Finding networks of segregation through Big Data

Vinicius Netto and colleagues have written an interesting study where they explore metadata from Twitter users moving around the city and try to derive their networks of spatial behaviour and  segregation. The authors would be glad to receive comments on the study.

Digital footprints in the cityscape: Finding networks of segregation through Big Data

Abstract:

Segregation has been one of the most persistent features of cities and therefore one of the main research topics in social studies. From a tradition that can be traced back to the Chicago School in the early 20th century, social segregation has been seen as the natural consequence of the social division of space, reducing segregation territorial segregation and taking the space as a substitute for social distance. We propose a change in the focus of static segregation of places to as social segregation is played by embodied urban trajectories. We analysed trajectories of groups of social actors differentiated by income levels in Rio de Janeiro, Brazil. Firstly, we used metadata from Twitter users moving around the city to derive geographic coordinates and timestamp of tweets, and identified users’ origins and destinations. Then we crossed information on trajectories with socioeconomic data in order to see potential social networks according to income, assess their spatial behaviour and potential spaces of social convergence – a geography of the segregative / integrative potential of encounters. This approach is intended to recast the spatiality of segregation potentially active in the circumstances of social contact in the city rather than in static territories and patterns of residential location.

[image credit: Netto et al, 2015]