A size-adaptive strategy to characterize spatially heterogeneous neighborhood effects in cellular automata simulation of urban growth

Abstract

As an important component of the cellular automata (CA) model, the neighborhood defines the geographical interaction domain (GID) of cells and their interaction mechanisms. However, the invocation of “spatial neighborhood stationarity” leads to the CA neighborhood being spatially homogeneous, which apparently violates the spatial heterogeneity of geographical dynamics. This study proposes a size-adaptive strategy for the definition of the CA neighborhood, which makes its size vary with the cell location. The CA model with size-adaptive neighborhood (SAN) has been constructed by coupling a defined size sequence with the distribution of cell GID approximated by the local accessibility distribution. Taking the urban growth of Wuhan from 2000 to 2020 as an example, the CA models with original neighborhood (ORN), dual-scale neighborhood (DSN), and SAN have been compared to investigate the performance of the SAN. The results show that the SAN is superior to the ORN and DSN in the characterization of the local interactions of urban growth, and the SAN-CA model can produce a more regular urban landscape and more accurate simulation results. Besides, the applicability of serial and parallel algorithms to the size-adaptive strategy has been detected by comparing their computational efficiency. Although the serial algorithm is easier to perform the size-adaptive strategy, the high computational efficiency of the parallel algorithm makes it more suitable for calculating the SAN effect. This study contributes to superseding the hypothesis of “spatial neighborhood stationarity” and improving the ability of CA models to simulate urban growth with spatial heterogeneity.