High-resolution population estimation using household survey data and building footprints - Boo, G; Darin, E; Leasure, DR; Dooley, CA; Chamberlain, HR; Lázár, AN; Tschirhart, K; Sinai, C; Hoff, NA; Fuller, T; Musene, K; Batumbo, A; et al. (Springer Nature, 2022)
The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.