Revealing the determinants of gender inequality in urban cycling with large-scale data, by A. Battiston, L. Napoli, P. Bajardi, A. Panisson, A. Perotti, M. Szell & R. Schifanella, published in EPJ Data Science

The uptake of cycling in today’s cities 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. While Strava data is particularly well-suited to describe the behavior of regular cyclists and its generalizability to occasional cyclists requires further investigation, the size of these data and their characteristics represent an unprecedented opportunity for the literature on cycling. 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—where the penetration of Strava is assumed to be comparable—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. Assuming that the determinants of the gender-cycling-gap are similar across regular and occasional cyclists, our study suggests that 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.

Today we published a paper on ideological polarization. Special congrats to Marilena for this being her first paper!

An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: they cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization which can capture the factors we listed. The measure is based on the Generalized Euclidean (GE) distance, which estimates the distance between two vectors on a network, e.g., representing people’s opinion. This measure can fill the methodological gap left by the state of the art, and leads to useful insights when applied to real-world debates happening on social media and to data from the US Congress.

Multidimensional tie strength and economic development, by L.M. Aiello, S. Joglekar, and D. Quercia, published in Scientific Reports

For decades, Granovetter’s tie strength has been quantified using the frequency of interaction. Yet, frequency does not reflect Granovetter’s initial conception of strength, which is a mix of social dimensions including exchnage of knowledge and provision of support. We used Natural Language Processing to quantify whether text messages convey expressions of knowledge or support, and applied it to a large conversation network from of Reddit users resident in the United States. Borrowing a classic experimental setup, we tested whether the diversity of social connections of Reddit users resident in a specific US state would correlate with the economic opportunities in that state (estimated with GDP per capita). We found that the combination of diversity calculated on the knowledge and support networks correlates much more strongly with GDP than diversity calculated on a network weighted with interaction frequency (R2=0.62 vs. R2=0.30). We also found that the two types of ties differ in their geographical span. Knowledge ties are long-distance (i.e., connecting people living in far-away states), support ties are mostly created among people living close by. Read more in this blogpost.

We published another urban planning paper:

1. Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen, by S.M. Breum, B. Kostic, and M. Szell, published in Transport Findings

   

   To improve intersection planning, here we develop a computational method to detect cyclist trajectories from video recordings and apply it to the Dybbølsbro intersection in Copenhagen, Denmark. In one hour of footage we find hundreds of trajectories that contradict the design, explainable by the desire for straightforward, uninterrupted travel largely not provided by the intersection. This neglect and the prioritization of vehicular traffic highlight opportunities for improving Danish intersection design.