Using network metrics to explore the community structure that underlies movement patterns

TL;DR

This paper analyzes movement patterns in Santiago de Chile using network metrics to identify community structures, revealing insights into urban segregation and spatial organization during working hours.

Contribution

It introduces a novel combination of community detection algorithms with segregation analysis to better understand urban community structures.

Findings

Identified distinct communities within Santiago based on movement data.

Revealed correlations between community structures and segregation patterns.

Demonstrated the effectiveness of network metrics in urban spatial analysis.

Abstract

This work aims to explore the community structure of Santiago de Chile by analyzing the movement patterns of its residents. We use a dataset containing the approximate locations of home and work places for a subset of anonymized residents to construct a network that represents the movement patterns within the city. Through the analysis of this network, we aim to identify the communities or sub-cities that exist within Santiago de Chile and gain insights into the factors that drive the spatial organization of the city. We employ modularity optimization algorithms and clustering techniques to identify the communities within the network. Our results present that the novelty of combining community detection algorithms with segregation tools provides new insights to further the understanding of the complex geography of segregation during working hours.