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

这项研究建立在几个现有的研究倡议沿着得克萨斯州海岸提供一个“生活实验室”，检查社区恢复和灾后恢复能力。 德克萨斯州海岸正在迅速成为美国增长最快的地区，可能使数百万人受到气象灾害的不利影响。 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，德克萨斯州海岸社区规划地图集记录了德克萨斯州海岸沿着的物理，环境，监管和社会发展模式（见coastalatlas.tamug.edu）。 根据NSF探索性研究小额资助（SGER）CMMI-0901605收集的数据提供了有关影响，脱位和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情况。 利用原始样本，研究人员将建立一系列对家庭、住房单元、企业主、企业和企业结构的小组研究，以跟踪恢复轨迹和适应性学习。 一个地理编码的包裹级数据集允许我们聚合单位，以得出多个尺度的结论。 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，评估适应性管理和社会学习。灾害研究界呼吁，应采用更加系统和定量的方法来模拟灾害后的影响和恢复过程，并更加关注多层次的恢复测量，to better model模型community社区resilience弹性. 为了评估与适应性学习有关的复原力的一个重要方面，还需要系统地确定公共当局在备灾、应灾、恢复和减灾规划以及政策制定方面所作的关键决定。 这项研究与现有项目的协同作用提供了这样做的能力-从灾害前事件到社区响应，学习和恢复，定量建模建筑，监管和社会环境的动态-其中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，使当地社区和参与建筑和自然环境的设计，监管和管理的专业人员能够构建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.