Topological street-network characterization through feature-vector and cluster analysis

TL;DR

This study introduces a method to characterize and compare cities based on topological features extracted from their street network graphs, revealing urban similarities through clustering.

Contribution

It proposes a novel approach using topological feature vectors and clustering to analyze and compare city street networks, filling a gap in urban network characterization.

Findings

Topological features effectively describe urban characteristics.

Clustering reveals meaningful city similarities.

Method applied to 645 cities in Sao Paulo.

Abstract

Complex networks provide a means to describe cities through their street mesh, expressing characteristics that refer to the structure and organization of an urban zone. Although other studies have used complex networks to model street meshes, we observed a lack of methods to characterize the relationship between cities by using their topological features. Accordingly, this paper aims to describe interactions between cities by using vectors of topological features extracted from their street meshes represented as complex networks. The methodology of this study is based on the use of digital maps. Over the computational representation of such maps, we extract global complex-network features that embody the characteristics of the cities. These vectors allow for the use of multidimensional projection and clustering techniques, enabling a similarity-based comparison of the street meshes. We experiment with 645 cities from the Brazilian state of Sao Paulo. Our results show how the joint of global features describes urban indicators that are deep-rooted in the network's topology and how they reveal characteristics and similarities among sets of cities that are separated from each other.