Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models

TL;DR

This paper demonstrates that commute networks, modeled with deep learning, effectively predict urban socioeconomic indicators across U.S. cities, highlighting the importance of network structures in urban analysis.

Contribution

It introduces a novel framework combining commute networks with deep learning models for socioeconomic prediction, outperforming traditional methods.

Findings

Mobility network structures significantly improve socioeconomic prediction accuracy.

Deep learning models outperform conventional machine learning approaches.

Commute networks alone can predict socioeconomic indicators without node features.

Abstract

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.