High-resolution population estimation using household survey data and building footprints.
Nat Commun
; 13(1): 1330, 2022 03 14.
Article
em En
| MEDLINE
| ID: mdl-35288578
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 However, this data may become outdated during the intercensal period, which can stretch up to several decades. 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of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Censos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Nat Commun
Ano de publicação:
2022
Tipo de documento:
Article