NYU-Poly professors win Google Faculty Research Awards
Two faculty members from the Polytechnic Institute of New York University (NYU-Poly) are among the latest recipients of the Google Faculty Research Awards—one-year grants supporting cutting-edge research in various disciplines of computer science and engineering
uliana Freire, professor of computer science and engineering, and Thanasis Korakis, research assistant professor of electrical and computer engineering, are among the 100 university engineers and scientists from around the globe recognized by the web search giant.
Freire’s research tackles one aspect of a major hurdle facing urban planners and policymakers at a time when more people than ever are living in cities: how to analyze extremely complex data sets to better understand the dynamics of cities, assess their service needs, and ensure that they are met. In this project, Freire is exploring data from a central element of urban life in New York City—taxi cab rides—as a model for a new framework for analyzing spatio-temporal data.
With information provided by the NYC Taxi and Limousine Commission, Freire used data from more than 540 million taxi cab rides over a three-year period to create a prototype visual exploration system that enables scientists and lay people to analyze data involving time and location on a scale that is currently impossible. Taxi rides are a rich source of information about urban life, providing insight into many aspects of New York City, including identifying areas that are most popular at certain times of day, neighborhoods underserved by taxis, and traffic patterns. These can in turn be used to better understand economic activity, human behavior, and mobility patterns.
“Tremendous amounts of data are available, but making sense of it is very challenging,” Freire explained. “Social scientists and decision-makers are limited by the current tools for analysis, which can’t handle large data sets. They can analyze slices of data, but it’s much harder to appreciate the full picture,” she said.
Freire’s model will unify data selection and visual analysis to allow even lay users to explore large data sets through visual queries; for example, a user could explore taxi service in different neighborhoods at a certain time of day by selecting the regions and time frame on a map. The query results would present highly complex information in a simple visual format. Freire and her collaborators also plan to incorporate other data sets, including data from New York City’s bike share program, Citi Bike.