Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities

TL;DR

This paper introduces a novel approach using graph neural networks to analyze commute networks for urban delineation, effectively capturing socioeconomic disparities and providing a new tool for urban community detection.

Contribution

It presents a new method employing GNNs to derive low-dimensional embeddings of urban areas from commute networks, improving community detection and socioeconomic analysis.

Findings

GNN embeddings effectively distinguish socioeconomic groups in cities.

Commute network data enhances regional delineation accuracy.

GNN-based approach outperforms traditional methods in urban community detection.

Abstract

Delineating areas within metropolitan regions stands as an important focus among urban researchers, shedding light on the urban perimeters shaped by evolving population dynamics. Applications to urban science are numerous, from facilitating comparisons between delineated districts and administrative divisions to informing policymakers of the shifting economic and labor landscapes. In this study, we propose using commute networks sourced from the census for the purpose of urban delineation, by modeling them with a Graph Neural Network (GNN) architecture. We derive low-dimensional representations of granular urban areas (nodes) using GNNs. Subsequently, nodes' embeddings are clustered to identify spatially cohesive communities in urban areas. Our experiments across the U.S. demonstrate the effectiveness of network embeddings in capturing significant socioeconomic disparities between communities in various cities, particularly in factors such as median household income. The role of census mobility data in regional delineation is also noted, and we establish the utility of GNNs in urban community detection, as a powerful alternative to existing methods in this domain. The results offer insights into the wider effects of commute networks and their use in building meaningful representations of urban regions.