# SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula

arXiv: https://arxiv.org/abs/1904.07998 · HTML: https://mlsanity.com/paper/1904.07998/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models](https://mlsanity.com/paper/2502.11059/) (2025)
- [Utility Theory of Synthetic Data Generation](https://mlsanity.com/paper/2305.10015/) (2023)
- [CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction](https://mlsanity.com/paper/2506.17326/) (2025)
- [Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population](https://mlsanity.com/paper/2304.12284/) (2023)

## Abstract (arXiv)

Synthetic population generation is the process of combining multiple socioeconomic and demographic datasets from different sources and/or granularity levels, 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 predictive models to merge datasets into one and then scales them accordingly to match the marginal constraints. We make three 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) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 3) release an easy-to-use framework implementation for reproducibility, and 4) ensure the methodology is scalable at the production level and can easily incorporate new data.