Gridded Data on Population Density in Russia: Validation and Correction of the Global GHS-POP Model

Regional Research of Russia. 2026. Vol. 16. P. 399–409.

Sheludkov A.V., Gonyukhov P.O., Ignatenko P.Y., Zykov G.S., Ryabinina A.I., Fomenkov G.K., Tsygankov E. A.

The article validates and corrects the global 100-m resolution GHS-POP population density dataset for Russia. Adapting global population density models to national contexts and improving their accuracy addresses the critical lack of high-resolution population distribution data required across geography and related disciplines. Modern population density models (e.g., LandScan, GHS-POP, WorldPop) employ dasymetric mapping principles, disaggregating statistical population data into regular grid cells using satellite-derived proxies. However, these models may yield divergent estimates due to conceptual differences in defining “population,” auxiliary datasets, and algorithms. The GHS-POP model estimates resident population density based on built-up volumes derived from Sentinel-2 imagery. We developed a two-stage data correction algorithm and applied it to three test sites with distinct urban morphologies: the cities of Krasnodar, Saratov, and Naberezhnye Chelny, followed by a validation of the resulting dataset. The correction (1) eliminated systematic discrepancies with the 2020(21) All-Russian Population Census results at municipal level and (2) resolved population misallocation errors in industrial zones using OpenStreetMap data on land use. Validation against national housing registry data demonstrated good agreement with the reference data, with total allocation accuracy of 47–56% at a 100-m resolution and 75–83% at a 1-km resolution. The scalable correction algorithm was applied to all of Russia, with corrected datasets openly available via the geoportal of HSE University Faculty of Geography and Geoinformation Technology.

Keywords: population density, dasymetric mapping, geoinformation modelling, GHS population grid

Publication based on the results of: Серия исследований по пространственному анализу и моделированию городских процессов (2024)