顿附近登陆。在飓风艾克之前，德州海岸社区规划地图集记录了德州海岸的自然、环境、法规和社会发展模式（见coastalatlas.tamug.edu）。在NSF探索性研究小额资助（SGER） CMMI-0901605下收集的数据提供了关于撞击、脱位以及早期修复和重建决策的即时数据。这些数据为拟议的研究提供了基线措施，以衡量两年期间多个尺度的社区恢复。利用原始样本，研究人员将建立一系列关于家庭、住房单位、企业主、企业和商业结构的小组研究，以跟踪恢复轨迹和适应性学习。地理编码的包裹级数据集允许我们在多个尺度上汇总单元以得出结论。此外，研究人员还将通过参与式观察分析、定性访谈和文献分析，跟踪县市政府的政策变化，以评估适应性管理和社会学习。灾害研究界呼吁采用越来越系统化和定量的方法来模拟灾害后的影响和恢复过程，更多地关注在多个层面上衡量恢复，以便更好地模拟社区的恢复能力。还需要系统地确定公共当局在备灾、救灾、恢复和减灾规划以及政策制定方面作出的关键决定，以评估与适应性学习有关的复原力的一个关键方面。这项研究与现有项目的协同作用提供了这样的能力——从灾害前事件到社区反应、学习和恢复，对建筑、监管和社会环境的动态进行定量建模——每一个都是恢复力的关键维度。这项研究的结果将利用现有的推广工具来进一步了解，这将使参与设计、监管和管理人造环境和自然环境的当地社区和专业人士能够建设更具社会和物理弹性的社区。

This research builds upon several existing research initiatives along the Texas coast to provide a "living laboratory" for examining community recovery and resilience after a disaster. The Texas coast is quickly becoming the fastest growing area in the United States, exposing potentially millions more people to the adverse impacts of meteorologically-based disasters. Most recently, Hurricane Ike made landfall overnight on September 12, 2008 near Galveston, Texas. Prior to Hurricane Ike, the Texas Coastal Communities Planning Atlas documented the physical, environmental, regulatory, and social development patterns present along the Texas Coast (see coastalatlas.tamug.edu). Data collection under NSF Small Grant for Exploratory Research (SGER) CMMI-0901605 provided immediate data on impact, dislocation, and early repair and rebuilding decisions. These data provide baseline measures for the proposed research measuring community recovery at multiple scales over a two-year period. Using the original sample, the researchers will establish a series of panel studies of households, housing units, business owners, businesses, and business structures to track recovery trajectories and adaptive learning. A geo-coded parcel-level dataset allows us to aggregate units to draw conclusions at multiple scales. In addition, the researchers will, through participatory observation analysis, qualitative interviews, and documentary analysis, track policy changes by county and city governments to assess adaptive management and social learning.The disaster research community has called for increasingly systematic and quantitative approaches to modeling the impacts and recovery processes following a disaster, with greater attention to measuring recovery at multiple levels, to better model community resilience. Systematic identification of the key decisions made by public authorities regarding disaster preparedness, response, recovery and mitigation planning and policy development is also needed to assess a critical dimension of resiliency associated with adaptive learning. The synergy of this research with existing projects provides the ability to do just that - to quantitatively model the dynamics of the built, regulatory, and social environment from pre-hazard event to community response, learning, and recovery - each of which are key dimensions in resilience. Findings from this research will leverage existing outreach tools to further knowledge that will enable local communities and professionals involved in the design, regulation, and management of the built and natural environments to construct communities that are more socially and physically resilient.