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.

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.

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.

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

Population density 2023 (inhabitants per km²), Lorraine: 2021 Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2024 Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2418&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/3ed89eb1-9a37-4b86-b793-126411751345 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2023

Population density 2015 (inhabitants per km²), Lorraine: 2013 Territorial entities: arrondissements (Wallonie), zones d'emploi (Lorraine), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: INSEE Grand Est; SPF Economie; STATEC; Statistisches Landesamt Rheinland-Pfalz; Statistisches Amt Saarland. Harmonization: IBA / OIE 2016 Geodata sources: EuroGeographics EuroRegionalMap v3.0 - 2010. Harmonization: SIG-GR / GIS-GR 2016 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=1732&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/4f71026c-4ab0-4153-a00d-2a5d34aae307 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2015

Population density in inhabitants/ha based on block map 1: 5,000 (ISU5, spatial reference environmental atlas 2005), recording level 31.12.2009.

This indicator is calculated by dividing the projected population in a region by the region total area. The level of spatial detail of this indicator is per NUTS0 and NUTS2.

Population density 2013 (inhabitants per km²) Territorial entities: arrondissements (Wallonie), zones d'emploi (Lorraine), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: INSEE Lorraine; SPF Economie; STATEC; Statistisches Landesamt Rheinland-Pfalz; Statistisches Amt Saarland. Harmonization: IBA / OIE 2014 Geodata sources: EuroGeographics EuroRegionalMap v3.0 - 2010. Harmonization: SIG-GR / GIS-GR 2014 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=1713&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/747df575-4704-4016-b04f-d19d16d41298 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2013

Population density 2024 (inhabitants per km²) per municipality Statistical data sources: INSEE Grand Est, IWEPS, Statistisches Landesamt Rheinland-Pfalz, Statistisches Amt Saarland Geodata sources: ACT Luxembourg 2024, IGN France 2022, GeoBasis-DE / BKG 2024, NGI-Belgium 2024. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2434&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/4ba433fb-6c1e-459f-89ca-a2914eedfdaa This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2024

Population density in inhabitants/ha based on the block map 1 : 5,000 (ISU5, spatial reference environmental atlas 2015), as of December 31, 2018.

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.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Mali data available from WorldPop here.

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

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

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 data in the dataset come from the Estonian Statistical Database. Grid-based population data are updated once a year. Census data are georeferenced to building accuracy, which allows data to be aggregated to grid level of different resolution. The building's centroid was used as the basis for aggregating into squares. The data of several buildings within the square were linked to the square where the building's centroid was located. If the population data could not be linked to the building (the data were linked to the building by address and in some cases the address was not identifiable), the data were added in the middle of the village or census station. Counted homeless people are also associated with the village or precinct centre.

The 1 km x 1 km square map of the population covers the whole territory of Estonia, including only inhabited squares. Grid-based data serve as a basis for making competent decisions in the preparation of social plans and development plans, including regional development plans. Grid-based data are used in scientific research, in the private sector mainly to select the best location and to define the target group.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Rwanda data available from WorldPop here.

Indicador de vulnerabilidad. La densidad de población por municipio se obtiene de forma sencilla a partir de la población y el área de cada municipio. A partir de ésta se puede graduar la exposición en niveles. Datos desde 1998Fuente: NASTATPeriodicidad: Anual

Population density in inhabitants/ha based on the block map 1 : 5,000 (ISU5, spatial reference environmental atlas 2005), as of December 31, 2009.