|Talks|

Urban highways are barriers to social ties

Visiting speaker
In-person
Past Talk
Sándor Juhász
Complexity Science Hub, Vienna
May 9, 2024
1:00 pm
May 9, 2024
1:00 pm
In-person
4 Thomas More St
London E1W 1YW, UK
The Roux Institute
Room
100 Fore Street
Portland, ME 04101
Network Science Institute
2nd floor
Network Science Institute
11th floor
177 Huntington Ave
Boston, MA 02115
Network Science Institute
2nd floor
Room
58 St Katharine's Way
London E1W 1LP, UK

Talk recording

Urban highways are common, especially in the US, making cities more car-centric. They promise the annihilation of distance but obstruct pedestrian mobility, thus playing a key role in limiting social interactions locally. Although this limiting role is widely acknowledged in urban studies, the quantitative relationship between urban highways and social ties is barely tested. Here we define a Barrier Score that relates massive, geolocated online social network data to highways in the 50 largest US cities. At the unprecedented granularity of individual social ties, we show that urban highways are associated with decreased social connectivity. This barrier effect is especially strong for short distances and consistent with historical cases of highways that were built to purposefully disrupt or isolate Black neighborhoods. By combining spatial infrastructure with social tie data, our method adds a new dimension to demographic studies of social segregation. Our study can inform reparative planning for an evidence-based reduction of spatial inequality, and more generally, support a better integration of the social fabric in urban planning.

About the speaker
Sándor Juhász is currently a Marie Sklodowoska Curie Postdoctoral Fellow at Complexity Science Hub Vienna and an external member of ANETI Lab Budapest. He holds a PhD in economic geography from Utrecht University, 2019 and a PhD in economics from University of Szeged, 2020. His main research interests are urban data science, geography of innovation and interfirm networks. He works mainly on data-driven projects, using spatial and network data to understand social and economic processes such as local economic development, the success and failure of industries, or socio-economic inequalities.
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May 09, 2024