快速城市化给可持续发展目标带来了巨大挑战，如资源过度开发和人口激增。传统的元胞自动机（CA）模型被广泛用于独立模拟空间特征的变化，例如土地利用、人口分布、经济产出等。然而，大多数CA模型依赖历史数据作为静态驱动因子来预测未来情景，忽略了发展过程中多种特征之间的相互耦合影响。为解决这一问题，本研究提出一种空间协同模拟（SCS）方法，用于模拟土地利用、人口与经济的变化。SCS方法首先通过独立的CA模型获取各特征的初始状态；随后，将其他两个特征的模拟结果作为动态更新的驱动因子，而非静态的历史数据，以捕捉多特征在发展过程中的相互耦合效应。该过程迭代进行，直至各特征变化收敛，最终输出模拟结果。在粤港澳大湾区的模拟实验表明，SCS方法能够有效捕捉多要素的协同演化过程，优于基准方法。该方法具备预测未来发展趋势的能力，有助于支持空间规划与基础设施协同发展。

Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies.