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Modeling structural change in spatial system dynamics: A Daisyworld example.

C Neuwirth1, A Peck2, S P Simonović2

  • 1Doctoral College GIScience, University of Salzburg, Salzburg, Austria ; Department of Geography, University of Munich (LMU), Munich, Germany.

Environmental Modelling & Software : with Environment Data News
|June 26, 2015
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Summary
This summary is machine-generated.

System dynamics (SD) modeling now integrates spatial data using Geographic Information Systems (GIS). This approach enhances understanding of complex systems by incorporating spatio-temporal feedbacks, crucial for structural change simulations.

Keywords:
Process and structureSpatial system dynamicsStructural feedbackTight coupling

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Area of Science:

  • Environmental modeling
  • Computational science
  • Geographic Information Systems

Background:

  • System dynamics (SD) effectively models temporal behavior but often lacks spatial dependency.
  • Existing spatial SD models are primarily limited to simulating diffusion processes.
  • Structural change models, like Land Use/Land Cover (LULC) modeling, particularly need enhanced spatial capabilities.

Purpose of the Study:

  • To develop a Python program coupling SD software with GIS for bidirectional, synchronized interactions.
  • To address the limitations of non-spatial approaches in simulating structural changes.
  • To demonstrate the importance of spatio-temporal feedbacks in complex system modeling.

Main Methods:

  • Developed a Python program to tightly couple System Dynamics (SD) software with Geographic Information Systems (GIS).
  • Implemented bidirectional and synchronized interactions between SD and GIS operations.
  • Recreated the 'Daisyworld' environment in a spatial system dynamics (SSD) setting to simulate structural changes.

Main Results:

  • The coupled SD-GIS approach enables robust handling of spatial dependencies in complex systems.
  • Simulations in the spatial 'Daisyworld' highlighted the critical role of spatio-temporal feedbacks.
  • Comparison of spatial and non-spatial simulations underscored the necessity of integrated approaches.

Conclusions:

  • The proposed SD-GIS coupling method significantly advances the simulation of structural changes.
  • Spatial system dynamics (SSD) is vital for accurately modeling environmental and societal dynamics.
  • This integrated approach has practical applications in agriculture and disaster management.