Wandering in cities: a statistical physics approach to urban theory

TL;DR

This paper applies statistical physics models to urban systems, explaining city growth, mobility patterns, segregation, and spatial networks, revealing that simple models can effectively capture complex urban phenomena.

Contribution

It introduces a stochastic, out-of-equilibrium model of city growth and a unifying framework for urban segregation, advancing understanding of urban dynamics through simple, data-driven approaches.

Findings

Secondary subcenters emerge due to traffic congestion.

Total distance traveled scales sublinearly with city population.

Segregation patterns can be quantitatively characterized.

Abstract

The amount of data that is being gathered about cities is increasing in size and specificity. However, despite this wealth of information, we still have little understanding of what really drives the processes behind urbanisation. In this thesis we apply some ideas from statistical physics to the study of cities. We first present a stochastic, out-of-equilibrium model of city growth that describes the structure of the mobility pattern of individuals. The model explains the appearance of secondary subcenters as an effect of traffic congestion, and predicts a sublinear increase of the number of centers with population size. Within the framework of this model, we are further able to give a prediction for the scaling exponent of the total distance commuted daily, the total length of the road network, the total delay due to congestion, the quantity of CO2 emitted, and the surface area with the population size of cities. In the third part, we focus on the quantitative description of the patterns of residential segregation. We propose a unifying theoretical framework in which segregation can be empirically characterised. In the fourth and last part, we succinctly present the most important---theoretical and empirical---results of our studies on spatial networks. Throughout this thesis, we try to convey the idea that the complexity of cities is -- almost paradoxically -- better comprehended through simple approaches. Looking for structure in data, trying to isolate the most important processes, building simple models and only keeping those which agree with data, constitute a universal method that is also relevant to the study of urban systems.