用于创建多属性地理显式合成种群的生成框架
A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population
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中文总结 AI 辅助
提出一种分层扩散生成框架，可从汇总数据重建多属性区域联合分布，生成含3.32亿个体的地理显式合成种群，效果优于IPF等基线方法，能提升地理模拟的真实性。
中文摘要 AI 辅助
生成具有真实联合分布和地理变异的多属性合成种群，是微观模拟、基于智能体的建模等地理模拟技术的基础需求。然而，现有方法仅从汇总层面数据重建特定区域的联合分布仍具挑战性。因此，我们提出一种基于分层扩散的生成框架，该框架以多属性的真实区域特定联合分布为训练目标，创建合成种群并为其分配明确的居住和工作地点。将该框架应用于美国50个州及华盛顿特区，生成了包含3.32387543亿个体、5项属性（如年龄、性别、就业状况、教育程度、收入）的全国性地理显式合成种群。保留区域的实验表明，与迭代比例拟合（IPF）和一次性扩散基线方法相比，该框架对联合分布的重建效果更好，同时地点分配保留了主要的居住和工作模式。因此，所提框架为在区域和国家层面创建地理显式合成种群提供了一种可扩展的生成方法。通过使用该框架重建这5项属性的区域特定联合分布，所得合成种群可为地理模拟（如基于智能体的建模）引入更真实的行为，从而能通过人类互动进一步探索复杂城市现象的涌现。
英文摘要
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.
