Multiscalarity in Socio-Spatial Segregation: An Information-Theoretic Framework

TL;DR

This paper introduces an information-theoretic framework to analyze socio-spatial segregation across multiple scales, emphasizing the role of connectivity and population distribution in urban inequalities.

Contribution

It presents a novel multiscalar analytical method using GJSD to identify segregation patterns and their relation to connectivity, advancing urban inequality research.

Findings

Segregation patterns vary significantly across city, regional, and national scales.

Connectivity influences the persistence of spatial inequalities.

The framework provides actionable insights for targeted urban interventions.

Abstract

We present a novel analytical framework to examine socio-spatial segregation across multiple spatial scales, explicitly leveraging information theory and percolation theory. This framework emphasizes the interplay between regional connectivity and population distribution, which are critical for understanding how spatial inequalities arise and persist in urban regions. Employing the Generalised Jensen-Shannon Divergence (GJSD), this method identifies regions characterized by significant segregation and low connectivity, providing actionable insights for targeted urban interventions. Using Ecuador as a case study, we demonstrate how segregation patterns manifest differently at city, regional, and national scales, underscoring the critical role of multiscalarity in understanding urban inequalities and guiding scale-sensitive policies. This approach not only advances the methodology for studying socio-spatial segregation but also contributes to the broader field by highlighting the importance of considering multiscalar perspectives and connectivity in urban systems.