High-resolution population estimation using household survey data and building footprints
Abstract
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 However, this data may become outdated during the intercensal period, which can stretch up to several decades. We developed a Bayesian hierarchical model leveraging recent household surveys with probabilistic sampling designs and building footprints to produce up-to-date population estimates. We estimated population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibited a very good fit, with an R^2 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. The results confirm the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.
Cite
@article{arxiv.2106.07461,
title = {High-resolution population estimation using household survey data and building footprints},
author = {Gianluca Boo and Edith Darin and Douglas R Leasure and Claire A Dooley and Heather R Chamberlain and Attila N Lázár and Kevin Tschirhart and Cyrus Sinai and Nicole A Hoff and Trevon Fuller and Kamy Musene and Arly Batumbo and Anne W Rimoin and Andrew J Tatem},
journal= {arXiv preprint arXiv:2106.07461},
year = {2022}
}
Comments
27 pages, 6 figures