SCC-CIVIC-PG Track B: A Visualization Tool and Assessment Framework for Civic Technology Use in the DMV Area: The Case of 311 Systems During the COVID-19 Outbreak

市政官员目前部署的311系统可以有效地帮助机构发现非紧急的公民问题，如街道上的坑洼和垃圾。虽然311系统是为非紧急呼叫而设计的，但居民可以将该技术重新用于最初没有打算的功能。例如，当飓风伊尔玛在2017年袭击迈阿密时，居民使用311系统报告与灾害有关的问题，导致市政官员对该系统进行了调整，并为呼叫创建了一个新的类别。2019冠状病毒病大流行影响了公民基础设施的各个方面，从公共交通系统减少到公共图书馆关闭。如果管理得当，地方政府和公众可以共同开发有效的公民技术，解决与大流行病有关的问题，促进优质服务。由于目前尚不清楚地方政府如何通过311系统在管理效率和公民赋权之间取得平衡，因此项目团队旨在了解人们如何使用该系统，地方政府如何支持人们对该系统的不同使用，以及地方政府对311系统的不同绩效指标如何在大都市地区层面发挥作用。在该奖项支持的规划阶段，项目团队通过社区合作伙伴Connected DMV组织的圆桌会议，与华盛顿特区大都会地区（即DMV地区）的地方政府建立联系，并将目前不一致的311数据集整合为一个可行的统一数据库。如果第一阶段取得成功，项目团队将通过分析311个数据集和调查城市官员来整合地方政府的绩效指标。在调查的基础上，该团队将建立一个基于网络的可视化工具，显示大流行期间大都市级系统的性能。这些工具和指标将直接影响政府的服务质量、系统设计调整过程和公民对沟通过程的认识。这项研究还将教育本科生和研究生关于数据科学及其社会影响的知识，特别是关于在311系统中较少代表的边缘化社区。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The 311 systems that city officials currently deploy can efficiently help agencies detect non-emergency civic issues such as potholes and trash on the streets. Although the 311 system was designed for non-emergency calls, residents can re-appropriate the technology for capacities not initially intended. For example, when Hurricane Irma hit Miami in 2017, residents used 311 systems to report disaster-related problems, leading city officials to adapt the system and create a new category for calls. The COVID-19 pandemic has impacted all aspects of civic infrastructures that range from reduced public transportation systems and public libraries' closure. If managed well, local governments and publics can co-produce effective civic technology that addresses pandemic-related issues and promotes quality services. Because it is still unclear how local governments balance between managerial efficiency and citizen empowerment through 311 systems, the project team aims to understand how people use the system, how local governments support people's different uses of the system, and how the different performance metrics of local governments for 311 systems function at the metropolitan area level. In the planning phase supported by this award, the project team is establishing connections with local governments in the Washington D.C. metropolitan area (a.k.a., DMV area) through roundtables organized by a community partner, Connected DMV, and is consolidating currently-inconsistent 311 datasets as a viable unified database. If Stage 1 is successful, the project team will integrate local governments' performance metrics by analyzing their 311 datasets and survey city officials. Based on the survey, the team will build a web-based visualization tool that shows metropolitan-level system performance during the pandemic. These tools and metrics will directly impact governments' service quality, system design adjustment processes, and citizens' awareness of communication processes. This research will also educate undergraduate and graduate students about data science and its social implications, particularly regarding marginalized communities less represented in the 311 systems.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.