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Census data provide detailed information about population characteristics ata coarse resolution. Nevertheless, fine-grained, high-resolution mappings ofpopulation counts are increasingly needed to characterize population dynamicsand to assess the consequences of climate shocks, natural disasters,investments in infrastructure, development policies, etc. Dissagregating thesecensus is a complex machine learning, and multiple solutions have been proposedin past research. We propose in this paper to view the problem in the contextof the aggregate learning paradigm, where the output value for all trainingpoints 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). Wedemonstrate with a very simple and interpretable model that this method is onpar, and even outperforms on some metrics, the state-of-the-art, despite itssimplicity.

An aggregate learning approach for interpretable semi-supervised population prediction and disaggregation using ancillary data