You Don't Have to Live Next to Me: Towards Demobilizing Individualistic Bias in Computational Approaches to Urban Segregation

Segregation, conceptualized as the spatial expression of social inequalities, is a common phenomenon across different urban contexts. The increasing popularity of Big Data and computational models has inspired many computational studies on urban segregation. However, these studies are often epistemologically interdependent with prevalent economic theories, which have a hyperfocus on individual responsibility while neglecting systemic processes. We outline how individualistic bias impacts (de)segregation policies, and how disregarding data ethics leads to further marginalization of disadvantaged groups. Our essay suggests revisiting our computational processes to demobilize individualistic bias, fostering instead a collective, conscious, and systemic approach toward inequalities.