Cities Reconceptualized: Unveiling Hidden Uniform Urban Shape through Commute Flow Modeling in Major US Cities

TL;DR

This paper models commute flows in major US cities to reveal a hidden, consistent log-normal distribution in urban population patterns, offering new insights into urban organization and planning.

Contribution

It introduces a novel approach to uncovering underlying urban population distributions through mobility network embedding and theoretical modeling.

Findings

Population distributions tend to follow log-normal patterns in mobility space.

Urban organization may naturally align with log-normal distribution under ideal conditions.

A theoretical model supports the emergence of these distributions via preferential attachment and random walks.

Abstract

Urban development is shaped by historical, geographical, and economic factors, presenting challenges for planners in understanding urban form. This study models commute flows across multiple U.S. cities, uncovering consistent patterns in urban population distributions and commuting behaviors. By embedding urban locations to reflect mobility networks, we observe that population distributions across redefined urban spaces tend to approximate log-normal distributions, in contrast to the often irregular distributions found in geographical space. This divergence suggests that natural and historical constraints shape spatial population patterns, while, under ideal conditions, urban organization may naturally align with log-normal distribution. A theoretical model using preferential attachment and random walks supports the emergence of this distribution in urban settings. These findings reveal a fundamental organizing principle in urban systems that, while not always visible geographically, consistently governs population flows and distributions. This insight into the underlying urban structure can inform planners seeking to design efficient, resilient cities.