Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia

Yi Lu, Shawn Laffan, Chris Pettit and Min Cao
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Yi Lu: University of New South Wales, Australia
Shawn Laffan: University of New South Wales, Australia
Chris Pettit: University of New South Wales, Australia

Environment and Planning B, 2020, vol. 47, issue 9, 1605-1621

Abstract: The loss of accuracy in vector-raster conversion has always been an issue for land use change models, particularly for raster based Cellular Automata models. Here we describe a vector-based cellular automata (CA) model that uses land parcels as the basic unit of analysis, and compare its results with a raster CA model. Transition rules are calibrated using an artificial neural network (ANN) and historical land use data. Using Ipswich City in Queensland, Australia as the study area, the simulation results show that the vector and raster CA models achieve 96.64% and 93.88% producer’s spatial accuracy, respectively. In addition, the vector CA model achieves a higher kappa coefficient and more consistent frequency of misclassification, while also having faster processing times. Consequently, the vector-based CA model can be applied to explore regulations of land use transformation in urban growth process, and provide a better understanding of likely urban growth to inform city planners.

Keywords: Land use change; vector-based cellular automata; urban growth; artificial neural network; Ipswich City (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:47:y:2020:i:9:p:1605-1621

DOI: 10.1177/2399808319830971

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