Research

National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

Authors Douglas R. Leasure, Warren C. Jochem, Eric M. Weber, Vincent Seaman, Andrew J. Tatem
Source PNAS
Published 2020
Full publication

More publications

GRID3 Impact Report 2023-2024

Explore how GRID3’s core spatial data – including population estimates, settlements, health facilities, and administrative boundaries – are making a difference in Nigeria and the Democratic Republic of Congo. This report details our achievements between 2023 and 2024, highlighting our […]

Piloting the GMT in Nigeria: Insights from Kano and Kaduna

The Geospatial Microplanning Toolkit (GMT) is a mobile application that allows health staff to record, edit, and visualize key spatial data and features via an interactive user interface. The GMT was developed to address three issues affecting health planning and […]

Microplanning: A promising approach to identify and reach zero-dose children in Democratic Republic of Congo (DRC)

This case study examines microplanning approaches as a key component in the Mashako Plan 2.0 to revitalize routine immunization strategies in the Democratic Republic of Congo (DRC).