UniGCA: a universal graph cellular automata framework for both raster- and vector-based urban growth simulation

Urban growth simulation is crucial for sustainable spatial planning and policy-making. Cellular automata (CA) are among the most widely used approaches, and existing models can be categorized into raster-based (RCA) and vector-based (VCA) paradigms, in which structural differences hinder integration and transferability of available models. Here, a universal graph-based cellular automata (UniGCA) framework is proposed to equally support these two distinct paradigms and address the challenges in spatial structure design and spatial interaction modeling. Building on the UniGCA framework, the Partitioned Graph U-Net Cellular Automata (PGUN-CA) model is developed as a practical implementation. PGUN-CA employs optimized graph storage and multilevel graph partitioning to overcome memory and computational constraints in large-scale urban simulation, while integrating Graph U-Net with attention mechanisms to capture complex spatial interaction. Case studies in Shanghai (raster data) and the Pine Catchment, Australia (vector data), demonstrate that PGUN-CA achieves higher accuracy than baseline models, with figure of merit (FoM) improvements of 6.96% and 9.41%. These findings highlight the generality and scalability of the UniGCA framework, which will provide a practical and flexible solution for large-scale urban growth simulation across heterogeneous data types and solid support for urban planning and sustainable development.