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This paper proposes a nested spatiotemporal forecasting framework that uses spectral clustering to construct semantically coherent macro-level regions, which provide top-down guidance for fine-grained micro-level predictions. Experiments on high-dimensional datasets show consistent improvements over state-of-the-art baselines.