24 datasets found
  1. g

    MAP - Population density in the European area

    • gimi9.com
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    MAP - Population density in the European area [Dataset]. https://gimi9.com/dataset/eu_66bbe40cb23fb3b9cbf29a6e
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    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This map shows the population density in North-Eastern Europe in 2011. This map is extracted from the cartographic atlas made on the occasion of the merger of the Alsace, Champagne-Ardenne and Lorraine Regions in January 2016. It is available on the website of the Grand Est Region. This map was designed for A3 format, landscape.

  2. E

    A high resolution economic density zone map of Europe

    • dtechtive.com
    • find.data.gov.scot
    jpg, pdf, txt, zip
    Updated Aug 17, 2018
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    University of Edinburgh (2018). A high resolution economic density zone map of Europe [Dataset]. http://doi.org/10.7488/ds/2419
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    zip(9.27 MB), jpg(0.0838 MB), pdf(0.1632 MB), txt(0.0166 MB)Available download formats
    Dataset updated
    Aug 17, 2018
    Dataset provided by
    University of Edinburgh
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Europe
    Description

    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.

  3. j

    Data from: Data and code for "Sustainable Human Population Density in...

    • portalcienciaytecnologia.jcyl.es
    • investigacion.cenieh.es
    Updated 2022
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    Rodríguez, Jesús; Sommer, Christian; Willmes, Christian; Mateos, Ana; Rodríguez, Jesús; Sommer, Christian; Willmes, Christian; Mateos, Ana (2022). Data and code for "Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago" [Dataset]. https://portalcienciaytecnologia.jcyl.es/documentos/67321e95aea56d4af048594b
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    Dataset updated
    2022
    Authors
    Rodríguez, Jesús; Sommer, Christian; Willmes, Christian; Mateos, Ana; Rodríguez, Jesús; Sommer, Christian; Willmes, Christian; Mateos, Ana
    Area covered
    Western Europe
    Description

    This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

  4. Highest population density by country 2024

    • statista.com
    Updated May 27, 2025
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    Statista (2025). Highest population density by country 2024 [Dataset]. https://www.statista.com/statistics/264683/top-fifty-countries-with-the-highest-population-density/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    World
    Description

    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.

  5. e

    Population density 2019 (Environmental Atlas)

    • data.europa.eu
    wfs
    + more versions
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    Population density 2019 (Environmental Atlas) [Dataset]. https://data.europa.eu/data/datasets/5b824dfe-a693-3efb-8bb0-0ced08f7af9a?locale=en
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    wfsAvailable download formats
    Description

    Population density [residents/ha] 2019 at the level of the block and block areas of map 1: 5,000 (ISU5, spatial reference environmental atlas 2015).

  6. Population age distribution in Europe

    • geocat.ch
    Updated Feb 21, 2022
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    Atlas of Switzerland (2022). Population age distribution in Europe [Dataset]. https://www.geocat.ch/geonetwork/srv/api/records/2541c1f5-9b3f-49f0-8dea-11414c95f662?language=all
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    www:link-1.0-http--linkAvailable download formats
    Dataset updated
    Feb 21, 2022
    Dataset provided by
    World Bankhttp://worldbank.org/
    Atlas of Switzerland
    Authors
    Atlas of Switzerland
    License

    http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitationshttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations

    Time period covered
    Jan 1, 1960 - Dec 31, 2020
    Area covered
    Description

    Population age distribution in Europe. Map type: Charts. Spatial extent: Europe. Times: 1960, 1970, 1980, 1990, 2000, 2010, 2015, 2020. Distinction: 10-year class, 5-year class

  7. Global population density by region 2025

    • statista.com
    • ai-chatbox.pro
    Updated May 27, 2025
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    Statista (2025). Global population density by region 2025 [Dataset]. https://www.statista.com/statistics/912416/global-population-density-by-region/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Worldwide
    Description

    As of 2025, Asia was the most densely populated region of the world, with nearly 156 inhabitants per square kilometer, whereas Oceania's population density was just over five inhabitants per square kilometer.

  8. GHS population grid, derived from EUROSTAT census data (2011) and ESM R2016

    • data.europa.eu
    tiff
    Updated Apr 1, 2016
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    Joint Research Centre (2016). GHS population grid, derived from EUROSTAT census data (2011) and ESM R2016 [Dataset]. https://data.europa.eu/data/datasets/jrc-ghsl-ghs_pop_eurostat_europe_r2016a?locale=de
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    tiffAvailable download formats
    Dataset updated
    Apr 1, 2016
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Description

    The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

    The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

    This spatial raster dataset depicts the distribution and density of residential population, expressed as the number of people per cell. Resident population from censuses for year 2011 provided by Eurostat were disaggregated from source zones to grid cells, informed by land use and land cover from Corine Land Cover Refined 2006 and by the distribution and density of built-up as mapped in the European Settlement Map 2016 layer.

  9. Global population 1800-2100, by continent

    • statista.com
    • ai-chatbox.pro
    Updated Jul 4, 2024
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    Statista (2024). Global population 1800-2100, by continent [Dataset]. https://www.statista.com/statistics/997040/world-population-by-continent-1950-2020/
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    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

  10. Z

    Data from: Pan-European exposure maps and uncertainty estimates from HANZE...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 15, 2023
    + more versions
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    Paprotny, Dominik (2023). Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6783201
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    Dataset updated
    Jun 15, 2023
    Dataset authored and provided by
    Paprotny, Dominik
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Europe
    Description

    This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files:

    • CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP)

    • Pop: population

    • GDP: gross domestic product (2020 euros)

    • FA: fixed asset value (2020 euros)

    • imp: imperviousness density (%)

    Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods.

    Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables.

    For detailed description of the files, see the documentation provided with the code.

    This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

  11. Coastal dataset including exposure and vulnerability layers, Deliverable 3.1...

    • zenodo.org
    Updated Nov 25, 2023
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    E. Ieronymidi; D. Grigoriadis; E. Ieronymidi; D. Grigoriadis (2023). Coastal dataset including exposure and vulnerability layers, Deliverable 3.1 - ECFAS Project (GA 101004211), www.ecfas.eu [Dataset]. http://doi.org/10.5281/zenodo.7319270
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    Dataset updated
    Nov 25, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    E. Ieronymidi; D. Grigoriadis; E. Ieronymidi; D. Grigoriadis
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

    The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

    Description of the containing files inside the Dataset.

    The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

    Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

    * Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

    * Malta was added to the dataset

    Copernicus Land Monitoring Service:

    Coastal LU/LC

    Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

    EU-Hydro - Coastline

    Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

    Natura 2000

    Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

    European Settlement Map

    Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

    Imperviousness Density

    Resolution 10m; The percentage of sealed area

    Impervious Built-up

    Resolution 10m; The part of the sealed surfaces where buildings can be found

    Grassland 2018

    Resolution 10m; A binary grassland/non-grassland product

    Tree Cover Density 2018

    Resolution 10m; Level of tree cover density in a range from 0-100%

    Joint Research Center:

    Global Human Settlement Population Grid
    GHS-POP)

    Resolution 250m; Residential population estimates for target year 2015

    GHS settlement model layer
    (GHS-SMOD)

    Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

    GHS-BUILT

    Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

    ENACT 2011 Population Grid

    (ENACT-POP R2020A)

    Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

    JRC Open Power Plants Database (JRC-PPDB-OPEN)

    Europe's open power plant database

    GHS functional urban areas
    (GHS-FUA R2019A)

    Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

    GHS Urban Centre Database
    (GHS-UCDB R2019A)

    Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

    Additional Data:

    Open Street Map (OSM)

    BF, Transportation Network, Utilities Network, Places of Interest

    CEMS

    Data from Rapid Mapping activations in Europe

    GeoNames

    Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

    Global Administrative Areas

    Administrative areas of all countries, at all levels of sub-division

    NUTS3 Population Age/Sex Group

    Eurostat population by age and sex statistics interescted with the NUTS3 Units

    FLOPROS

    A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

    Disclaimer:

    ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

    This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

  12. o

    Data from: Data for 'Devising a method to remotely model and map the...

    • ora.ox.ac.uk
    Updated Jan 1, 2020
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    Willis, K; Benz, D; Nogue Bosch, S (2020). Data for 'Devising a method to remotely model and map the distribution of natural landscapes in Europe with the greatest recreational amenity value' [Dataset]. https://ora.ox.ac.uk/objects/uuid:2b4410a5-86e6-4b34-8243-643c9ca533f0
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    (651522598), (90320886), (211527773), (13275), (26334), (1466569036)Available download formats
    Dataset updated
    Jan 1, 2020
    Dataset provided by
    University of Oxford
    Authors
    Willis, K; Benz, D; Nogue Bosch, S
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2000 - 2018
    Area covered
    Europe
    Description

    With a growing emphasis on the societal benefits gained through recreation outdoors, a method is needed to identify which spaces are most valuable for providing those benefits. Social media platforms offer a wealth of useful information on where people prefer to enjoy the outdoors. We combined geotagged images from Flickr with several environmental metrics in a Maxent model to calculate the probability of a photograph being taken (the potential supply of recreational amenity). We then built a set of population density kernels to express the potential demand of recreational amenity. Linear regression was used to compare supply and demand layers to visitation records from 540 recreation sites across Europe. The result was a map estimating the number of visitors/km2/year. Our analysis showed that natural areas near population centres deliver more recreational benefit than attractive sites in remote locations. The former should therefore be prioritised by planners and policymakers seeking to protect or improve recreational amenity.

  13. Basic European Assets Map, Finland (2014-04-22)

    • data.europa.eu
    Updated Mar 6, 2018
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    Joint Research Centre (2018). Basic European Assets Map, Finland (2014-04-22) [Dataset]. https://data.europa.eu/euodp/ga/data/dataset/b7914f88-caea-4240-8c6d-afe994ed3960
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    esri file geodatabaseAvailable download formats
    Dataset updated
    Mar 6, 2018
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Area covered
    Finland
    Description


    Activation date: 2014-04-22
    Event type: Other

    Activation reason:
    Service Request: The nation-wide asset mapping for Finland provides a detailed regional geospatial dataset for the quick and uncomplicated calculation of potential damages either in the preparedness phase or during the immediate response phase of crises caused by natural hazard events. The concept follows the Basic European Asset Map (BEAM) data model developed under the Copernicus precursor project SAFER (Services and Applications for Emergency Response) and extended in the FP7 project IncREO (Increasing Resilience through Earth Observation).BEAM Finland is a comprehensive dataset comprising of a set of spatialized economic indicator values and a population density value. All economic attributes are expressed in EURO/m². By using GIS methods for intersecting BEAM data with hazard intensity information and appropriate vulnerability functions quick regional estimates can be made for exposure of assets and population, damage assessments and cost/benefit analysis.The wall-to-wall map and vector dataset depicts assets for various economic categories as well as for population density. The data are derived by combining socioeconomic data and land use/cover data. Fourteen distinct contributing attributes for the asset mapping are provided (e.g. buildings, households, industry, agriculture, etc.). Assets information is made available not only as a cumulative layer of different types of assets (e.g. private households, industry, commerce, vehicles, agriculture, etc.), but as accessible single contributing layers as well, each of them expressing its value.

  14. Distribution of the global population by continent 2024

    • statista.com
    • ai-chatbox.pro
    Updated Mar 27, 2025
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    Statista (2025). Distribution of the global population by continent 2024 [Dataset]. https://www.statista.com/statistics/237584/distribution-of-the-world-population-by-continent/
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    Dataset updated
    Mar 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

  15. f

    Additional file 5: of Development of F1 hybrid population and the...

    • springernature.figshare.com
    xlsx
    Updated Jun 2, 2023
    + more versions
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    Anatoly Zhigunov; Pavel Ulianich; Marina Lebedeva; Peter Chang; Sergey Nuzhdin; Elena Potokina (2023). Additional file 5: of Development of F1 hybrid population and the high-density linkage map for European aspen (Populus tremula L.) using RADseq technology [Dataset]. http://doi.org/10.6084/m9.figshare.c.3929437_D5.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    figshare
    Authors
    Anatoly Zhigunov; Pavel Ulianich; Marina Lebedeva; Peter Chang; Sergey Nuzhdin; Elena Potokina
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Europe
    Description

    Detailed information on genetic distances and linkage phase between adjacent SNP markers in the maternal linkage map constructed for P. tremula intra-specific cross. (XLSX 427Â kb)

  16. e

    Refined degree of urbanisation in Europe (DEGURBA level 2) - version 1, Jul....

    • sdi.eea.europa.eu
    • data.europa.eu
    eea:filepath +4
    Updated Jul 8, 2018
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    European Environment Agency (2018). Refined degree of urbanisation in Europe (DEGURBA level 2) - version 1, Jul. 2018 [Dataset]. https://sdi.eea.europa.eu/catalogue/srv/api/records/5de63803-6414-47a8-8230-f3d952cd7919
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    www:link-1.0-http--link, ogc:wms, esri:rest, www:url, eea:filepathAvailable download formats
    Dataset updated
    Jul 8, 2018
    Dataset provided by
    European Environment Agency
    License

    http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitationshttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations

    Time period covered
    Jan 1, 2011 - Dec 31, 2012
    Area covered
    Description

    This dataset presents the refined version of the degree of urbanisation of European countries. The degree of urbanisation relies on a population grid to classify local units. Originally the classification system was developed for the European Statistical System to classify local units into three classes (level 1): cities, towns & suburbs, and rural areas. In this version the classification was further refined (level 2) to also identify smaller individual settlements; distinguishing towns from suburbs and identifying villages, dispersed areas and mostly uninhabited areas in former rural areas class. The final classes of the refined degree of urbanisation dataset are six, namely 1) cities, 2) towns, 3) suburbs, 4) villages, 5) dispersed rural areas and 6) mostly uninhabited areas. The temporal reference is set between 2011 and 2012 because of the main inputs, the GEOSTAT population grid 2011 and the European Settlement Map 2012 from Copernicus. IMPORTANT NOTE: This metadata has been created using draft documentation provided by the European Commission, DG REGIO. This dataset has been created by the European Commission, DG Regional and Urban Policy (REGIO) in cooperation with the Joint Research Centre (JRC). Re-distribution or re-use of this dataset is allowed provided that the source is acknowledged.

  17. d

    Large carnivore distribution maps for Europe 2017 – 2022/23

    • datadryad.org
    zip
    Updated Nov 23, 2024
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    Petra Kaczensky; Nathan Ranc; Jennifer Hatlauf; John C. Payne; llya Acosta-Pankov; Francisco Álvares; Henrik Andrén; Panagiota Andri; Paola Aragno; Elisa Avanzinelli; Guna Bagrade; Vaidas Balys; Inês Barroso; Matej Bartol; Bruno Bassano; Sarah Bauduin; Carlos Bautista; Péter Bedő; Elisa Belotti; Teresa Berezowska-Cnota; Lorenzo Bernicchi; Hanna Bijl; Radames Bionda; Antonija Bišćan; Juan Carlos Blanco; Klaus Bliem; Felix Böcker; Neda Bogdanović; Virginia Boiani; Michal Bojda; Barbara Boljte; Natalia Bragalanti; Urs Breitenmoser; Henrik Brøseth; Jozef Bučko; Ivan Budinski; Luděk Bufka; Rok Černe; Roman Cherepanyn; Silviu Chiriac; Duško Ćirović; Sándor Csányi; Daniele De Angelis; Miguel de Gabriel Hernando; Laura Diószegi-Jelinek; Gundega Done; Nolwenn Drouet-Hoguet; Martin Duľa; Alexandar Dutsov; Thomas Engleder; Viktar Fenchuk; Maria Ferloni; Mauro Ferri; Stefano Filacorda; Slavomír Finďo; Urša Fležar; Lorenzo Frangini; Cathérine Frick; Christian Fuxjäger; Antonia Galanaki; Piero Genovesi; Daniela Gentile; Vincenzo Gervasi; Patrícia Gil; Giannatos Giorgos; Tomislav Gomerčić; Andrej Gonev; Jan Gouwy; Eva Gregorová; Claudio Groff; Goran Gužvica; Haris Hadžihajdarević; Samuli Heikkinen; Miklós G. Heltai; Heikki Henttonen; Annika Herrero; Bledi Hoxha; Djuro Huber; Yorgos Iliopoulos; Miranda Imeri; Gasteratos Ioannis; Gjorge Ivanov; Maja Jan; Hugh Jansman; Jasna Jeremić; Klemen Jerina; Naida Kapo; Nikoletta Karaiskou; Alexandros Karamanlidis; Jonas Kindberg; Gesa Kluth; Felix Knauer; Ilpo Kojola; Theodoros Kominos; Marjeta Konec; Petr Koubek; Josefa Krausová; Miha Krofel; Jarmila Krojerová-Prokešová; Jakub Kubala; Marko Kübarsepp; Florin Kunz; Josip Kusak; Miroslav Kutal; Stefanos Kyriakidis; Valentina La Morgia; Fatos Lajçi; Dennis Lammertsma; Luca Lapini; Roberta Latini; Pierre-Luigi Lemaitre; Alain Licoppe; John D.C. Linnell; José Vicente López-Bao; Aleksandra Majic Skrbinsek; Peep Männil; Francesca Marucco; Dime Melovski; Deniz Mengüllüoğlu; Joachim Mergeay; Yorgos Mertzanis; Simone Meytre; Tereza Mináriková; Jan Mokrý; Paolo Molinari; Anja Molinari-Jobin; Inès Moreno; Robert Mysłajek; Olivier Nägele; Ivan Napotnik; Melitjan Nezaj; Sabine Nowak; Kent Olsen; Jasmin Omeragić; Paolo Oreiller; Aivars Ornicāns; Jānis Ozoliņš; Guillermo Palomero; Aleksandar Pavlov; Aleksandar Perovic; Stefano Pesaro; Digna Pilāte; Virginia Pimenta; Lukáš Poledník; Mihai I. Pop; Vadzim Prakapchuk; Charilaos Pylidis; Pierre-Yves Quenette; Georg Rauer; Ilka Reinhardt; Slaven Reljić; Robin Rigg; Veronica Riva; Anna Maria Rodekirchen; Dainis Edgars Ruņģis; Martin Šálek; Valeria Salvatori; Maria Satra; Gergely T. Schally; Laurent Schley; Ivana Selanec; Aldin Selimovic; Nuria Selva; Jérôme Sentilles; Ilir Shyti; Sven Singer; Gregor Simčič; Magda Sindičić; Vedad Škapur; Tomaž Skrbinšek; Adam Francis Smith; Linda Smitskamp; Irina Solovej; Renata Špinkytė-Bačkaitienė; Alda Stepanova; Matija Stergar; Ursula Sterrer; Aleksandar Stojanov; Dominika Šuleková; Peter Sunde; Lidija Šver; Maciej Szewczyk; Ira Topličanec; Elisabetta Tosoni; Aleksandër Trajçe; Igor Trbojević; Tijana Trbojević; Tzoulia-Maria Tsalazidou; Elena Tsingarska-Sedefcheva; Jacopo Ursitti; Mia Valtonen; Jean-Michel Vandel; Cécile Vanpé; Rauno Veeroja; Manuela von Arx; Aleš Vorel; Bohdan Vykhor; Hannah Weber; Sybille Woelfl; Taras Yamelynets; Fridolin Zimmermann; Diana Zlatanova; Tomislav Žuglić; Jan Zukal; Agrita Žunna; Luigi Boitani (2024). Large carnivore distribution maps for Europe 2017 – 2022/23 [Dataset]. http://doi.org/10.5061/dryad.3xsj3txrc
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 23, 2024
    Dataset provided by
    Dryad
    Authors
    Petra Kaczensky; Nathan Ranc; Jennifer Hatlauf; John C. Payne; llya Acosta-Pankov; Francisco Álvares; Henrik Andrén; Panagiota Andri; Paola Aragno; Elisa Avanzinelli; Guna Bagrade; Vaidas Balys; Inês Barroso; Matej Bartol; Bruno Bassano; Sarah Bauduin; Carlos Bautista; Péter Bedő; Elisa Belotti; Teresa Berezowska-Cnota; Lorenzo Bernicchi; Hanna Bijl; Radames Bionda; Antonija Bišćan; Juan Carlos Blanco; Klaus Bliem; Felix Böcker; Neda Bogdanović; Virginia Boiani; Michal Bojda; Barbara Boljte; Natalia Bragalanti; Urs Breitenmoser; Henrik Brøseth; Jozef Bučko; Ivan Budinski; Luděk Bufka; Rok Černe; Roman Cherepanyn; Silviu Chiriac; Duško Ćirović; Sándor Csányi; Daniele De Angelis; Miguel de Gabriel Hernando; Laura Diószegi-Jelinek; Gundega Done; Nolwenn Drouet-Hoguet; Martin Duľa; Alexandar Dutsov; Thomas Engleder; Viktar Fenchuk; Maria Ferloni; Mauro Ferri; Stefano Filacorda; Slavomír Finďo; Urša Fležar; Lorenzo Frangini; Cathérine Frick; Christian Fuxjäger; Antonia Galanaki; Piero Genovesi; Daniela Gentile; Vincenzo Gervasi; Patrícia Gil; Giannatos Giorgos; Tomislav Gomerčić; Andrej Gonev; Jan Gouwy; Eva Gregorová; Claudio Groff; Goran Gužvica; Haris Hadžihajdarević; Samuli Heikkinen; Miklós G. Heltai; Heikki Henttonen; Annika Herrero; Bledi Hoxha; Djuro Huber; Yorgos Iliopoulos; Miranda Imeri; Gasteratos Ioannis; Gjorge Ivanov; Maja Jan; Hugh Jansman; Jasna Jeremić; Klemen Jerina; Naida Kapo; Nikoletta Karaiskou; Alexandros Karamanlidis; Jonas Kindberg; Gesa Kluth; Felix Knauer; Ilpo Kojola; Theodoros Kominos; Marjeta Konec; Petr Koubek; Josefa Krausová; Miha Krofel; Jarmila Krojerová-Prokešová; Jakub Kubala; Marko Kübarsepp; Florin Kunz; Josip Kusak; Miroslav Kutal; Stefanos Kyriakidis; Valentina La Morgia; Fatos Lajçi; Dennis Lammertsma; Luca Lapini; Roberta Latini; Pierre-Luigi Lemaitre; Alain Licoppe; John D.C. Linnell; José Vicente López-Bao; Aleksandra Majic Skrbinsek; Peep Männil; Francesca Marucco; Dime Melovski; Deniz Mengüllüoğlu; Joachim Mergeay; Yorgos Mertzanis; Simone Meytre; Tereza Mináriková; Jan Mokrý; Paolo Molinari; Anja Molinari-Jobin; Inès Moreno; Robert Mysłajek; Olivier Nägele; Ivan Napotnik; Melitjan Nezaj; Sabine Nowak; Kent Olsen; Jasmin Omeragić; Paolo Oreiller; Aivars Ornicāns; Jānis Ozoliņš; Guillermo Palomero; Aleksandar Pavlov; Aleksandar Perovic; Stefano Pesaro; Digna Pilāte; Virginia Pimenta; Lukáš Poledník; Mihai I. Pop; Vadzim Prakapchuk; Charilaos Pylidis; Pierre-Yves Quenette; Georg Rauer; Ilka Reinhardt; Slaven Reljić; Robin Rigg; Veronica Riva; Anna Maria Rodekirchen; Dainis Edgars Ruņģis; Martin Šálek; Valeria Salvatori; Maria Satra; Gergely T. Schally; Laurent Schley; Ivana Selanec; Aldin Selimovic; Nuria Selva; Jérôme Sentilles; Ilir Shyti; Sven Singer; Gregor Simčič; Magda Sindičić; Vedad Škapur; Tomaž Skrbinšek; Adam Francis Smith; Linda Smitskamp; Irina Solovej; Renata Špinkytė-Bačkaitienė; Alda Stepanova; Matija Stergar; Ursula Sterrer; Aleksandar Stojanov; Dominika Šuleková; Peter Sunde; Lidija Šver; Maciej Szewczyk; Ira Topličanec; Elisabetta Tosoni; Aleksandër Trajçe; Igor Trbojević; Tijana Trbojević; Tzoulia-Maria Tsalazidou; Elena Tsingarska-Sedefcheva; Jacopo Ursitti; Mia Valtonen; Jean-Michel Vandel; Cécile Vanpé; Rauno Veeroja; Manuela von Arx; Aleš Vorel; Bohdan Vykhor; Hannah Weber; Sybille Woelfl; Taras Yamelynets; Fridolin Zimmermann; Diana Zlatanova; Tomislav Žuglić; Jan Zukal; Agrita Žunna; Luigi Boitani
    Description

    Large carnivore distribution maps for Europe 2017 – 2022/23

    https://doi.org/10.5061/dryad.3xsj3txrc

    Description of the data and file structure

    The mapping approach generally follows the methods described in (Chapron et al. 2014) and (Kaczensky et al. 2013). It updates the published Species Online Layers 2012-2016 for brown bear, Eurasian lynx, wolf, golden jackal, and wolverine (Kaczensky et al. 2021; Ranc et al. 2022) for the period 2017-2022/23.

    Large carnivore presence was mapped at a 10 x 10 km (ETRS89-LAEA Europe) grid scale. This grid is widely used for Habitat Directive reporting to the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2. The map encompasses the continental EU countries plus Switzerland and Norway, and the EU candidate / potential candidate countries in the Balkan region, in addition ...

  18. Historical population of the continents 10,000BCE-2000CE

    • statista.com
    • ai-chatbox.pro
    Updated Dec 31, 2007
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    Statista (2007). Historical population of the continents 10,000BCE-2000CE [Dataset]. https://www.statista.com/statistics/1006557/global-population-per-continent-10000bce-2000ce/
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    Dataset updated
    Dec 31, 2007
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

  19. Random Forest models and maps of heavy metal and nitrogen concentrations in...

    • zenodo.org
    • data.niaid.nih.gov
    Updated Jan 21, 2020
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    Stefan Nickel; Winfried Schröder; Stefan Nickel; Winfried Schröder (2020). Random Forest models and maps of heavy metal and nitrogen concentrations in moss in 2010 across Europe, link to research data and scientific software [Dataset]. http://doi.org/10.5281/zenodo.1320242
    Explore at:
    Dataset updated
    Jan 21, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Stefan Nickel; Winfried Schröder; Stefan Nickel; Winfried Schröder
    Description

    Research data and scientific software related to a study exploring the statistical relations between the concentration of nine heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, V, Zn) and N in moss specimens collected in 2010 throughout Europe and a set potential explanatory variables (such as the atmospheric deposition calculated by use of two chemical transport models, distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils). Statistical analysis and modelling relies on Random Forest (RF). RF-models in conjunction with a Geographical Information System (GIS) were then used for mapping spatial patterns of element concentrations in moss across Europe.

  20. Z

    Data from: Distribution of large carnivores in Europe 2012 - 2016:...

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Apr 7, 2022
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    Szabó, László (2022). Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6382215
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    Dataset updated
    Apr 7, 2022
    Dataset provided by
    Molinari, Paolo
    Giannatos, Giorgos
    Guimaraes, Nuno
    Yakovlev, Yegor
    Krofel, Miha
    Bučko, Jozef
    Ranc, Nathan
    Kutal, Miroslav
    Fabijanić, Nera
    Migli, Despina
    Selanec, Ivana
    Lanszki, József
    Lapini, Luca
    Ivanov, Gjorgi
    Heltai, Miklós
    Stoyanov, Stoyan
    Zimmermann, Fridolin
    Hatlauf, Jennifer
    Reinhardt, Ilka
    Cirovic, Dusko
    Sunde, Peter
    Šálek, Martin
    Olsen, Kent
    Filacorda, Stefano
    von Arx, Manuela
    Jansman, Hugh
    Trajçe, Aleksandër
    Ionescu, Ovidiu
    Melovski, Dime
    Trbojevic, Igor
    Szabó, László
    Balys, Vaidas
    Ozoliņš, Jānis
    Männil, Peep
    Stojanov, Aleksandar
    Kowalczyk, Rafał
    Acosta-Pankov, Ilya
    Pavanello, Marco
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Europe
    Description

    Abstract

    Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.

    Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (Lynx lynx), the wolf (Canis lupus), the brown bear (Ursus arctos) and the wolverine (Gulo gulo). The golden jackal (Canis aureus) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst et al. 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović et al. 2016; Lanszki et al. 2018; Fenton et al. 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst et al. 2015; Hatlauf et al. 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.

    These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.

    Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc et al. 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though.

    Methods

    The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.

    In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2

    The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:

    1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)

    3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)

    5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)

    6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)

    7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)

    8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)

    Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).

    To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (Lynx lynx) in the Alps (Molinari-Jobin et al. 2012):

    Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);

    Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and

    Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.

    See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.

    Usage Notes

    The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.

    References

    Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3

    Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. Biological Conservation, 199: 51–55.

    Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346: 1517-1519.

    Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. Mammalian Biology, 101: 619–630.

    Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (Canis aureus) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7

    Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (Canis aureus) and its legal status in Central Europe. European Journal of Wildlife Research, 67, 25.

    Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy.

    Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a

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MAP - Population density in the European area [Dataset]. https://gimi9.com/dataset/eu_66bbe40cb23fb3b9cbf29a6e

MAP - Population density in the European area

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License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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Description

This map shows the population density in North-Eastern Europe in 2011. This map is extracted from the cartographic atlas made on the occasion of the merger of the Alsace, Champagne-Ardenne and Lorraine Regions in January 2016. It is available on the website of the Grand Est Region. This map was designed for A3 format, landscape.

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