Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this study, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) to fill the gap. SynC first removes potential outliers in the data and then fits the filtered data with a Gaussian copula model to correctly capture dependencies and marginal distributions of sampled survey data. Finally, SynC leverages neural networks to merge datasets into one and then scales them accordingly to match the marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) design a metric for validating the accuracy of generated data when the ground truth is hard to obtain, 3) demonstrate its effectiveness with the Canada National Census data and presenting two real-world use cases where datasets of this nature can be leveraged by businesses, and 4) release an easy-to-use framework implementation for reproducibility.

本研究提出了一个名为SynC(Synthetic Population via Gaussian Copula)的多阶段框架，以从聚合数据源中生成个人级数据，并证明其价值不仅是特征工程工具，而且在收集困难的情况下，还可以作为一种收集数据的替代方法。通过两个真实数据集，本研究展示了其功能，并发布了易于使用的框架实现，并确保该方法可以扩展到生产级别并可以轻松集成新数据。

SynC: 一种使用高斯Copula生成合成人口的统一框架