Generating Spatial Synthetic Populations Using Wasserstein Generative Adversarial Network: A Case Study with EU-SILC Data for Helsinki and Thessaloniki

TL;DR

This paper demonstrates how Wasserstein GANs can generate realistic, spatially distributed synthetic populations from census data, aiding urban simulations while addressing privacy and bias issues.

Contribution

It introduces a novel application of Wasserstein GANs for creating spatial synthetic populations using EU-SILC data for Helsinki and Thessaloniki.

Findings

GANs can produce demographic profiles with high fidelity.

Challenges include balancing data and avoiding bias against fringe profiles.

Synthetic populations may under-represent marginalized groups.

Abstract

Using agent-based social simulations can enhance our understanding of urban planning, public health, and economic forecasting. Realistic synthetic populations with numerous attributes strengthen these simulations. The Wasserstein Generative Adversarial Network, trained on census data like EU-SILC, can create robust synthetic populations. These methods, aided by external statistics or EU-SILC weights, generate spatial synthetic populations for agent-based models. The increased access to high-quality micro-data has sparked interest in synthetic populations, which preserve demographic profiles and analytical strength while ensuring privacy and preventing discrimination. This study uses national data from Finland and Greece for Helsinki and Thessaloniki to explore balanced spatial synthetic population generation. Results show challenges related to balancing data with or without aggregated statistics for the target population and the general under-representation of fringe profiles by deep generative methods. The latter can lead to discrimination in agent-based simulations.