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Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems. [ABSTRACT FROM AUTHOR]

Interpretations of the urban cellular automata (CA) model aim to ensure that its predictive behaviors are consistent with real-world processes. Current urban CA interpretations have revealed the impacts of driving factors on land development suitability, or neighborhood effects and random perturbation on simulation results. However, three limitations remain unresolved: (1) the interpretations of deep learning (DL)-based urban CA are seldom integrated with the prerequired feature selection, (2) the input features from different urban CA modules are still explained by separate approaches, and (3) the interpretation results are rarely derived at the cell level to uncover spatially varying urban land development patterns. This study proposes a SHapley Additive exPlanations (SHAP)-based urban CA interpretation framework to address these challenges and improve urban CA. This framework uses model-level SHAP importance to identify dominant features from different modules for constructing the final simulation model. Then, cell-level SHAP importance is used to uncover spatially varying driving forces of urban expansion. The framework's effectiveness is rigorously tested and confirmed using a convolution neural network CA (CNN-CA) model for Dongguan City. The experimental results demonstrate that (1) SHAP-based model interpretation improves feature selection for DL-based urban CA. The figure of merit for CNN-CA calibrated using SHAP-based important features improves by 3%, outperforming the tested baseline methods. (2) SHAP measures the impacts of each feature from different CA modules in a whole. In this case, physical factors are much more important at the model level than proximity and accessibility factors, while neighborhood effect is the second most crucial factor. (3) Cell-level SHAP interpretations uncover spatially different urban land development patterns. For example, due to the extensive industrial land development in the northern Songshan Lake Zone, in the CNN-CA model, proximity to major roads within this region is associated with positive SHAP-based contribution share on cell-level urban expansion. [Display omitted] • SHAP interpretation and deep learning based urban CA are integrated to explain the simulation mechanism. • SHAP interpretation facilitates the model feature selection and improves the accuracy of urban CA. • Model-level SHAP interpretations globally quantify the impacts of input features from different CA modules. • Cell-level SHAP interpretations reveal spatial differences of local urban land development patterns. [ABSTRACT FROM AUTHOR]

Since urban growth results from frequent spatial interaction between urban units, adequate representation of spatial interaction is important for urban growth modeling. Among urban growth models, vector-based cellular automata (VCA) excels at expressing spatial interaction with realistic entities, and has accordingly been used extensively in recent studies. However, two issues with VCA modeling still remain: 1) inefficient manual selection of interaction targets with various neighborhood configurations; 2) inaccurate quantification of interaction intensity due to ignorance of spatial heterogeneity in entity interaction. To address these two limitations, this study proposed a novel VCA model with high-order graph attention network (HGAT-VCA). In this model, a graph structure is first built from the topology adjacency relationship between cadastral parcels. In terms of the HGAT components, the original 1 st -order parcel neighborhood is extended to high-order to capture the distant dependency, while graph attention is applied to quantify the heterogeneous interaction intensity between parcels. Finally, the conversion probability obtained by HGAT is integrated with VCA to simulate urban land use change. Land use data from the Moreton Bay Region in Queensland, Australia from 2005 to 2009 are selected to verify the proposed HGAT-VCA model. Experimental results illustrate that HGAT-VCA outperforms four classical CA models and achieves the highest simulation accuracy (e.g., the increase of FoM is about 40.7%). In addition, extensive neighborhood configuration experiments show that with HGAT only tuning discrete topological order can generate similar accuracy results compared with the repetitive buffer-based neighborhood configuration, and this can significantly improve the calibration efficiency of VCA models. • VCA is integrated with graph neural network to accurately model spatial interaction in urban growth. • High-order neighborhood extension is conducted to extract distant dependency between parcels. • Graph attention is applied for adaptive target selection and intensity quantification of spatial interaction. • HGAT with discrete topological order tuning can highly improve the efficiency of model calibration. [ABSTRACT FROM AUTHOR]