Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infrastructure, development policies, etc. Dissagregating these census is a complex machine learning, and multiple solutions have been proposed in past research. We propose in this paper to view the problem in the context of the aggregate learning paradigm, where the output value for all training points is not known, but where it is only known for aggregates of the points (i.e. in this context, for regions of pixels where a census is available). We demonstrate with a very simple and interpretable model that this method is on par, and even outperforms on some metrics, the state-of-the-art, despite its simplicity.

本文提出一种基于聚合学习范式的方法，对人口普查进行高分辨率的细粒度分析，以此来评估人口数量的动态变化以及气候、自然灾害、基础设施投资、发展政策等对人口数量的影响。我们的简单可解释模型在某些指标上已超越最先进技术，证明该方法的有效性。

利用辅助数据的集成学习方法进行可解释的半监督人口预测与细分