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The Use of Particle Swarm Optimization for a Vector Cellular Automata Model of Land Use Change (Short Paper)

Authors Yi Lu , Shawn Laffan

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ACM Subject Classification
  • Computing methodologies → Modeling methodologies
Keywords
  • Vector cellular automata (CA)
  • Particle swarm optimization (PSO)
  • Land use simulation
  • Ipswich

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Abstract

Cellular automata (CA) is an important area of research in GIScience, with recent research developing vector-based models in addition to the traditional raster data formats. One active area of research is the calibration of transition rules, particularly when applied to vector CA. Here we evaluate a particle swarm optimization (PSO) process to calibrate a vector CA model of land use change for a sub-region of Ipswich in Queensland, Australia, for the period 1999-2016. We compare the results with those for a raster CA of the same dataset. The spatial indices of the vector PSO-CA model exceed that of the raster model, with spatial accuracies being 82.45% and 76.47%, respectively. In addition, the vector PSO-CA model achieved a higher kappa coefficient. Vector-based PSO-CA model can be used for the exploration of urbanization process and provide a better understanding of land use change.

Cite As Get BibTex

Yi Lu and Shawn Laffan. The Use of Particle Swarm Optimization for a Vector Cellular Automata Model of Land Use Change (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 42:1-42:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.42

Author Details

Yi Lu
  • University of New South Wales, Sydney, Australia
Shawn Laffan
  • University of New South Wales, Sydney, Australia

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