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The national census is an essential data source to support decision-making inmany areas of public interest. However, this data may become outdated duringthe intercensal period, which can stretch up to several decades. We developed aBayesian hierarchical model leveraging recent household surveys withprobabilistic sampling designs and building footprints to produce up-to-datepopulation estimates. We estimated population totals and age and sex breakdownswith associated uncertainty measures within grid cells of approximately 100m infive provinces of the Democratic Republic of the Congo, a country where thelast census was completed in 1984. The model exhibited a very good fit, with anR^2 value of 0.79 for out-of-sample predictions of population totals at themicrocensus-cluster level and 1.00 for age and sex proportions at the provincelevel. The results confirm the benefits of combining household surveys andbuilding footprints for high-resolution population estimation in countries withoutdated censuses.

High-resolution population estimation using household survey data and building footprints