Contemporary investigations into cellular automata (CA) modeling often neglect the considerable influence of spatial heterogeneity on both spontaneous and self-organized processes of urban growth. In this research, we combined a partitioned quantity control strategy, the geographically weighted artificial neural network (GWANN), and a neighborhood size-adaptive approach to formulate a CA framework for simulating spatially heterogeneous spontaneous and self-organized urban growth (SHSS-CA). We examined the simulation performance of SHSS-CA in Beijing, Shanghai, and the Pearl River Delta during 2000–2010 for calibration and 2010–2020 for validation. The findings demonstrate that incorporating spatial heterogeneity enhances CA performance in approximately 90% of regions. Introducing spatially heterogenous spontaneous or self-organized urban growth rules alone can improve the simulation performance of CA. Furthermore, their integration leverages the strengths of both rules and makes SHSS-CA the optimal choice, resulting in a notable improvement in the figure of merit (FoM)—approximately 9% during calibration and around 5% during validation—when compared with ANN-CA. This study contributes new methodologies for developing urban growth simulation rules and has the capacity to effectively help urban planners understand and analyze the complicated urban growth processes.