Urban transfer entropy across scales

TL;DR

This paper investigates how information exchange across different spatial and temporal scales influences urban morphology, using transfer entropy within a stochastic fractal model to simulate urban growth and policy impacts.

Contribution

It introduces an information theoretic framework to quantify scale interactions in urban systems using transfer entropy and a novel stochastic fractal model.

Findings

Different policies influence information flow across scales.

Urban morphology is affected by inter-scale information exchange.

The model reveals scale-dependent urban growth patterns.

Abstract

The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.