Russia is the largest country in Europe, and also the largest in the world, its total size amounting to 17 million square kilometers (km2). It should be noted, however, that over three quarters of Russia is located in Asia, and the Ural mountains are often viewed as the meeting point of the two continents in Russia; nonetheless, European Russia is still significantly larger than any other European country. Ukraine, the second largest country on the continent, is only 603,000 km2, making it about 28 times smaller than its eastern neighbor, or seven times smaller than the European part of Russia. France is the third largest country in Europe, but the largest in the European Union. The Vatican City, often referred to as the Holy Sea, is both the smallest country in Europe and in the world, at just one km2. Population Russia is also the most populous country in Europe. It has around 144 million inhabitants across the country; in this case, around three quarters of the population live in the European part, which still gives it the largest population in Europe. Despite having the largest population, Russia is a very sparsely populated country due to its size and the harsh winters. Germany is the second most populous country in Europe, with 83 million inhabitants, while the Vatican has the smallest population. Worldwide, India and China are the most populous countries, with approximately 1.4 billion inhabitants each. Cities Moscow in Russia is ranked as the most populous city in Europe with around 13 million inhabitants, although figures vary, due to differences in the methodologies used by countries and sources. Some statistics include Istanbul in Turkey* as the largest city in Europe with its 15 million inhabitants, bit it has been excluded here as most of the country and parts of the city is located in Asia. Worldwide, Tokyo is the most populous city, with Jakarta the second largest and Delhi the third.

The average for 2021 based on 27 countries was 187 people per square km. The highest value was in Malta: 1620 people per square km and the lowest value was in Finland: 18 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

The average for 2021 based on 47 countries was 221323.2 sq. km. The highest value was in Russia: 8153116 sq. km and the lowest value was in Gibraltar: 0 sq. km. The indicator is available from 1990 to 2022. Below is a chart for all countries where data are available.

Total usually resident population by place of usual residence, sex, current activity status (number of employed persons), country/place of birth and place of usual residence one year prior to the census of the 2021 population and housing census results geocoded to a 1 km2 European grid. Data aggregation of 13 indicators for 6 topics (total population, sex (male, female), age (under 15 years, 15 – 64 years, 65 years and over), current activity status (employed), country of birth (reporting country, other Memberstate, elsewhere), place of usual residence one year prior to the census (usual residence unchanged, move within reporting country, move from the outside of the reporting country)) was done. The data tables are made as SDMX compliant (based on SDMX GRID DSDs). ‘Geographic Information System’ (GIS) software performed the following steps: 1. Given (national) coordinates were transformed into the required EU grid reference frame. 2. The corresponding 1 km² grid cell in which the transformed coordinates lie was identified. 3. The respective grid cell code was allocated to the microdata record in question. Data aggregation was made on estimated individual data based on registers. The difference between the Latvian 1km2 network and the European 1km2 network is that they are located at a different angle to each other.

This thematic map of coroplets represents the demographic indicator Population. Foreigners per km2, for the territorial delimitation of islands of the Canary Islands, from the Municipal Register of Inhabitants (PMH) to this date.

The EuMedClim dataset provides times series (1901-2014) of climate grids at 30 arc second resolution (~ 1 km2) for Europe and the Mediterranean Basin. An ‘anomaly-approach’ was used to combine the spatial information of Worldclim data (Hijmans et al. 2005) and the temporal information of the CRU TS 3.23 dataset (Harris et al. 2014). The EuMedClim dataset comprises 21 parameters: seasonal and annual mean temperature, mean diurnal temperature range, maximum temperature of the warmest month, minimum temperature of the coldest month, seasonal and annual precipitation, precipitation of the wettest and driest months, and yearly mean, minimum and maximum values of monthly potential evapotranspiration (PET) and precipitation minus PET (drought index). The entire data set is available at http://gentree.data.inra.fr/climate together with R scripts for downloading time series at sampling points and producing climate maps.

The dataset contains the extent of the land claim (permanent or temporary establishments of the sea) and the type of construction reported by the Contracting Parties for the period 2016-2021.

The data was made available by HELCOM Contracting Parties in response to a data call. The data was reported by Denmark, Estonia, Finland, Poland, Russia, and Sweden. The activity was declared as not relevant in Germany and Latvia.

Attribute specification and units (for points and polygons): Name: Name of the site Descrip: Brief description of the type of land claim Year: Year of construction, especially if built between 2016-2021 Area: Area in km2 Notes: Notes regarding the data

Attribute specification and units (for lines): Country: Contracting Party that reported the information Type: Brief description of the type of land claim Estimated: Estimated year Area: Area in km2 Name: Name of the site descripto: Description of the site in national language Notes: Notes regarding the data

Critical infrastructures (CIs) are assets, systems, or parts thereof that are essential for the maintenance of socioeconomic functions, health, safety and well-being of people. The exposure of CIs to natural and man-made hazards poses a risk to the economy and society. The spatial distribution of CIs and their economic value are a prerequisite for quantifying risk and planning suitable protection and adaptation measures. However, the incompleteness and inconsistency of existing information on CIs hamper their integration into large-scale risk frameworks.The ‘HARmonized grids of Critical Infrastructures in Europe’ (HARCI-EU) dataset represents major CIs in the transport, energy, industry and social sectors expressed in sector-specific, economically-relevant units. The HARCI-EU dataset contains 22 grids in GeoTIFF format with a resolution of 1 km2. The grids use the ETRS89 coordinate system and the Lambert Azimuthal Equal Area map projection. Cell values range between zero (i.e. absence of infrastructure) and a CI-specific maximum value. Raster cells outside the area of interest have null values. The files are best visualized and manipulated using appropriate Geographical Information System software.The HARCI-EU grids were produced by integrating geospatial and statistical data from multiple sources. Correlation analysis performed against independent metrics corroborates the approach showing average Pearson coefficients ranging between 0.61 and 0.95 across the sectors. HARCI-EU provides a consistent mapping of CIs in key sectors that can serve as exposure information for large-scale risk assessments in Europe.

G2 model is a new generic model for erosion, resulted from the cooperation of European Commission/Joint Research Centre and the Lab of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki in the framework of geoland2 project. G2 estimates soil loss (in t/ha) from sheet and interril erosion caused by splash and runoff, on a month-step basis and on a landscape scale. Soil erosion layers (Total & Monthly) plus Rainfall erosivity (Total & Monthly), vegetation retention (Total & Monthly), soil erodibility, topographic influence and slope intercept are available for the three (5) application areas: Cyprus: 9251 Km2 - pixel size: 100m including sediment module Crete island: 8,336 Km2 (Greece) - Pixel size: 300m, temporal coverage: 2011-2012. Ishmi-Erzeni watershed : 2,200 Km2 (Albania) - Pixel size: 300m, temporal coverage: 2011-2012. Korce Region: 1690 Km2 (Albania) - Pixel size: 30m Strymonas/Struma Catchment : 14,500 Km2 (Greece / Bulgaria) - Pixel size: 300m, temporal coverage: 2003-2006.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

• Population density 2023 (inhabitants per km2), Lorraine: 2021

• Territorial entities: arrondissements (Lorraine, Wallonia), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz)

• Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2024

• Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2024

Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=en&rotation=0&layers=2418&opacities=1&bgLayer=basemap_2015_global

Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/3ed89eb1-9a37-4b86-b793-126411751345

This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s):
-Pop_density_2023

The EMODnet (European Marine Observation and Data network) Geology project collects and harmonizes marine geological data from the European sea areas to support decision making and sustainable marine spatial planning. The project partnership consists mainly of the marine departments of the geological surveys of Europe (through the Association of European Geological Surveys-EuroGeoSurveys), which have assembled marine geological information at various scales from all European sea areas (e.g., the White Sea, Baltic Sea, Barents Sea, the Iberian Coast, and the Mediterranean Sea within EU waters), the Caspian Sea and the Caribbean Sea. EMODnet seabed substrate data products include datasets at 1:1 000 000, 1:250 000, 1:100 000 and high-resolution scales (1:1 500, 1:5 000, 1:10 000, 1:15 000, 1:20 000, 1:25 000, 1:30 000, 1:45 000, 1: 50 000, 1:60 000, 1:70 000). Traditionally, European countries have conducted their marine geological surveys according to their own national standards and classified substrates on the grounds of their national classification schemes. These national classifications are harmonised into a shared EMODnet schema using Folk's sediment triangle with a hierarchy of 16, 7 and 5 substrate classes. The data describes the seabed substrate from the uppermost 30 cm of the sediment column. The datasets have been compiled at different scales from background information that depends on the original producer. In cases, the data has been generalized into a target scales. Coarser scale data (1M, 250k and 100k) describes the seabed substrate at a general level only and is not suitable for a detailed analysis. High-resolution material includes multiple scales from 1:1 500 to 1:70 000 and the user should assess its suitability for the intended purpose. The smallest cartographic unit within the data is about 4 km2 at 1:1M, 0,3km2 at 1:250k and 0,05km2 at 1:100k scale. Further information about the EMODnet Geology project is available on the centralized EMODnet portal https://emodnet.ec.europa.eu/en/geology

E-HYPE is a model application set up to calculate hydrological variables (e.g.runoff, discharge, snow depth, groundwater level) and nutrient variables (e.g. concentrations and loads) for over 35 000 subbasins (median resolution=215 km2) across all of Europe. More information on this dataset can be found in the Freshwater Metadatabase - BFE_95 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_95).

We describe and share reconstructed data of the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire that covered more than 80 000 km2 that are currently divided among Czechia, Poland and Ukraine. The network dataset includes the times of railway appearance and of the most dynamic development until 1914, which marked the outbreak of the First World War. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed down between 1914 and 2019 and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imagery and historical maps. Altogether, the network dataset covers nearly 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. the year of construction, length, starting point, finish point, and type (normal, narrow-gauge, etc.), and are available for download in the shp format.

This dataset is associated to the peer-reviewed publication: Lugato E, Paniagua L, Jones A, de Vries W, Leip A (2017) "Complementing the topsoil information of the Land Use/Land Cover Area Frame Survey (LUCAS) with modelled N2O emissions." PLoS ONE 12(4): e0176111. https://doi.org/10.1371/journal.pone.0176111 It contains: 1) Average nitrous oxides emissions in soil LUCAS pointsThis dataset contains the average (2010-2014 time period) emissions of N2O-N (kg ha-1 yr-1) simulated in soil LUCAS points.The format is in dbf; the dataset can be joined with the LUCAS soil properties shapefile (available in ESDAC) by the 'sample_ID' field.The average N2O-N emissions and standard deviation were used to build Figs 3 and 4 of the cited paper. 2) Nitrous oxides emissions in agricultural soils of the EU This dataset contains the N2O-N emissions (kg N ha-1 yr-1) at 1 km2 resolution in the EU, obtained by the meta-model MT1 (Fig 6) and MT2.Format: geoTiffResolution: 1 km2Projection: ETRS89 Lambert Azimuthal Equal Area Details on the underlying methodology can be found in cited paper.

The map shows the number of children per km2 that attend older primary schools and may therefore be exposed to more lead. Before 1945, lead drinking water pipes were mainly installed in buildings. This has been discontinued since about 1960. The drinking water companies replaced the majority of the outdoor water pipes in the period 1995-2002. Homeowners are responsible for indoor plumbing. In older buildings there is therefore a chance that lead pipes are still present.

This datasets contains the code used to extract and process the OSM data, maps for both wind turbine and solar PV locations and the probability maps.The Overpass Turbo API wrapper is accessed through a Python script, which applies key/value pairs to extract wind turbines and solar PV. The resulting GeoJSON file contains a series of nodes and ways, delimiting the parameters of the items mapped and classified under the pairs of tags and keys. The solar PV and wind turbine map display the most recent and coherent spatial dataset of onshore wind turbines (118,238) and industrial solar photovoltaics (670 km2) in the European Union, excluding urban and water areas. The results present an 8.5-fold and 2.5-fold increase in onshore wind turbines and solar photovoltaics, respectively, compared to similar results from 2020. Two models were developed to understand the significance, associated relationship, and strength of a wide range of variables as location determinants. A 50-fold cross-validation concluded the robustness of both models, allowing for the development of probability maps that illustrate the future expansion potential in the European Union which are provided as tiff files in this dataset.

The map shows the number of children per km2 that stay in older day care centers and are therefore possibly exposed to more lead. Before 1945, lead drinking water pipes were mainly installed in buildings. This has been discontinued since about 1960. The drinking water companies replaced the majority of the outdoor water pipes in the period 1995-2002. Homeowners are responsible for indoor plumbing. In older buildings there is therefore a chance that lead pipes are still present.

The GEMAS dataset is based on low density geochemical sampling of agriculture (Ap) and grassland (Gr) soils across 34 European countries. Sample density covering an area of 5.6 million km2 of 1 site each, arable land (0-20 cm) and land under permanent grass cover (0-10 cm), per 2500 km2. The Geochemical Mapping of Agricultural and Grazing Land Soil comprises more than 70 chemical elements and parameters determined on more than 4000 soil samples. The geochemistry of European agriculture and grazing soils are depicted graphically on maps of the GEMAS geochemical atlas.

In 2016 the Geological Survey of Ireland as a European partner contributes to GEMAS and EGDI (European Geological Data Infrastructure) with provision of a GIS spatial data classification and publication of WMS geochemical web mapping services to support European data interoperability of EGDI web portal.

The GIS GEMAS sample classification were constructed in ArcGIS 10.1 and the original GEMAS dataset is available as ESRI shapefile format.