Search

Spatial heterogeneity is an essential feature of land use change. It is important for cellular automata (CA)-based urban expansion modeling to consider the spatial heterogeneity of neighborhood effect. According to the first law of geography, there exists a distance decay effect in the neighborhood space of the urban CA models. But no research has ever been able to accurately quantify the neighborhood decay effect and apply it to models. In this study, we propose a methodology to accurately quantify the neighborhood decay effect of CA models with large neighborhood size, including the design of neighborhood configuration and the selection and optimization of decay strategies. Taking Wuhan and Guangzhou as examples, we show the implementation process of the methodology and simulate urban expansion of two areas for the periods 2000–2010 and 2010–2015 by CA models with distance decay neighborhood to reveal the advantages and characteristics of modeling the neighborhood decay effect. The results show that the simulation accuracy of CA models with the spatial heterogeneous neighborhood and the hybrid neighborhood (HYN) is higher than that of CA models with homogeneous neighborhood, and in most cases, the HYN can more accurately describe the neighborhood decay effect. [ABSTRACT FROM AUTHOR]

• The spatial neighborhood stationarity hypothesis becomes a limitation of CA modeling. • A neighborhood size-adaptive strategy for CA-based urban growth modeling is proposed. • This strategy considers spatial heterogeneity to define the size of CA neighborhood. • The SAN is superior to the ORN and the DSN in characterizing CA neighborhood effects. 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. [ABSTRACT FROM AUTHOR]

Urban modelers have long interpreted urban growth as a coupled evolution of two processes, namely spontaneous and self-organized processes. However, most scholars have always paid attention to the exploration of the driving mechanisms of the spontaneous process. While for the self-organized process, most simulations based on cellular automata (CA) simply abstract it as the clustering process between land use types, ignoring the important role of human behaviors in urban growth. This study presents a framework that integrates a size-adaptive strategy with the multi-agent system (MAS) to establish a neighborhood with stakeholders' interactions (NSI). We employed the CA with NSI (Nsi-CA) to simulate the urban growth of Wuhan from 2000 to 2020 and analyzed its performance. The findings indicate that Nsi-CA performs better than the commonly used CA models at both local and global scales, with an increase in global Figure of Merit (FoM) of about 11.5 %. Moreover, the effectiveness of parameters controlling stakeholders' interactions in the NSI was revealed through a sensitivity analysis. By setting different scenarios with these parameters, the future urban land patterns of Wuhan in 2035 were predicted and used to provide planning suggestions for its future development. • A neighborhood with stakeholders' interactions (NSI) was proposed for urban CA modeling. • MAS was incorporated into the CA neighborhood to depict the self-organized process. • Nsi-CA is able to simulate self-organized urban growth influenced by human behavior. • Predicting urban growth through Nsi-CA can effectively serve practical urban planning. [ABSTRACT FROM AUTHOR]