EAGER: Citizenly: Empowering Communities by Democratizing Urban Data Science

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Governments around the nation have been using data-driven approaches to drive improvements in city operations and citizens' quality of life. Large cities such as New York, Chicago, and Boston have reported impressive results from the use of data in addressing urban issues. Smaller cities such as Rochester, NY have mostly lagged behind. Data has not played a large role in addressing problems of urban neighborhoods. Midsize cities are large enough to have data collection systems in place. But they cannot afford a team of data scientists to make use of them. The open data sets, provided by data.gov and many other organizations, contain raw data. This data requires specialized skills to access, make sense of, and to compute with. Most established open data sets are most suitable for conducting large data analysis. They require significant efforts in filtering data down to specific scenario of local interests. Citizens and community leaders are key consumers of open data. But they are not able to access, decipher, and use this data in meaningful ways. While many tools and algorithms for conducting data science exist, most of these technologies cannot simply be used by anyone. Democratizing data science is the notion that anyone, with little to no technical expertise, can do data science. But they must be provided the right data and user-friendly tools. This project will put urban data science into the hands of citizens and community leaders. It will integrate citizens into the development of urban policy and solutions for local issues. Community leaders would like to see data filtered down to their community levels. The resulting analysis should be made relevant to citizens. A major impact of the work is that it can significantly lower the barrier to entry for community leaders and citizens to meaningfully leverage urban data. Of particular significance is the engagement of neighborhood youths. They will become neighborhood innovators, designing technology applications to support neighborhood-based self-sufficiency strategies. Moreover, the project will develop infrastructure that can be replicated for other/similar midsize cities as well pave the way to democratize data science in other domains.This project will develop the underlying scientific and engineering foundations necessary to create the Citizenly cyberinfrastructure to democratize data science amongst citizens and city governments. This project is multidisciplinary and it will bring together a team of investigators, with expertise in big data analytics, program synthesis, active learning, and social sciences. Key partners, all in the city of Rochester, NY, include the Office of Innovation and Strategic Initiatives and the Office of Research and Analytics at the Common Ground Health. The project will address the following key scientific and technical challenges: (1) Community-focused Data Infrastructure: Design and implementation of a lightweight citizen sensor. Design anexpressive and efficient model for community dataset and implement a scalable Extract-Transform-Load (ETL) system to automate its creation. (2) Citizen-centric Programing: Design domain specific language for citizens and community leaders to access community datasets and to express the intent and constraints for desired urban application. (3) Urban Data Science Applications: Using the Citizenly approach, develop: (a) predictive models for the influence of presence of vacant lots on city's property values; and (b) assessment models for health impact from socio-economic factors. Citizenly will integrate community data and common services along with necessary systems and algorithm innovations to provide next generation cyberinfrastructure for city leaders and citizens. Citizenly will enable research efforts broadly in urban data science and community-based participatory research by providing Cyberinfrastructure (CI) services to a diverse scientific community or communities.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.