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

2025-01-27

AI Summary

Synthetic population models for urban social simulation suffer from inadequate privacy protection, biased underrepresentation of marginalized subpopulations (e.g., low-income or immigrant groups), and insufficient geographic fidelity. Method: We propose the first Wasserstein GAN framework for multi-city spatial synthetic population generation. It integrates EU-SILC microdata with weighted sampling, spatially constrained modeling, and statistical calibration—novelly incorporating EU-SILC survey weights and external demographic constraints to explicitly mitigate systematic underrepresentation of rare subgroups. Results: Evaluated on Helsinki and Thessaloniki, our framework generates high-fidelity synthetic populations that significantly improve distributional consistency, enhance representativeness of marginalized groups, and prevent simulated discrimination. It delivers a reproducible, scalable, and fairness-aware paradigm for privacy-preserving, agent-based urban simulation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• Wasserstein Generative Adversarial Networks
• Urban Population Modeling
• Privacy Protection

Innovation

• Wasserstein GAN
• Privacy Protection
• Deep Learning Limitations