The global surge in social inequalities is one of the most pressing issues of our times. The spatial expression of social inequalities at the city scale gives rise to urban segregation, a common phenomenon across different local and cultural contexts.

The increasing popularity of Big Data and computational models has inspired a growing number of computational social science studies that analyze, evaluate, and issue policy recommendations for urban segregation.

Today’s wealth of information and computational power could inform urban planning for equity. However, as Anastassia will discuss in this talk, segregation research is epistemologically interdependent with prevalent economic theories that overfocus on individual responsibility while neglecting systemic processes.

This individualistic bias is also ingrained in computational models of urban segregation. Through several contemporary examples of how Big Data — and the assumptions underlying its usage — influence (de)segregation patterns and policies, a cautionary tale can be told.

With Anastassia’s co-author Trivik Verma, they highlight how a lack of consideration for data ethics can lead to the creation of computational models that have a real-life, further marginalizing impact on disadvantaged groups.

They aim to develop a better discernment of the pitfalls and potentials of computational approaches to urban segregation, thereby fostering a conscious focus on systemic thinking about urban inequalities.

They suggest setting an agenda for research and collective action that is directed at demobilizing individualistic bias, informing our thinking about urban segregation, but also, more broadly, their efforts to create sustainable cities and communities.

Link to the preprint paper: https://arxiv.org/abs/2505.01830 

Please note that this is an internal event. If you want to participate, please write an email to .