Towards a new paradigm for segregation measurement in an age of big data


Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.

In Urban Informatics
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Supplementary notes can be added here, including code, math, and images.

Qili Gao (高琦丽)
Qili Gao (高琦丽)
Assistant Professor

My research interests include human dynamics and urban informatics.