Bin Zhang

Capturing patch features can significantly enhance the performance of cellular automata (CA)‐based land use modeling. However, current research has yet to comprehensively explore the definition of patch‐based CA simulation rules and their integration with grid‐based rules. This study proposes a generalized urban CA framework that integrates grid‐ and patch‐based rules across scales. Using Beijing's urban growth from 2000 to 2020 as a case study, we evaluated the simulation performance of CA under different rule‐integration modes. The results demonstrate that patch‐level assessment of urban growth potential improves the model's temporal generalizability, robustness, and accuracy. However, using patches as cells for local interactions reduces simulation performance and efficiency, whereas grid‐based neighborhoods produce better results by more closely resembling complex boundary buffer neighborhoods. Furthermore, treating patches as basic units for urban expansion control enhances simulated urban morphology and accuracy. Integrating these optimal rules across scales within the proposed framework yields the best‐performing CA model. This study offers a methodological reference for grid‐patch integration in land use modeling, which can facilitate pre‐assessing urban growth‐induced land degradation risks and achieving reasonable spatial planning, supporting sustainable urban development.

Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of small-scale urban growth simulation to model urban agglomeration growth to assist in carbon-neutral planning, ignoring the significant characteristics of the process to achieve carbon neutrality: large-scale and long-term. This paper proposes a framework to model large-scale and long-term urban growth, which couples a quantity module and a spatial module to model the quantity and spatial allocation of urban land, respectively. This framework integrates the inertia of historical land-use change, the driving effects of the urbanization law (S-curve), and the traction of the urban agglomeration network to model the long-term quantity change of urban land. Moreover, it couples a partitioned modeling framework, spatially heterogeneous rules derived by geographically weighted regression (GWR), and quantified land-use planning orientations to build a cellular automata (CA) model to accurately allocate the urbanized cells in a large-scale spatial domain. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) as an example, the proposed framework is calibrated by the urban growth from 2000 to 2010 and validated by that from 2010 to 2020. The figure of merit (FoM) of the results simulated by the framework is 0.2926, and the simulated results are also assessed by some evidence, which both confirm the good performance of the framework to model large-scale and long-term urban growth. Coupling with the coefficients proposed by the Intergovernmental Panel on Climate Change (IPCC), this framework is used to project the carbon emissions caused by urban growth in the GHMGBA from 2020 to 2050. The results indicate that Guangzhou, Foshan, Huizhou, and Jiangmen are under great pressure to achieve the carbon-neutral targets in the future, while Hong Kong, Macao, Shenzhen, and Zhuhai are relatively easy to bring up to the standard. This research contributes to the ability of land-use models to simulate large-scale and long-term urban growth to predict carbon emissions and to support the carbon-neutral planning of the GHMGBA.

Urban sprawl is a typical geographic dynamic process with spatial heterogeneity and nonlinearity. However, current studies usually focus on only one of them to extract urban sprawl mechanisms and build cellular automata (CA) models. In the current work, the urban CA transition rules are derived by a geographically weighted artificial neural network (GWANN), which can discover the driving mechanism of urban sprawl by considering both spatial heterogeneity and nonlinearity. Taking the urban sprawl of Wuhan and Beijing during 2000–2020 as examples, the advantages of GWANN in deriving transition rules are investigated by comparing it with logistic regression (LR), geographically weighted logistic regression (GWLR), and artificial neural network (ANN). Furthermore, the simulation performance of CA models based on LR, GWLR, ANN, and GWANN is compared and analyzed from the aspects of global and regional simulation accuracy and the morphology of simulated urban patches. The results show that GWANN has better fitting and simulation performance, indicating the validity and necessity of coupling spatial heterogeneity and nonlinearity to establish transition rules. This study is a novel exploration that contributes to deriving CA transition rules through a hybrid modeling approach that couples statistical models with learning models.