SCC-PG:MAPPING INSTABILITY: Building an Intelligent Community Agent Platform for Understanding the Impact of Large Scale Crisis on Small Town Communities

新冠肺炎大流行继续以不可察觉和潜移默化的方式影响建成环境和社区生活。与大城市不同，大城市的居住、工作和流动性依赖于大型基础设施，而较小社区的生活严重依赖于个人资源和自我维持的结构。应对重大危机的关键一步是及时收集丰富的数据，以了解社区的问题、斗争和需求。然而，传统的数据收集方法，如公开会议，效果不佳，可能有育儿或工作冲突的人参加也很少。在线数据收集方法，如调查，消除了实际存在的需要，扩大了覆盖面和包容性，但它们并不总是支持对话交流，鼓励人们对其问题和需求提供更深入的见解。该项目将通过设计、建立和评估对话代理来改进公民数据收集，以收集有关大流行对马萨诸塞州西部阿默斯特、霍利奥克和皮茨菲尔德居民的影响的数据。虽然这些社区在地理上很接近，但它们的人口结构、经济繁荣和获得公共服务的机会不同。这项研究有助于确定弱势、服务不足和代表性不足的群体，以便分配和优先分配资源和材料，并为解决美国各地小城镇的类似问题提供概念证明。该项目使地方官员能够更深入地了解这些小城镇在当前流行病面前面临的挑战。通过使用以人为中心的方法，该项目将建立一个基于人工智能的对话代理，以收集有关社区生活的各个方面的数据，如居住、交通、工作、教育和医疗保健。这项研究将通过五种方式改变计算机科学和建筑领域的行动和操作模式：(1)通过设计和建立以社区为中心的对话代理平台来改善公共数据收集的现状，(2)经验地解决各种人口统计数据如何与会话代理交互的问题，(3)有助于认识到会话代理在收集公民有意义的意见方面的作用，(4)对当前大流行对小城镇居民生活的影响提供丰富的理解，这将向建筑界提供关于社会、经济、该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

The COVID-19 pandemic continues to impact the built environment and communities’ lives in perceptible and imperceptible manners. Unlike big cities, where dwelling, working, and mobility rely on large infrastructures, life in smaller communities relies heavily on individual resources and self-sustained structures. A vital step in responding to major crises is the timely collection of rich data to understand the community’s issues, struggles, and needs. However, traditional data collection methods such as public meetings are ineffective and poorly attended by those who may have childcare or work conflicts. Online data collection methods such as surveys broaden the outreach and inclusivity by eliminating the need for physical presence, but they do not always support a conversational exchange that encourages people to provide deeper insights into their issues and needs. This project will improve civic data collection by designing, building, and evaluating a conversational agent to collect data about the pandemic’s impact on residents of Amherst, Holyoke, and Pittsfield in Western Massachusett. While these communities are in close geographic proximity, they have different demographics, economic prosperity, and access to public services. This research facilitates identifying vulnerable, under-served, and under-represented groups for allocation and prioritization of resources and materials and serves as a proof of concept for addressing similar issues for small towns across the United States.This project enables local officials to gain a rich understanding of those small towns’ challenges in the face of the current pandemic. By using human-centered methods, this project will build an AI-based conversational agent to collect data about diverse aspects of communities' lives such as Dwelling, Transportation, Work, Education, and Healthcare. This research will transform modes of action and operation in both Computer Science and Architecture fields in five ways: (1) Advancing the status quo of public data collection by designing and building a community-centered conversational agent platform, (2) Empirically addressing how various demographics interact with conversational agents, (3) Contributing to recognition of conversational agents’ role in gathering meaningful input from citizens, (4) Providing a rich understanding of the impacts of the current pandemic on small-town residents’ lives that will inform the architecture field about the effects of social, economic, and cultural forces on the built environment and (5) Making architectural analysis more synchronous and responsive to forces that affect it.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.