Abstract
In this talk I will discuss work undertaken for the project “Learning Data Science Through Civic Engagement With Open Data”. This project, which is funded through the National Science Foundation’s Advancing Informal STEM Leaning (AISL) program, studies the informal data science learning that takes place within workshops, and other events and activities, that have been developed to support community engagement with civic open data in New York City (NYC).
NYC is a leader in Open Data initiatives, which are centered around the NYC Open Data portal, and which have become enshrined in the City Charter. It also has a large and highly diverse population, including many traditionally underserved communities. As government service provision becomes increasingly digital, large amounts of data are generated and subsequently used to assess need, drive service delivery decisions, and evaluate effectiveness. Services producing these data include education, transport, and 311 service requests (a non-emergency municipal service available in many cities for reporting problems such as noise or public safety concerns), and the data they produce can be probed to ask many important questions such as: “How do City agencies respond to noise in my neighborhood?”, “How do waste and recycling services in my neighborhood compare with others?”, and “Are there more construction permits issued for my neighborhood than similar areas?”. To better understand how diverse communities might access and analyze these data to answer questions, share narratives about issues of concern, and respond to data driven policy and resource allocation, we are studying programs offered by the Mayor’s Office of Data Analytics (MODA) and BetaNYC. MODA is the NYC agency with overall responsibility for the City’s Open Data programs, while BetaNYC is a leading nonprofit organization working to improve the lives of NYC residents through civic design, technology, and engagement with government open data. We study the ecosystem that has emerged around the programs these organizations offer as a possible model for identifying, validating, and evaluating best practices; including questions of participation and potential barriers to entry.
Bio Graham Dove
Dr. Graham Dove is a human-computer interaction researcher, with experience in participatory approaches to design and citizen science.
Based in NYU Tandon’s Dept. of Technology Management and Innovation (TMI), and the Center for Urban Science and Progress (CUSP), Graham investigates ways that people who are not experts in data science can use quantitative data and artificial intelligence to inform decision making, advocacy, and creativity in design. Current projects include investigating the informal learning that takes place around NYC Open Data, designing data rich interfaces to support future healthcare work practices. SONYC (Sounds of New York City), which investigates approaches to monitoring and mitigating noise pollution. He has previously worked in Denmark and the UK.
