RAPID/Collaborative Research: Linking Household and Infrastructure Data to Understand the Impacts of Winter Storm Uri in Texas

2021年2月，冬季风暴乌里影响了25个州和超过1.5亿美国人，导致长时间停电和停水。在受风暴影响的州中，德克萨斯州受灾最严重。URI对德克萨斯州的影响远远超出预期，导致多人死亡，新冠肺炎疫苗的分发和管理大幅停止，经济损失估计为900亿美元。最重要的是，由得克萨斯州折衷的可靠性委员会管理的独立电网非常接近完全失败。因此，当地电力公司不得不大幅降低电力消耗，导致数百万家庭和企业经历了长时间的停电。当地供水系统也出现了连锁故障，因为泵站出现了故障。据广泛报道，停电对一些人的影响比对其他人的影响更大，但几乎没有任何系统的证据来证实这些观察结果。需要科学地记录这些故障的性质、任何潜在的不同影响，并使用适当的数据确定促成因素。这种数据收集可以提供机会，以了解不同级别的问题，包括技术和基础设施维护问题、预防措施、规划和执行。这项研究的结果可用于解决环境正义问题和更广泛的基础设施复原力规划。因此，该项目旨在通过从多个方面收集详细数据，包括来自家庭和电力/基础设施系统的易腐烂数据，来执行地面事实验证。这项研究的结果将促进建立更具抗灾能力的基础设施系统的知识，这对促进我们国家的健康、繁荣和福利至关重要。这项研究立足于工程学和社会科学的联系，旨在最终探索工程基础设施、运营决策和家庭体验之间的潜在关系。更具体地说，这个快速项目将从两个方面收集数据。首先是家庭数据，包括人口统计、社会经济状况以及他们在风暴和停电期间的经历。这些数据将通过混合数据收集方法收集，包括对家庭的在线调查、对社区组织(CMO)的采访以及从各种社交媒体渠道收集的二次数据。虽然在线数据收集有助于揭示总体趋势，但对社区管理组织和不同社区中受影响的利益相关者的采访将有助于解决低收入社区的数字鸿沟问题。最后，对社交媒体帖子的内容分析提供了另一层信息，将用于填补剩余的数据差距并交叉检查数据趋势。第二个数据收集活动侧重于收集与风暴期间和之后停电的位置、时间和持续时间相关的电网和公用事业数据。这些数据将补充家庭/邻里数据，以提供关于事件拆解及其相关决策的自上而下和自下而上的见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

In February 2021, Winter-Storm Uri, impacted 25 states and more than 150 million Americans leading to extended power and water outages. Among the states impacted by the storm, Texas was the hardest hit. The impact of Uri on the state of Texas was far beyond expectations leading to several deaths, a significant halt in the distribution and administering of COVID-19 vaccines, and an estimated $90 billion economic loss. Most significantly, the independent electric grid managed by the Eclectic Reliability Council of Texas, came very close to a complete failure. Consequently, local electric utilities had to significantly reduce power consumption resulting in millions of households and businesses, experiencing significant and extended periods without power. There were also cascading failures of local water systems, as pumping stations failed. It was widely reported that the power outages disproportionately impacted some populations more than others, and yet there was little in the way of any systematic evidence establishing these observations. There is a need to scientifically document the nature of these failures, any potential disparate impacts, and establish contributing factors using appropriate data. Such data collection can provide opportunities to learn what went wrong at various levels, including technical and infrastructure maintenance issues, precautionary measures, planning, and execution. Findings from this research can be employed to address environmental justice issues and broader infrastructure resilience planning. As such, this project aims to perform ground truth validation by collecting detailed data from multiple fronts including perishable data from households and power/infrastructure sysetms. The findings of this research will advance knowledge for building more disaster resilient infrastructure systems which are critical for promoting the health, prosperity, and welfare of our nation.This research lies at the nexus of engineering and social sciences aiming to ultimately explore potential relationships between engineering infrastructure, operational decisions and household experiences. More specifically, this RAPID project will collect data on two fronts. The first is household data including demographic, socioeconomic statuses together with their experiences throughout the storm and outages. These data will be collected a hybrid data collection method including an online survey of households, interviews with community-based organizations (CMOs), and secondary data collections from various social media outlets. While online data collection helps with uncovering general trends, interviews with CMOs and impacted stakeholders within a diverse set of neighborhoods will help address digital divide issues within lower-income neighborhoods. Finally, content analysis of social media posts provides another layer of information will be used to fill the remaining data gaps and cross-examine data trends. The second data collection activity focuses on the collection of grid and utility data related to the locations, timing, and duration of outages during and after the storm. These data will complement household/neighborhood data to provide both top-down and bottom-up insights on the unraveling of the event and its associated decision making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.