Random planar graphs and the London street network

TL;DR

This paper analyzes London's street network using models and finds it behaves as a self-organizing system balancing physical and mental effort, revealing underlying principles of urban growth.

Contribution

It introduces models of London’s street network and demonstrates how its growth reflects a balance between physical and informational space.

Findings

London's street network exhibits self-organizing properties.

A principle of least effort influences the network's growth.

Models show a balance between physical and mental navigation efforts.

Abstract

In this paper we analyse the street network of London both in its primary and dual representation. To understand its properties, we consider three idealised models based on a grid, a static random planar graph and a growing random planar graph. Comparing the models and the street network, we find that the streets of London form a self-organising system whose growth is characterised by a strict interaction between the metrical and informational space. In particular, a principle of least effort appears to create a balance between the physical and the mental effort required to navigate the city.