A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata

This paper proposes a spatiotemporal model of land use change based on ant colony optimization (ACO), Markov chain and cellular automata (CA). These three methodologies have previously been used separately or in pairs to simulate land use change. In this paper, we apply them in combination, using ant colony optimization and cellular automata to manage the spatial distribution of land use, and applying Markov chain and cellular automata to manage the total amount of land use coverage. We first describe the principle and implementation of the model. Then a land use map of an experimental area (Changping, a district of Beijing) based on land use maps from 1988 and 1998 is simulated for 2008 using the model. By analyzing with real situation, accuracy of the simulation result manifests that the model is useful for land use change simulation. And compared with the other two models (CA–Markov model and ACO–CA model), the model is more appropriate in predicting both the quantity and spatial distribution of land use change in the study area. Therefore the model proposed by this paper is capable of simulating land use change.

► Introduce a spatiotemporal model of land use change.
► To manage the spatial distribution of land use, the model uses ant colony optimization and cellular automata to discover the local transition rules.
► To control the total amount of land use coverage, the model uses Markov process to predict the transition area matrix of land use.