Advances in small area population estimation in the absence of national census data

• 成果类型: Article
• 署名作者: Tatem, Andrew J.; Boo, Gianluca; Chamberlain, Heather R.; Nnanatu, Christopher C.; Darin, Edith; Leasure, Douglas R.; Yankey, Ortis; Gadiaga, Assane; Juran, Sabrina; Rodriguez, Luis de la Rua; Espey, Jessica; Lazar, Attila N.
• 署名单位: University of Southampton; University of Oxford; United Nations Population Fund
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2413993123
• 发表日期: 2026-09-01
• 页码: e2413993123
• 关键词: population modeling; census; geostatistical models; Surveys; Remote sensing

摘要

Population data at small area scales are essential for effective decision-making, influencing public health, disaster response, and resource allocation, among others. While national censuses remain the cornerstone of population data, they are often constrained by high costs, infrequent collection cycles, and coverage gaps, which can hinder timely data availability. To address these challenges, geospatial statistical approaches using limited microcensus surveys have been demonstrated as a reliable source, but the field has advanced substantially in recent years, with significant developments in both data sources and modeling methodologies. New approaches now leverage routine health intervention campaign data, satellite-derived settlement maps, and bespoke modeling approaches to produce reliable small area population estimates where enumeration is difficult or outdated. Various countries are applying these techniques to support census operations, health program planning, and humanitarian response. This manuscript reviews recent advances in bottom-up population mapping approaches, highlighting innovations in input data, modeling methods, and validation techniques. We examine ongoing challenges, including partial observation of buildings under forest canopy, population displacement, and institutional uptake. Finally, we discuss emerging opportunities to enhance these approaches through better integration with traditional data ecosystems, capacity strengthening, and coproduction with national institutions.