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This paper introduces a hierarchical diffusion-based generative framework that creates multi-attribute synthetic populations with realistic geographic variation, addressing the challenge of reconstructing region-specific joint distributions from aggregated data. By applying this method to 50 U.S. states and Washington, D.C., the framework generates a synthetic population of over 332 million individuals, accurately reflecting five key attributes while maintaining significant residential and workplace patterns. The results demonstrate improved reconstruction of joint distributions compared to traditional methods like Iterative Proportional Fitting, enhancing the realism of geo-simulations and agent-based modeling.
A new generative framework can synthesize over 332 million individuals with realistic geographic and demographic attributes, outperforming traditional methods in joint distribution reconstruction.
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.
