This product contains the Urban Centres polygon and point layer and the summary statistics of GHS-WUP-BUILT-S multi-temporal (1975-2100) and GHS-WUP-POP multi-temporal (1975-2100) at Urban Centre level. The dataset reports information of individual urban centres identified between 1975 and 2100 and linked spatially over time.

This dataset contains statistics on urban centres based on data from the Global Human Settlement Layer (GHSL) produced at the Joint Research Centre of the European Commission, unit E.1 (Disaster Risk Management). This release is based on the GHSL Data Package 2023, the Degree of Urbanisation to delineate spatial entities, and geospatial data integration from a variety of open source datasets to characterise them. The result is the most complete information system on cities to date with data for 11,422 quality-controlled urban centres across 15 thematic domains, 471 indicators, and 2600 attributes. The UCDB has two data streams, one based on a fixed delineation of urban centres in 2025, and a second version based on multi-temporal delineation of urban centres, traceable over time.The UCDB integrates data from Copernicus Services (including Emergency, Land Monitoring, Marine and Climate), peer-reviewed datasets (i.e. from the scientific literature), and institutional information systems (i.e. from the United Nations).

This product contains the spatial raster dataset grids representing the distribution of the total built-up (BU) surfaces estimates between 1975 and 2100 in 5 years intervals. The data between 1975 and 2020 is obtained from the GHS-BUILT-S R2023 product and the 2025-2100 total built-up surface projections are computed considering a stable non-residential built-up surface and modelling the growth of the residential component of the total built-up surface.

This product contains the spatial raster dataset grids from 1975 to 2100 at 5 years interval representing the distribution of human population, expressed as the number of people per cell. Residential population estimates at 5 years interval between 1975 and 2020 are derived from the raw global census data harmonized by CIESIN for the Gridded Population of the World (GPWv4.11) combined with population trends obtained from the Urban Agglomeration time series of the UN World Urbanisation Prospects 2018 (UN WUP 2018 – F22). The 2025-2100 population projections are computed following the 2000-2020 local growth rate in subnational Functional Areas converging to the national growth rate (UN World Population Prospects, 2024) in 100 years and downscaled using CRISP methodology.

This product contains the summary statistics of the area, population and built-up surface by Degree of Urbanisation classes (both L1 and L2) for each country in the World (United Nation “Standard country or area codes for statistical use” (ST/ESA/STAT/SER.M/49/Rev.3), produced by the JRC in the epochs 1950-2100 (5-yrs interval).

This product contains the spatial raster dataset grids from 1975 to 2100 at 5 years interval representing the settlement classification per grid cell and, for the year 2025, the vector layers of the delineated boundaries of settlement entities (i.e. urban centres, UC; dense urban clusters, DUC; semi-dense urban clusters, SDUC; and rural clusters, RC) with main attributes, in vector files (country, area, population, built-up surface and population weighted centroid) and the list of country capitals with reference to the entity IDs.

Historical road networks in Europe: Shapefiles including the correct geometry and speed of the European road networks from 1960. Methodological description and application in:- Condeço-Melhorado, A. M., Christidis, P., Dijkstra, L. (2015). Travel speed changes along the European core road network for the period 1960–2030: An application of octilinear cartograms. Journal of Maps, (November), 1–4. doi:10.1080/17445647.2015.1088482Three classes of roads (1,2,3), class 1 being highways. Six time periods: 2012, 2000, 1990, 1980, 1970 and 1955 Field NCLASS_xxx is the class of the road in each year (last xxx digits) Travel time is the time (in minutes) to travel the distance (shape_length, in meters) at the speed of the class the road has in 2012.

A dataset of different accessibility indicators for all urban areas with more than 250 thousand people in the EU27, the UK, Switzerland and Norway. Each city is analysed by means of a population grid of 500 m by 500 m and represented by a wider area covering both the densely populated urban centre and the commuting zone. To capture congestion, we measure accessibility for each grid cell at different times of the day that correspond to different traffic conditions using the detailed network and congestion information provided by TomTom.

This dataset describes counterfactual public transport networks that were simulated for 36 world cities, and the aggregate data discussed in the paper in which these data are published.UNIT OF MEASURE: Meters of network length.RESOLUTION: 1:1000000.COMPLETENESS: 100%.POLICY CONTEXT: Regional and urban policies.METHODOLOGY: Network expansion modelling.DATA SOURCES: FUA boundaries and population sizes according to 1km GHSL population grids (release 2019).LEVEL OF AGGREGATION: cities defined on population density clusters.UNCERTAINTY AND LIMITATIONS: Data based on simulation exercise with the explicit aim of creating counterfactual networks.

This dataset describes the FRAGs (Functional Rural Area at the Grid level) and FRAUs (Functional Rural Area at the local administrative Unit level) as described in the JRC working paper Dijkstra, Jacobs-Crisioni (2023), Developing a definition of Functional Rural Areas in the EU. It also contains an overview of the matching between FRAGs and the LAU-2 units from which the FRAUs are composed.RESOLUTION: 1:1000000.COMPLETENESS: 100%.POLICY CONTEXT: Regional and urban policies.METHODOLOGY: Functional rural areas cover all the territory outside functional urban areas. They are constructed in three steps. First, we define rural centres: they are the largest town or village within a 10-minute drive. Second, we create catchment areas by assigning every grid cell to the nearby rural centre that has the greatest gravitational pull. Third, we combine small and nearby catchment areas. We combine catchment area until each has at least 25 000 inhabitants or is more than an hour’s drive away from the surrounding catchment areas. We also combine catchment areas that have centres that are less than a 30-minute drive apart, even if they have a population of at least 25 000 inhabitants. Next, we show that functional rural areas are more harmonised in terms of population and area size than LAUs and NUTS-3 regions. The analysis of population change and of the distance to the nearest school shows that the results by functional area are less volatile than the results per LAU and show more detail than the results per NUTS-3 regions. Functional rural areas can inform policies that promote access to services and that respond to demographic change. They can also be used to inform transport infrastructure investments and public transport provision.DATA SOURCES: Settlement definitions according to degrees of urbanisation, Geostat 2011. Population based on JRC-Geostat 2018. FUAs from provisional 2021 FUA boundaries. Network connectivity and travel times from Tom Tom freeflow impedances.LEVEL OF AGGREGATION: Functional Rural AreasUNCERTAINTY AND LIMITATIONS: Data represent likely functionally autonomous areas, with a loose definition of functional autonomy. Not validated empirically.