Citizenly: Empowering Communities by Democratizing Data Science
2020-02-03T22:08:36Z (GMT) by
Governments around the nation have been embracing data-driven approaches to drive improvements in city operations and citizens’ quality of life. While mega cities (e.g. New York, Chicago, and Boston) have reported impressive results from the use of data in addressing urban issues, midsize cities (e.g. Rochester, NY and other similar sized cities) have mostly lagged behind. Data has played relatively insignificant role in addressing problems of distressed urban neighborhoods. Midsize cities are large enough to have data collection systems in place but cannot afford a cadre of data scientists to make use of them. The open data sets, provided by data.gov and many other organizations, contain raw data requiring specialized skills to access, make sense of, and to compute with it. Moreover, most established open data sets are suitable for conducting macro-level data analysis and require significant efforts in filtering data down to specific scenario of local interests. Citizens and community leaders, key consumers of open data, are thus not able to access, decipher, and use data in meaningful ways. While multitude of tools and algorithms for conducting data science exist, in reality, 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 if provided the right data and user-friendly tools. By putting urban data science in the hands of citizens and community leaders, who are the key community stakeholders, this project will integrate citizens in the development of urban policy and solutions for local issues that pertain to them. Community leaders would like to see data filtered down to their community levels and make the resulting analysis relevant to citizens. The main broader impact of the work is that it can significantly lower the barrier to entry for community leaders and citizens to leverage urban data in a meaningful fashion. Of particular significance is the engagement of neighborhood youths who 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.