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Urban Transfer Entropy across Scales.
Roberto Murcio1, Robin Morphet1, Carlos Gershenson2
1Centre for Advanced Spatial Analysis, University College London, London, United Kingdom.
Plos One
|July 25, 2015
Summary
Urban migration pathways influence city growth across scales. Understanding information flow helps predict how urban policies shape settlement distribution and morphology.
Area of Science:
- Urban studies
- Complexity science
- Spatial analysis
Background:
- Urban agglomeration morphology is influenced by information exchange across spatio-temporal scales.
- Urban migration exhibits non-random patterns, impacting settlement dynamics.
- Cities are complex, non-linear systems requiring analysis of inter-scale relationships.
Purpose of the Study:
- To investigate information exchange between different spatio-temporal scales in urban agglomeration.
- To quantify the relationships between local/regional urban policies and the distribution of urban settlements.
- To understand how policy interventions affect urban morphology through information flow.
Main Methods:
- Utilizing an information theoretic approach, specifically Transfer Entropy.
- Employing a stochastic urban fractal model to simulate urban growth and migration.
- Analyzing information generation across geographical scales.
Main Results:
- Migration patterns are non-random and follow predictable pathways.
- The study quantifies information transfer across scales in urban systems.
- Results demonstrate the impact of policies on urban morphology via information dynamics.
Conclusions:
- Inter-scale information exchange is crucial for understanding urban morphology.
- Urban fractal models provide insights into settlement dynamics.
- Policy decisions can be informed by analyzing information flow across geographical scales to influence urban form.
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