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Related Experiment Videos

Urban growth simulation from "first principles".

Claes Andersson1, Kristian Lindgren, Steen Rasmussen

  • 1EES-6 MS T003, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 and Physical Resource Theory, Chalmers University of Technology, 412 96 Göteborg, Sweden. andersson@lanl.gov

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 21, 2002
PubMed
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This study introduces a new urban growth model with realistic microscopic dynamics, offering insights into urban sprawl. The model

Area of Science:

  • Urban planning and computational social science.
  • Mathematical modeling of complex systems.

Background:

  • Existing general urban growth models lack microscopic realism.
  • Physical models like diffusion-limited aggregation have limited applicability to urban dynamics.
  • Urban sprawl, characterized by low-density land use, presents significant global challenges.

Purpose of the Study:

  • To develop a novel urban growth model with realistic microscopic dynamics.
  • To simulate and analyze characteristic urban morphologies, including urban sprawl.
  • To provide a framework for understanding human decisions and policy impacts on urban development.

Main Methods:

  • Development of an urban growth model based on Markov random fields.
  • Simulation of urban growth with realistic microdynamics.

Related Experiment Videos

  • Statistical analysis of generated urban macrostates and comparison with empirical data.

    • Main Results:

    • The proposed model successfully reproduces key urban morphologies.
    • Microdynamics are mappable to human decisions, offering policy relevance.
    • Simulations incorporating diverse land use classes and topography demonstrate model versatility.

    Conclusions:

    • The Markov random field model offers a realistic approach to simulating urban growth.
    • The model provides valuable insights into the dynamics of urban sprawl.
    • This framework supports applied urban planning by linking micro-level decisions to macro-level outcomes.