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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. 2012 ACM Subject Classification Computing methodologies → Modeling methodologies

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