Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment: A Case Study of Croatia

Global gridded population datasets are widely used in urban analysis, risk assessment, and sustainability monitoring, including the calculation of indicators for the Sustainable Development Goals (SDGs). Despite their broad use, their behaviour at local scales in shrinking cities remains insufficiently understood. This study evaluates three global population datasets—WorldPop, GHS-POP, and GPWv4—in seven Croatian city cores using official census data as reference. Croatia represents a relevant case due to long-term population decline combined with relatively stable built-up extents. Population estimates were compared at the city-core level for the period 2001–2021, and spatial differences between datasets were examined using pixel-level residuals, built-up intensity metrics, and land-cover stratification. The results show that WorldPop and GHS-POP achieve similar accuracy in city-total estimates, with relative errors generally ranging between about 2% and 10%, but differ systematically in their spatial allocation of population. GHS-POP concentrates population within built-up areas, while WorldPop redistributes a substantial share into non-built-up land-cover classes, exceeding GHS-POP by approximately 290,000 inhabitants outside built-up areas, whereas GHS-POP concentrates over one million additional inhabitants within built-up zones. GPWv4 often shows the smallest city-level errors but produces spatially uniform population surfaces that limit its suitability for intra-urban analysis. The findings highlight that model choice can strongly influence spatial indicators used in SDG-related and sustainability assessments, highlighting the need for context-specific evaluation of global population datasets in shrinking urban environments.