Multiscale Entropy in the Spatial Context of Cities

TL;DR

This paper introduces a multiscale entropy framework to analyze urban spatial complexity, linking microscopic decisions to macroscopic city morphology, and demonstrates its application through simulations and a case study of West London.

Contribution

It provides the first interpretation of entropy for cities that captures multiscale interactions, explaining urban complexity and morphological patterns.

Findings

Structural complexity can be the most probable configuration in multiscale spatial systems.

Higher entropy explains polycentric sprawl in West London.

The framework can inform urban planning under uncertainty.

Abstract

Entropy relates the fast, microscopic behaviour of the elements in a system to its slow, macroscopic state. We propose to use it to explain how, as complexity theory suggests, small scale decisions of individuals form cities. For this, we offer the first interpretation of entropy for cities that reflects interactions between different places through interdependently linked states in a multiscale approach. With simulated patterns we show that structural complexity in spatial systems can be the most probable configuration if the elements of a system interact across multiple scales. In the case study that observes the distribution of functions in West London from 1875 to 2005, we can partly explain the observed polycentric sprawl as a result of higher entropy compared to spatially random spread, compact mixed use growth or fully segregated patterns. This work contributes to understanding why cities are morphologically complex, and describes a consistent relationship between entropy and complexity that accounts for contradictions in the literature. Finally, because we evaluate the constraints urban morphology imposes on possible ways to use the city, the general framework of thinking could be applied to adjust urban plans to the uncertainty of underlying assumptions in planning practice.