ANET Lab Seminar Series: Rossano Schifanella

Rossano Schifanella (University of Turin; ISI Foundation): Revealing the determinants of gender inequality in urban cycling with large-scale data

Abstract | Cycling is an outdoor activity with massive health benefits, and an effective solution towards sustainable urban transport. Despite these benefits and the recent rising popularity of cycling, most countries still have a negligible uptake. This uptake is especially low for women: there is a largely unexplained, persistent gender gap in cycling. To understand the determinants of this gender gap in cycling at scale, here we use massive, automatically-collected data from the tracking application Strava on outdoor cycling for 61 cities across the United States, the United Kingdom, Italy and the Benelux area. Leveraging the associated gender and usage information, we first quantify the emerging gender gap in recreational cycling at city-level. A comparison of cycling rates of women across cities within similar geographical areas unveils a broad range of gender gaps. On a macroscopic level, we link this heterogeneity to a variety of urban indicators and provide evidence for traditional hypotheses on the determinants of the gender-cycling-gap. We find a positive association between female cycling rate and urban road safety. On a microscopic level, we identify female preferences for street-specific features in the city of New York. Enhancing the quality of the dedicated cycling infrastructure may be a way to make urban environments more accessible for women, thereby making urban transport more sustainable for everyone.

Bio | Rossano Schifanella is an Associate Professor in Computer Science at the University of Turin, and a Researcher at ISI Foundation, where he is a member of the Data Science for Social Impact and Sustainability group. His research embraces the creative energy of a range of disciplines across machine learning, urban science, computational social science, complex systems, and data visualization. He leverages data-driven approaches to model the behavior of (groups of) individuals and their interactions on social media platforms, aiming at understanding the interplay between online and offline social behavior. He is also passionate about building web interactive interfaces to explore urban spaces and to access human knowledge through geography.