基于地理坐标的人口合成
Population synthesis with geographic coordinates
AI总结：
提出基于NF+VAE的人口合成算法，将地理坐标映射至潜在空间以生成精细分辨率的合成人口，并在121个数据集上验证其优于基准方法，同时确保隐私保护。
摘要：
生成具有明确坐标而非粗略地理区域的合成人口日益重要，但目前尚无成熟方法实现这一目标。原因之一是经纬度与其他连续变量不同，存在大片空白空间且密度极不均匀。为此，我们提出一种人口合成算法，首先使用 Normalizing Flows (NF) 将空间坐标映射到更规则的潜在空间，然后在 Variational Autoencoder (VAE) 中将其与其他特征结合以生成合成人口。该方法还学习空间与非空间特征之间的联合分布，利用空间自相关性。我们在121个数据集中生成了与真实家庭具有相同统计特性的合成家庭，涵盖多样化的地理环境。我们进一步提出一个评估框架，同时衡量空间准确性和实用性，并确保隐私保护。结果表明，NF+VAE架构优于流行基准方法，包括基于copula的方法和地理区域内的均匀分配。生成具有精细空间分辨率的地理定位合成人口的能力，为需要详细地理信息的应用打开了大门，从家庭对洪水的响应到流行病传播、疏散规划和交通建模。
英文摘要：
It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population synthesis algorithm that first maps spatial coordinates into a more regular latent space using Normalizing Flows (NF), and then combines them with other features in a Variational Autoencoder (VAE) to generate synthetic populations. This approach also learns the joint distribution between spatial and non-spatial features, exploiting spatial autocorrelations. We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets, corresponding to diverse geographies. We further propose an evaluation framework that measures both spatial accuracy and practical utility, while ensuring privacy preservation. Our results show that the NF+VAE architecture outperforms popular benchmarks, including copula-based methods and uniform allocation within geographic areas. The ability to generate geolocated synthetic populations at fine spatial resolution opens the door to applications requiring detailed geography, from household responses to floods, to epidemic spread, evacuation planning, and transport modeling.
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