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.

The European Population Map 2006 is a digital raster grid that reports the number of residents (night-time population) per 100 x 100 meter cells. It has been produced by downscaling census population data, at the finest available resolution, to the 100m grid cell level given pycnophylactic constraints. This downscaling is done by using data on land uses (a refined version of the Corine land cover 2006) and soil-sealing.

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.

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

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

Date des données : 21/05/2018Source des données : Natural EarthCette couche d’entités a été réalisée par Esri France avec des données de Natural Earth. Elle est en Web Mercator et couvre le monde entier. Une couche « océan » est également disponible pour cacher la carte de fonde.Cette couche d’entités est offerte par l’équipe Contenus et Services en Ligne d’Esri France. Les données sont optimisées pour l’usage dans la plateforme ArcGIS. Plus d’information sur les offres sur esrifrance.fr/contenus. Contactez-nous avec des questions ou des commentaires via info@esrifrance.fr.

Permanent resident population in Europe. Map type: Charts. Spatial extent: Europe. Time: 1950 – 2020

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.

This repository includes spatial population projections until 2100 for the different Shared Socioeconomic Pathways at 30 arc seconds (~1km) resolution for all EU countries. The projections are provided in WGS84 coordinate system.Detailed information about the projections and the model to produce them can be found in the related publication (Bonatz et al. 2025, in preparation). The python code to run the model is also provided in this repository.

This shapefile represents sovereign states of Europe for the year 1. Sovereign states are considered as sovereign or independent states all entities fulfilling the following conditions: a) a territory covering a geographic area, b) an own population, c) an authority ruling the territory and the population, d) this authority is sovereign, i.e. not subject to any other power or state. This layer is part of the Euratlas Georeferenced Vector Data collection that is composed of 21 maps, one for each century from year 1 to year 2000. These maps depict the detailed political situation of Europe at the first day of each centennial year C.E. from year 1 to 2000. Each map is composed of two kinds of layers: physical features layers, such as seas and rivers, and political features layers, such as states and cities. Some layers also cover adjacent portions of North Africa and the Middle East.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 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 lives 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 few years 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.

The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N = 717; Rwanda, N = 450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome. We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.

This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Contains all data for the map "Eastern Native Population, Early 17th Century" in the unit Native Population and Subsistence, 17th Century in the Historical Atlas of Canada Online Learning Project. View data in 'tree' view to download the data for specific maps. Documentation and file location found in the file: HACOLP_Nat_Pop_East_17C_Distribution_Info_20161207.pdf NB: Other maps in this unit not included in Byron Moldofsky's distribution folder but that we could probably make available in this same dataset: "Linguistic Families, 17th Century", "Eastern Native Population, Early 17th Century", "Native Subsistence at European Contact, Ethnohistoric Data", and Native Subsistence, 1000 CE to European Contact, Archaeological Data"

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.

The dataset contains input information used to prepare exposure maps for 37 European countries and territories from 1870 to 2020. It includes baseline land cover/use map and population map, and Excel tables with national or regional-level data on the environment, population and economy. Inofrmation on currencies and inflation can be used to convert nominal value of natural hazard-related losses to present-value euro.

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)

This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

OBSOLETE RELEASE Get the latest release at https://ghsl.jrc.ec.europa.eu/download.php

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 population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.

The map depicts flood prone areas in Europe for flood events with 200-year return period. Cell values indicate water depth (in m). The map can be used to assess flood exposure and risk of population and assets