ABSTRACT

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.

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