A new type of dual-scale neighborhood based on vectorization for cellular automata models

Abstract

Although the neighborhood of the cellular automata (CA) model has been studied in detail, there is a contradiction in the selection of the neighborhood size that has not been revealed and addressed. The contradiction is that small neighborhoods can constrain the shape complexity of the simulated landscape, but they cannot sufficiently characterize the local interactions, while large neighborhoods do the opposite. In this paper, we propose a new type of dual-scale neighborhood (DSN) based on vectorization to avoid this contradiction. Taking Beijing, Wuhan, and the Pearl River Delta in China as study areas, two kinds of CA models, namely, the CA model using the original neighborhood (ORN-CA) and the CA model using the proposed DSN (DSN-CA), were constructed based on the serial/scalar algorithm and the vectorized algorithm, respectively. The comparison of the simulation results and the time taken shows that the DSN enables the user to choose the appropriate neighborhood configuration to obtain high-accuracy simulation results and a landscape that is similar to the ground truth. The vectorization can also greatly improve the computational efficiency of the neighborhood effects. Overall, the findings show that integrating the DSN with vectorization can significantly improve the simulation performance and efficiency of CA models.