Spatially aggregated data on socio-demographic groups often fail to capture the population's spatial heterogeneity in cities. This poses challenges for urban planning, particularly when addressing the needs of groups such as migrants or families with children. Moreover, the commonly provided aggregated units, such as census tracts, vary in size and across data sources. Existing literature on disaggregation typically handles individual subgroups separately, ignoring their interrelations in the downscaling process. This article explores the potentials of multi-output regression models for simultaneous spatial downscaling of multiple groups and conducts a detailed spatial error analysis using individualized neighborhoods. We experiment with self-training gradient-boosting trees and fully convolutional neural networks, assessing the quality of results against ground truth data at the target resolution. We show that the evaluation of the disaggregated results at this detailed resolution requires unconventional methods. The methodology proves convenient and achieves high-accuracy results using input datasets of building features.

中文翻译：

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人口分布建模：对多输出空间分解的梯度提升和深度学习模型进行可视化和定量分析

关于社会人口群体的空间聚合数据通常无法捕捉城市人口的空间异质性。这给城市规划带来了挑战，特别是在满足移民或有孩子的家庭等群体的需求时。此外，通常提供的聚合单位（例如人口普查区）的大小和数据源各不相同。现有的分解文献通常单独处理各个子组，忽略它们在降尺度过程中的相互关系。本文探讨了多输出回归模型同时空间缩小多个组的潜力，并使用个性化邻域进行了详细的空间误差分析。我们尝试使用自训练梯度增强树和全卷积神经网络，根据目标分辨率的地面实况数据评估结果的质量。我们表明，在这种详细分辨率下评估分类结果需要非常规方法。该方法被证明是方便的，并且使用建筑特征的输入数据集获得了高精度的结果。