EAGER: Citizen Science for Infrastructure Monitoring at the Neighborhood Level

为了追求安全可靠的基础设施系统，收集监控数据以评估这些系统的状况、使用情况和运行性能。对于大型基础设施，通常使用各种传感器技术和定期现场检查来收集监测数据。然而，对于社区规模的基础设施，这些数据在数量和质量上仍然有限。虽然参与性数据来源为产生这些数据提供了机会，但对于如何以及何时收集有效和可靠的参与性数据来代替或补充物理测量，人们知之甚少。通过该奖项的支持，将进行基础研究，以设计和测试由志愿公民科学家在社区一级收集基础设施监测数据的协议和工具。 EARLY概念探索性研究（EAGER）项目将有助于了解影响公民生成的基础设施监测数据可靠性和有效性的因素，重点是雨水基础设施。成功实施居民收集基础设施监测数据的协议和工具将加快社区一级高质量数据的产生，使包括当地社区、基础设施工程师、城市规划师和研究人员在内的多方利益相关者受益。这在社会和身体弱势群体的社区尤其有影响力，例如本研究将在休斯顿进行的社区。本研究将推动土木工程、城市规划、社会学和公共卫生领域跨学科研究团队的学术动力，以更好地了解如何以及何时让公众参与收集基础设施监测数据。指导本研究设计的问题是：（1）哪些因素影响了社区一级公民生成的基础设施监测数据的可靠性和有效性，以及（2）如何利用我们对这些因素的理解来制定协议和工具，以便由公众成员收集高质量的基础设施监测数据？草案协议和工具将在一个迭代过程中设计、测试、验证和完善。休斯顿大都市区的社区将被用作研究区域，重点是雨水基础设施。现场试验将包括收集观察数据（由公民科学家收集），基于测量的数据（由工程专业人员收集）和反馈数据（通过研讨会和结束问卷收集）。工程专业人员将使用移动的激光扫描和相机单元来收集基于测量的数据（例如，雨水排放设施的位置、几何形状和条件）。通过这个迭代过程，我们将确定公民科学数据中的方法问题（因为它们适用于雨水基础设施监测），并最大限度地提高协议和工具的保真度。从实地试验中获得的新的经验数据将能够测试关于公民科学家收集的数据（观察数据集）和专业人员收集的数据（基于测量的数据集）之间的一致性的假设，并将参与者的观点纳入数据收集过程（反馈数据集）。

In the pursuit of safe and reliable infrastructure systems, monitoring data are collected to assess the condition, usage, and in-service performance of these systems. For large-scale infrastructure, monitoring data are often collected using a variety of sensor technologies and periodic field inspections. For neighborhood scale infrastructure, however, these data remain limited in both quantity and quality. While participatory data sources provide an opportunity for producing these data, very little is known about how and when to collect valid and reliable participatory data in lieu of, or in addition to, physical measurements. Through support of this award, fundamental research will be pursued to design and test protocols and tools for collecting infrastructure monitoring data at the neighborhood level by volunteer citizen scientists. This EArly-concept Grant for Exploratory Research (EAGER) project will contribute to understanding the factors that influence the reliability and validity of citizen-generated infrastructure monitoring data, with focus on stormwater infrastructure. Successful implementation of protocols and tools for collecting infrastructure monitoring data by residents would accelerate the production of high-quality data at the neighborhood level, benefiting multiple stakeholders, including local communities, infrastructure engineers, urban planners, and researchers. This is especially impactful in neighborhoods with socially and physically vulnerable populations, such as those in Houston where this study will take place. This research will advance the scholarly momentum of an interdisciplinary team of investigators from civil engineering, urban planning, sociology, and public health to better understand how and when to engage members of the general public in collecting infrastructure monitoring data.The questions that guide the design of this study are: (1) What factors influence the reliability and validity of citizen-generated infrastructure monitoring data at the neighborhood level and (2) How can our understanding of these factors be employed to develop protocols and tools for collecting high-quality infrastructure monitoring data by members of the general public? Draft protocols and tools will be designed, tested in field trials, validated, and refined in an iterative process. Neighborhoods in the Houston metropolitan area will be used as the study area, with focus on stormwater infrastructure. The field trials will include the collection of observational data (collected by citizen scientists), measurement-based data (collected by engineering professionals), and feedback data (gathered through workshops and a closing questionnaire). The engineering professionals will use a mobile laser scanning and camera unit to collect the measurement-based data (e.g., location, geometry, and condition of stormwater drainage assets). Through this iterative process, we will identify the methodological issues in citizen science data (as they apply to stormwater infrastructure monitoring) and maximize the fidelity of the protocols and tools. New empirical data, obtained from the field trials, will enable testing hypotheses about the agreement between data collected by citizen scientists (observational dataset) and data collected by professionals (measurement-based dataset), and incorporating the participants perspective in the data collection process (feedback dataset).