LandScan HD: A High-Resolution Gridded Ambient Population Methodology for the World

Population datasets accounting for the full range of routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (HD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (roughly 90 m). Although LandScan HD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LandScan HD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LandScan HD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LandScan HD baseline dataset, we contrast the ambient population with a gridded population representing residential activities (WorldPop) by (1) exploring a practical application for flood risk assessment and (2) evaluating congruence with outcomes of collective human activities (subnational CO ₂ emissions). Finally, we discuss confronting current LandScan HD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.