GHS-POP ARCTIC R2025A – gridded population estimates for the Arctic region (1975-2030)

GHS gridded population estimates, derived from Gridded Population of the World (GPW4.11) data, from 1975 to 2030 in 5-year intervals. Values are expressed as decimals (Float). The data are published at 100m and 1km resolution in North Pole LAEA Europe spatial reference system (EPSG:3575).

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on planet Earth. It operates in a fully open and free data and methods access policy. The knowledge generated with the GHSL is supporting the definition, the public discussion and the implementation of European policies and the monitoring of international frameworks such as the 2030 Development Agenda. The GHSL are the core data set of the Exposure Mapping Component under the Copernicus Emergency Management Service. GHSL data continue to support the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data.

This document describes the public release of the GHSL Data Package 2023 (GHS P2023). The release provides improved built-up (including surface, volume and height) and population products as well as a new settlement model and classification of administrative and territorial units according to the Degree of Urbanisation.

The conversion of gridded data between two grids and/or spatial reference systems, commonly referred to as “warping”, is a fundamental step in geospatial data processing workflows. This process involves data resampling, which inherently introduces uncertainty. When dealing with stacks of statistical, gridded datasets measuring cell-level densities, consistently enumerated across multiple points in time, it is crucial to employ a volume-preserving method. Such a method should not only preserve changes in observations along the temporal dimension but also maintain the total sums of measured data per point in time, and allowing for different resampling strategies, while minimizing local distortions in the warped data. Conventional raster-based warping tools available in Geographic Information Systems and coding-based geospatial data processing environments lack explicit control over these critical properties. To address this limitation, we propose a novel vector-based method for areal interpolation based on spatial overlay operations. This approach enables lossless resampling of gridded data, which we apply to the gridded built-up surface data from the Global Human Settlement Layer (GHSL) covering the period from 1975 to 2030. As vector-based spatial data operations are computationally expensive, our method leverages a parallel-processing framework, allowing efficient warping of global gridded data cubes. Furthermore, this approach facilitates the provision of statistical data cubes across various spatial reference systems and grid definitions at planetary scale and high spatial resolution, extendible to the use of areal or spatio-temporal interpolation methods. We implemented this method in Python and call it “VectorCubeWarp”.

In recent years, the Arctic region has garnered attention due to its geo-political and strategic importance: as a reserve for natural resources, commercial routes, and climate activism. Understanding human settlements and their evolution across scales and over time is key to informing stakeholders and policy makers on relevant dynamics. However, the cartographic projections used for global geospatial data often poorly represent the Arctic territory as they are not optimized for polar regions. Herein, we describe GHSL-Arctic, an edition of the Global Human Settlement Layer (GHSL), constituting a comprehensive, up-to-date, and accessible tool for understanding and responding to the rapid changes taking place in the Arctic environment, accounting for this shortcoming. The contribution of this article is two-fold: 1) we present and describe the spatio-temporal variables in GHSL-Arctic, measuring both population and settlements at fine grid scale over the past 50 years (1975-2030), and 2) highlight exemplary analyses, based on GHSL-Arctic, revealing recent settlement patterns and trends in the Arctic.

The Global Human Settlement Layer (GHSL) project produces global spatial data and evidence-based information describing the human presence on Earth. It operates in a fully open and free data and methods access policy. The knowledge generated by the GHSL supports the implementation and public discussion of European policies and the monitoring of international frameworks such as the United Nations Sustainable Development Goals. GHSL is the core dataset of the Exposure Mapping Component under the Copernicus Emergency Management Service (CEMS). Moreover, GHSL data support the GEO (Group on Earth Observations) Human Planet Initiative that is committed to develop a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of human presence on Earth, supporting global policy processes with agreed, actionable and goal-driven metrics. This document describes the public release of the GHSL Arctic Edition 2025 (GHS ARCTIC R2025A), a region-centric edition of the GHSL data for the Arctic, complementing the global data produced by the GHSL. As the Arctic region has recently garnered attention due to its geo-political and strategic importance, understanding human settlements and their evolution across scales and over time is key to informing stakeholders and policy makers on relevant dynamics. However, the cartographic projections used for global geospatial data often poorly represent the Arctic territory as they are not optimized for polar regions. GHSL Arctic aims to account for this shortcoming, constituting a comprehensive, up-to-date, and accessible tool for understanding and responding to the rapid changes taking place in the Arctic environment.