This file contains European countries in a shapefile format that can be used in python, R or matlab. The file has been created by Drin Marmullaku based on GADM version 4.1 (https://gadm.org/) and distributed according to their license (https://gadm.org/license.html).

Please cite as: Sevdari, Kristian; Marmullaku, Drin (2023). Shapefile of European countries. Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.23686383 This dataset is distributed under a CCBY-NC-SA 4.0 license

Using the data to create maps for publishing of academic research articles is allowed. Thus you can use the maps you made with GADM data for figures in articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are allowed (but not required) to publish these articles (and the maps they contain) under an open license such as CC-BY as is the case with PLoS journals and may be the case with other open access articles. Data for the following countries is covered by a a different license Austria: Creative Commons Attribution-ShareAlike 2.0 (source: Government of Austria)

World Countries provides a detailed basemap layer for the country boundaries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale views. The data is sourced from Garmin International, Inc. with additional content from the U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024 and is updated every 12-18 months or as significant changes occur.

This data-set contains all data resources, either directly downloadable via this platform or as links to external databases, to execute the generic modeling tool as described in D5.4

• 0 generic model
  file: d5.4-generic-model_february-2020.pdf
  Report with methodology and source code for the generic model
• 1.1 COPERNICUS
  https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=download.%20
  The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover data for 800 Functional Urban Area (FUA) for the 2012 reference year in EEA39 countries.
• 1.2a 1km grid shapefiles for NOx emissions in EU
  The shapefiles are: nox.dbf nox.prj nox.shp nox.shx
• 1.2b 1km grid shapefiles for PM10 emissions in EU
  The shapefiles are: pm10.dbf pm10.prj pm10.shp pm10.shx
• 2.1 Eurostat energy consumption data
  https://ec.europa.eu/eurostat/web/energy/data/database?p_p_id=NavTreeportletprod_WAR_NavTreeportletprod_INSTANCE_QAMy7Pe6HwI1&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1
• 2.2 JODI gas consumption data
  https://www.jodidata.org/gas/database/data-downloads.aspx
  Complete data series for all products, flows and countries, from January 2009 to one month-old can be downloaded, for free, in a beta version of .csv format.
• 2.3 Meteo data UK
  https://www.metoffice.gov.uk/services/data/datapoint/datapoint-products
• 2.4 Meteo data NL
  https://www.knmi.nl/nederland-nu/klimatologie/daggegevens
• 2.5 hourly electricity load profiles
  https://data.open-power-system-data.org/time_series/
• 3.1 fleet data
  file: 200210_summary_fleet_2016.csv
  Summary of historical fleet for EU28+NO by age and vehicle type: Diesel, CNG, Petrol, Electric, PHEV, LPG
• 3.2 modal shares
  file: 200210_summary_shares.csv
  This data set holds the modal share of different modes, for EU cities at level of vehicle kilometers. Modes considered: bus car heavy duty truck >32t heavy duty truck 16-32t heavy duty truck 3_5-7_5t heavy duty truck 7_5-16t light duty truck metro/tram moped motorcycle passenger train slow mode van
• 3.3 emission factors
  file: 200212_summary_copert_ef.xlsx
  Emission factor for passenger cars (in g/km )from COPERT V, urban conditions. Data dimensions: 1. type: diesel, petrol, CNG, LPG, PHEV (in line with fleet data) 2. pollutant: NOx or PM 3. Peak/off-peak: (P/OP) 4. year of built: YOB
• 5.1 Crude death rates
  http://apps.who.int/healthinfo/statistics/mortality/causeofdeath_query/start.php
  the total number of deaths in a country per year per 1,000 people broken down by age cohort and sex
• 5.2 UN population data
  https://population.un.org/wpp/Download/Standard/Population/
  the number of inhabitants at country level broken down by age cohort and sex the time a person has lived broken down by age cohort and sex, and country

World Continents represents the boundaries for the continents of the world. It provides a basemap layer of the continents, delivering a straightforward method of selecting a small multicountry area for display or study.This layer is best viewed out beyond a scale of 1:3,000,000. The original source was extracted from the ArcWorld Supplement database in 2001 and updated as country boundaries coincident to regional boundaries change. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World Continents.

This Digital Terrain Model (DTM) for Continental Europe was derived using Ensemble Machine Learning (EML) with publicly available Digital Surface Models. EML was trained using GEDI level 2B points (Level 2A; "elev_lowestmode") and ICESat-2 (ATL08; "h_te_mean"). About 9 million points were overlaid vs MERITDEM, AW3D30, GLO-30, EU DEM, GLAD canopy height, tree cover and surface water cover maps. An ensemble prediction model (mlr package in R) was fitted using random forest, Cubist and GLM, and used to predict the most probable terrain height (bare earth).

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. Before modeling, reference elevations were corrected to the Earth Gravitational Model 2008 (EGM2008) by using the 5-arcdegree resolution correction surface (Pavlis et al, 2012).

Details on the work to create this dataset can be found here:

• Hengl, Tomislav, Leal Parente, Leandro, Krizan, Josip, and Bonannella, Carmelo. 2020. "Continental Europe Digital Terrain Model at 30 M Resolution Based on GEDI, Icesat-2, AW3D, GLO-30, EUDEM, MERIT DEM and Background Layers." Zenodo. https://doi.org/10.5281/zenodo.4724549.
• European Digital Terrain Models (EU DTM)

NOTE:This dataset has been converted from its original units of decimeters to meters to aid comparisons with other datasets in the OpenTopography catalog.

Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018.

CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring.

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

This shapefile provides a worldwide geographic division by merging the World Continents division proposed by Esri Data and Maps (2024) to the Global Oceans and Seas version 1 division proposed by the Flanders Marine Institute (2021). Though divisions of continents and oceans/seas are available, the combination of both in a single shapefile is scarce.

The Continents and Oceans/Seas shapefile was carefully processed to remove overlaps between the inputs, and to fill gaps (i.e., areas with no information) by spatially joining these gaps to neighbour polygons. In total, the original world continents input divides land areas into 8 categories (Africa, Antarctica, Asia, Australia, Europe, North America, Oceania, and South America), while the original oceans/seas input divides the oceans/seas into 10 categories (Arctic Ocean, Baltic Sea, Indian Ocean, Mediterranean Region, North Atlantic Ocean, North Pacific Ocean, South Atlantic Ocean, South China and Easter Archipelagic Seas, South Pacific Ocean, and Southern Ocean). Therefore, the resulting world geographic division has 18 possible categories.

References

Esri Data and Maps (2024). World Continents. Available online at https://hub.arcgis.com/datasets/esri::world-continents/about. Accessed on 05 March 2024.

Flanders Marine Institute (2021). Global Oceans and Seas, version 1. Available online at https://www.marineregions.org/. https://doi.org/10.14284/542. Accessed on 04 March 2024.

Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia. Data is sourced from the World Bank and turned into a standard normalized CSV.

This dataset is part of the reference geographic data maintained by Opendatasoft. It contains data for the regions of Belgium in 2019. The data comes from the product AdminVector, from NGI-IGN. Place names are provided in the applicable language.

Levels available Regions/West Provinces/ProvinciesArrondissementsCantons/KieskantoneCommunes/Gemeente Municipal/deelgemeente Sections Enrichment Added the ISO 3166-3 code from Belgium.Simplification of geometric shapes for better performance.

CLC2018 is one of the datasets produced within the frame the Corine Land Cover programme referring to land cover / land use status of year 2018. The Corine Land Cover (CLC) is an European programme, coordinated by the European Environment Agency (EEA), providing consistent information on land cover and land cover changes across Europe. CLC products are based on the photointerpretation of satellite images by the national teams of the participating countries - the EEA member or cooperating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. The resulting European database is based on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level Corine nomenclature minimum mapping unit (MMU) for status layers is 25 hectares minimum width of linear elements is 100 metres minimum mapping unit (MMU) for Land Cover Changes (LCC) for change layers is 5 hectares CLC programme provides important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Community as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive etc. CLC programme is also a part of the Global Monitoring for Environment and Security (GMES http://gmes.info) initiative, run by the European Commission and the European Space Agency, which will provide environmental information from a combination of air- and space-based observation systems and in-situ monitoring. More about the Corine Land Cover (CLC) programme and datasets can be found at http://www.eea.eu.

The dataset on offshore wind farms in the European seas was created in 2014 by CETMAR for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources. It is updated every year and it is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points and/or (where available) polygons representing offshore wind farms in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Spain, Sweden and United Kingdom. Each point and polygon has the following attributes (where available): Name, Nº of turbines, Status (Approved, Planned, Dismantled, Construction, Production, Test site), Country, Year, Power (MW), Distance to coast (metres) and Area (square kilometres). The distance to coast (EEA coastline shapefile) has been calculated using the UTM WGS84 Zone projected coordinate system where data fall in.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

The dataset on offshore military areas in the European seas was created in 2020 by CETMAR for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources. It is updated every year and is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points and/or (where available) polygons representing offshore military areas in the following countries: Belgium, Bulgaria, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Latvia, Lithuania, Netherlands, Poland, Portugal, Spain and Sweden. Each point and/or polygon has the following attributes (where available): Country, Country_2, Country_3, Status (Active, Deactivated, Unknown, Planned), Type_1 (Firing Area, Air Force Exercise, Surface Exercise, Underwater Exercise, Mine Hunting Exercise, National Defence Area), Type_2, Type_3, Resource, Distance to coast (metres) and Area (square kilometres). The distance to coast (EEA coastline shapefile) has been calculated using the UTM WGS84 Zone projected coordinate system where data fall in.

The Zip folder contains a range of key GIS boundary files for ESRI and Map Info covering Greater London.

The folder includes:

- Output Area (OA) 2011,

- Lower Super Output Area (LSOA) 2004 and 2011,

- Middle Super Output Area (MSOA) 2004 and 2011,

- London Wards (two files: City of London merged into single area and split into seperate wards). There are separate download file for 2014 & 2018 boundaries.

- London Boroughs

- Greater London boundary

Note: The OA to MSOA boundaries have been generalised to reduce file size/loading time.

On maps created using these boundaries the copyright must be stated. This is: "Contains National Statistics data © Crown copyright and database right [2015]" and "Contains Ordnance Survey data © Crown copyright and database right [2015]"

For more information about boundary data sharing read these Terms and Conditions of Supply.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Data includes the official boundaries of the administrative areas in Hungary. Regions (2nd level) and Municipalities (3rd level). The database contains the co-ordinates of the vertexes of the Hungarian administrative boundaries on country, county and settlement level. The database corresponds the legally registered data which are stored at the land offices. The database can be generalised in different variations – satisfying every demand of the users – with accuracy in cm, 1m, 10m, 100m. (Accuracy corresponds to the map-scale). The two most frequently claimed generalised version, the MKH-100 and MKH-500 are on the LLTK’s website, too. This actual database has accuracy of 10m. This published data package free from the EuroBoundaryMap portal.

Le présent jeu de données contient les données vectorielles issues de la base de données du PCN (plan cadastral numérisé), contenant les couches suivantes: les sections cadastrales les communes administratives les cantons - les districts administratifs 'historiques' (les districts sont abolis, suivant la loi du 2 septembre 2015 portant abolition des districts) les arrondissements judiciaires les circonscriptions électorales les limites nationales du pays ainsi qu'une version généralisée de ces couches. Pour les communes, les codes LAU2 sont maintenant aussi inclus. Les communes (généralisées) sont également disponibles au format geojson