A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population

2026-08-12

AI Summary

Existing methods struggle to reconstruct geographically heterogeneous joint distributions of multiple attributes from aggregated data alone, limiting the realism of microsimulation. This work proposes a hierarchical diffusion generative framework that, for the first time, integrates hierarchical diffusion mechanisms into synthetic population generation, targeting region-specific joint attribute distributions as the training objective to simultaneously generate individual characteristics along with precise residential and workplace locations. By combining regional joint distribution modeling with geographic allocation algorithms, the approach surpasses traditional iterative proportional fitting (IPF) and single-step diffusion baselines in both fidelity and scalability. Experiments successfully generate a nationwide synthetic population covering all 50 U.S. states and the District of Columbia, comprising 332 million individuals, significantly outperforming existing methods in reconstructing joint distributions and preserving geographic patterns.

Abstract

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.

Problem

• synthetic population
• joint distribution
• geographic variation
• geo-simulation
• micro-simulation

Innovation

• synthetic population
• geographically-explicit
• hierarchical diffusion
• joint distribution reconstruction
• agent-based modeling