Across the world, the Open Data movement is growing and more and more cities are releasing data to the public. As citizens push for more openness, it is fair to ask: what can we do with all of this data to make our cities better? In this talk, I’ll explore how I’ve used my blog, I Quant NY, and some data science techniques to make changes in the city I live in: New York City.
From parking ticket geography, to restaurant inspection scores to subway and taxi pricing, I will discuss best practices for data science in the policy space, explore how story telling is an important aspect of data science, and highlight the various data-driven interactions I've had with City agencies that have led to infrastructure and policy changes.
Along the way, I will point out that data science need not use complicated math. I will show examples of the power of simple arithmetic, and show how often it is more about your curiosity and the questions you ask than the complexity of the equations you use.
Ben Wellington is a Quantitative Analyst at Two Sigma and the creator of I Quant NY, a data science and policy blog that focuses on insights drawn from New York City's public data, and advocates for the expansion and improvement of that data. His data science has influenced local government policy including changes in NYC street infrastructure, the way New Yorkers pay for cabs and the design of NYC subway vending machines. Ben is a contributor to the New Yorker, and a visiting assistant professor in the City & Regional Planning program at the Pratt Institute in Brooklyn where he teaches statistics using urban open data and holds a Ph.D. in Computer Science from New York University.