A methodology to quantify the neighborhood decay effect of urban cellular automata models

A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit

“…It improves the simulation efficiency without compromising simulation accuracy, thereby making it widely applicable to grid CA models. For CA models with different neighborhood types, such as von Neumann neighborhood and circular neighborhood, and different neighborhood rules, such as the distance decay rule (Liao et al., 2014; Zeng, Wang, et al., 2023), although the functional forms of these neighborhood are different, essentially, they are all collecting information around the central cell to characterize local interaction effects. Consequently, regardless of the neighborhood configuration, the calculation process of neighborhood effect is a statistical process, that is, a weighted moving average is performed on the cell space, which is also a standard convolution process.…”

“…However, in the same study area, a higher resolution means an increase in the number of pixels, which leads to a significant increase in the computational intensity of the model and occupies more memory space. Additionally, with the development of CA modeling research, scholars have made a series of improvements to the CA model to enhance its simulation accuracy (Liao et al., 2014; Zeng, Wang, et al., 2023; Zhang & Wang, 2021). These improvements inevitably increase the computational complexity of the model.…”

“…In an urban CA model, neighborhood describes the local interaction between the central cell and the neighborhood cells (Clarke et al., 1997). It is precisely this local interaction mechanism that endows the CA model with the ability to simulate local self‐organizing evolution of complex systems (Zeng, Wang, et al., 2023). This interaction is a continuous and dynamic process.…”