Comparative Study of Cities as Complex Networks

TL;DR

This paper explores the limitations of degree distributions in urban network analysis and introduces a new method based on far-away neighbor statistics for automatic city classification.

Contribution

It proposes a novel approach using far-away neighbor statistics to improve structural classification of urban networks beyond traditional degree distribution analysis.

Findings

Degree distributions are scale-dependent and insufficient for urban network analysis.

Far-away neighbor statistics provide additional structural insights.

The new method enables automatic classification of cities based on network structure.

Abstract

Degree distributions of graph representations for compact urban patterns are scale-dependent. Therefore, the degree statistics alone does not give us the enough information to reach a qualified conclusion on the structure of urban spatial networks. We investigate the statistics of far-away neighbors and propose the new method for automatic structural classification of cities.