用生成对抗网络创建高保真城市人口模型，兼顾隐私与代表性。
Generating Spatial Synthetic Populations Using Wasserstein Generative Adversarial Network: A Case Study with EU-SILC Data for Helsinki and Thessaloniki
- 基于欧统局数据训练WGAN，生成带多属性的虚拟居民。
- 模型在边缘群体代表上存在不足，易导致模拟偏差。
- 适合城市规划、公共政策等需隐私保护模拟的研究者。
基于代理的社交模拟可提升对城市规划、公共卫生和经济预测的理解。具备丰富属性的真实合成人口能增强这些模拟效果。利用在欧盟统计调查（EU-SILC）等普查数据上训练的水印生成对抗网络（Wasserstein GAN），可生成稳健的合成人口。结合外部统计数据或EU-SILC权重，该方法能为基于代理的模型生成空间合成人口。随着高质量微观数据获取日益便捷，合成人口因其在保持人口特征和分析能力的同时保障隐私、防止歧视，受到广泛关注。本研究以芬兰赫尔辛基和希腊塞萨洛尼基的国家数据为基础，探索均衡的空间合成人口生成。结果表明，在有无聚合统计信息条件下，平衡目标人口数据存在挑战；且深度生成方法普遍存在边缘群体代表性不足的问题，可能在基于代理的模拟中引发歧视性后果。
原文摘要 · Abstract (English)
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.
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