This study demonstrates that graph construction is a critical modelling decision in spatial GNN forecasting by introducing and systematically evaluating five alternative spatial graph designs. The findings show that selecting or combining graph structures to reflect local mobility and spatial configurations can substantially improve predictive performance, underscoring the need to tailor graph representations to urban context.