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Fine resolution estimates of demographic and socioeconomic attributes arecrucial for planning and policy development. While several efforts have beenmade to produce fine-scale gridded population estimates, socioeconomic featuresare typically not available at scales finer than Census units, which may hidelocal heterogeneity and disparity. In this paper we present a new statisticaldownscaling approach to derive fine-scale estimates of key socioeconomicattributes. The method leverages demographic and geographical extensivecovariates available at multiple scales and additional Census covariates onlyavailable at coarse resolution, which are included in the model hierarchicallywithin a "forward learning" approach. For each selected socioeconomic variable,a Random Forest model is trained on the source Census units and then used togenerate fine-scale gridded predictions, which are then adjusted to ensure thebest possible consistency with the coarser Census data. As a case study, weapply this method to Census data in the United States, downscaling the selectedsocioeconomic variables available at the block group level, to a grid of ~300spatial resolution. The accuracy of the method is assessed at both spatialscales, first computing a pseudo cross-validation coefficient of determinationfor the predictions at the block group level and then, for extensive variablesonly, also for the (unadjusted) predicted counts summed by block group. Basedon these scores and on the inspection of the downscaled maps, we conclude thatour method is able to provide accurate, smoother, and more detailedsocioeconomic estimates than the available Census data.

Magnify Your Population: Statistical Downscaling to Augment the Spatial Resolution of Socioeconomic Census Data