  Fine resolution estimates of demographic and socioeconomic attributes are
crucial for planning and policy development. While several efforts have been
made to produce fine-scale gridded population estimates, socioeconomic features
are typically not available at scales finer than Census units, which may hide
local heterogeneity and disparity. In this paper we present a new statistical
downscaling approach to derive fine-scale estimates of key socioeconomic
attributes. The method leverages demographic and geographical extensive
covariates available at multiple scales and additional Census covariates only
available at coarse resolution, which are included in the model hierarchically
within a "forward learning" approach. For each selected socioeconomic variable,
a Random Forest model is trained on the source Census units and then used to
generate fine-scale gridded predictions, which are then adjusted to ensure the
best possible consistency with the coarser Census data. As a case study, we
apply this method to Census data in the United States, downscaling the selected
socioeconomic variables available at the block group level, to a grid of ~300
spatial resolution. The accuracy of the method is assessed at both spatial
scales, first computing a pseudo cross-validation coefficient of determination
for the predictions at the block group level and then, for extensive variables
only, also for the (unadjusted) predicted counts summed by block group. Based
on these scores and on the inspection of the downscaled maps, we conclude that
our method is able to provide accurate, smoother, and more detailed
socioeconomic estimates than the available Census data.
