  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.
