Activity Spaces and Big Data Sources in Segregation Research: A Methodological Review

Abstract

The activity space approach is increasingly mobilized in spatial segregation research to broaden its scope from residential neighborhoods to other socio-spatial contexts of people. Activity space segregation research is an emerging field, characterized by quick adaptation of novel data sources and interdisciplinary methodologies. In this article, we present a methodological review of activity space segregation research by identifying approaches, methods and data sources applied. First, our review highlights that the activity space approach enables segregation to be studied from the perspectives of people, places and mobility flows. Second, the results reveal that both traditional data sources and novel big data sources are valuable for studying activity space segregation. While traditional sources provide rich background information on people for examining the social dimension of segregation, big data sources bring opportunities to address temporality, and increase the spatial extent and resolution of analysis. Hence, big data sources have an important role in mediating the conceptual change from a residential neighborhood-based to an activity space-based approach to segregation. Still, scholars should address carefully the challenges and uncertainties that big data entail for segregation studies. Finally, we propose a framework for a three-step methodological workflow for activity space segregation analysis, and outline future research avenues to move toward more conceptual clarity, integrated analysis framework and methodological rigor.