Generating multi-attribute synthetic populations with realistic joint distributions and geographic variation is a foundational requirement for geo-simulation techniques, such as micro-simulation and agent-based modeling. However, it remains challenging for existing methods to reconstruct region-specific joint distributions from aggregated-level data alone. Thus, we propose a hierarchical diffusion-based generative framework that utilizes a realistic region-specific joint distribution of multiple attributes as the training target to create a synthetic population along with assigning their explicit home and work locations. Applied to 50 U.S. states and Washington, D.C., this framework generates a nationwide geographically-explicit synthetic population consisting of 332,387,543 individuals with five attributes (e.g., age, gender, employment, education, income). Held-out regional experiments show improved reconstruction of joint distributions relative to Iterative Proportional Fitting (IPF) and a one-shot diffusion baseline. At the same time, the location assignment preserves major residential and workplace patterns. As such, the proposed framework provides a scalable generative approach for creating geographically explicit synthetic populations at both regional and national levels. By reconstructing region-specific joint distributions of these five attributes using this framework, the resulting synthetic population could introduce more realistic behaviors into geo-simulations, such as agent-based modeling, enabling further exploration of the emergence of complex urban phenomena through human interactions.

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