Denmark has, by far, the highest population density of the Nordic countries. This is related to the fact that it is the smallest Nordic country in terms of land area. Meanwhile, Iceland, which has the smallest population of the five countries, also has the lowest population density. As the total population increased in all five countries over the past decade, the population density also increased.

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

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.

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.

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.

The number of COVID-19 deaths reported from European countries has varied more than 100-fold. In terms of coronavirus transmission, the relatively low death rates in some countries could be due to low intrinsic (e.g. low population density) or imposed contact rates (e.g. non-pharmaceutical interventions) among individuals, or because fewer people were exposed or susceptible to infection (e.g. smaller populations). Here we develop a flexible empirical model (skew-logistic) to distinguish among these possibilities. We find that countries reporting fewer deaths did not generally have intrinsically lower rates of transmission and epidemic growth, and flatter epidemic curves. Rather, countries with fewer deaths locked down earlier, had shorter epidemics that peaked sooner, and smaller populations. Consequently, as lockdowns are eased we expect, and are starting to see, a resurgence of COVID-19 across Europe.

The population density of Spain maintained a steadily at a rate of over 92 inhabitants per square kilometers in the last decade, with the latest figures revealing a density of 95 people per square kilometer in 2022. Spain’s degree of urbanization is rather high, with levels reaching over 81 percent of urbanization in the country. Andalusia, with a total number of 8.6 million inhabitants, ranked first on the list of most populous autonomous communities in Spain.

Population density: a world of contrast

Spain is far from the European Union’s average population density, which stood at approximately 111.89 people per square kilometer in 2021, that is, a difference of over 17 people per square meter below the average. Monaco, the country with the highest population density in the world, featured about 24,621 inhabitants per square kilometer, making Spain’s population density look minimal. The results in Macao were very similar, with a population density that reached over 21,000 people per square kilometer.

The re-population of a country

The population of Spain declined for many years during the economic recession, returning to a positive trend after 2015. The Spanish population is projected to increase by nearly two million by 2028 compared to 2024. Despite this expected increase, Spain has one of the lowest fertility rate in the European Union, with barely 1.29 children per woman according to the latest reports.

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.

Exposure indicator. Population density per municipality can be calculated easily based on the population and the area of each municipality. Using this, the exposure can then be graded in levels. Similarly, other studies uses home density in residential areas (Homes/ha.) (2008), categorising the exposure into four levels:Homes/ha. > 100 –> Very high exposure65 < homes/ha. < 100 –> High exposure50 < homes/ha. < 65 –> Average exposureHomes/ha. < 50 –> Low exposureData since 1998Source: INEMonitoring: Annual

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.

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.

Tanzania data available from WorldPop here.

A. Population genetic parameters of the red deer administrative management units (AMUs) in North Rhine-Westphalia (NRW). B. Population genetic parameters of the red deer administrative management units (AMUs) in Hesse.

Demographic parameters are key to understanding population dynamics. Here, we analyse the survival and reproduction of the German wolf population in the 20 years following recolonization. Specifically, we analysed the effects of environmental, ecological, and individual characteristics on i) the survival probability of the population; ii) annual survival rates of age classes; iii) reproduction probability; and iv) reproductive output, measured as the number of detected pups/juveniles. Using the Cox proportional hazards model, we estimated a median survival time of circa 3 years for wolves. Annual survival probabilities were found to be 0.75 for juveniles, 0.75 for subadults, and 0.88 for adults. Survival was lower for juveniles in winter and for subadult males in summer, probably associated with dispersal events. Low habitat suitability was clearly associated with lower survival in juveniles and subadults, but not in adults. Local territory density was related to increased survival. Reproduction probability within a territory was 0.89, but explanatory variables had no effect. Reproductive output was four pups/juveniles on average, positively related to habitat suitability and female experience, but negatively related to territory density. Survival values were very high for the species when compared to other regions. We hypothesize that carrying capacity has not been reached in the study area, thus the survival may decrease in the future if the landscape becomes saturated. Furthermore, our results highlight a spatial pattern in survival and reproduction, with areas of better habitat suitability favouring faster population growth. Thus, targeting conservation measures to low habitat suitability areas will have a strong population effect in the short term by boosting the survival and reproduction of the individuals, while long-term viability should be carefully planned with high suitability areas in mind, as those contain the territories with higher survival and reproduction potential. Methods Wolf individual and territory data for survival and reproduction analyses were provided by the Federal Documentation and Consultation Centre on Wolves (DBBW, www.dbb-wolf.de) and by the Senckenberg Centre for Wildlife Genetics. Information about individuals and territories was grouped into monitoring years (from the 1st of May to the 30th of April next year), starting in 2000 until 2020 (April 2021). Individuals were identified genetically and for the survival analysis, the original dataset was filtered to retain only reliable information on the lifespan of the animals. Thus, individuals with NA ("not available") in the variables 'sex' or 'date of birth' as well as individuals born or died outside the German border were removed, as the environmental data included in the demographic analyses were only available for Germany. Consequently, the status of the individuals (dead, alive) was assessed until April 2021. The age classes were defined as juveniles including pups (0-12 months), subadults (13-24 months), and adults (> 24 months) (Mech and Boitani, 2003). The final dataset contained a total of 1054 individuals. Reproduction data was analysed at the territory level. The number of juvenile counts might be less than the actual number of pups born, thus we defined this variable as ‘minimum reproductive output’. Territories with more than 10 observed pups/ juveniles were removed from analyses to account for the fact that such a high number of pups might stem from a double reproduction and thus belong to one or more females (n = 4). In addition, territories from the first year of pair formation were removed (n = 227), because pairs typically form shortly before or during the breeding season (in autumn or winter) and therefore, there is no opportunity for reproduction in the months prior to the pair formation, which would correspond to the reproduction in the first year in the dataset. The final dataset consisted of 723 entries comprising 205 different territories with data from 1-16 years per territory. Explanatory variables We analysed the survival and reproduction of wolves in relation to environmental and ecological conditions and individual characteristics. For the survival analysis, we used as environmental variables the wolf habitat suitability (Planillo et al., 2024) in an 8 km radius of the territory centroid, wolf local territory density for each year in a 50 km radius, season defined as summer (May-Oct) or winter (Nov-Apr), individual sex and age, with the latter being classified as age classes: juveniles < 12 months, subadults 12 to 24 months, and adults > 24 months old. For the reproduction analysis, environmental and ecological conditions were described by habitat suitability values and local territory density around each breeding territory. As individual characteristics, we included the experience of the reproductive female in the models, measured sequentially as the number of years the same breeding female had reproduced, i.e., the first year that the female reproduced was considered year 1, the second year 2, and so forth. Data Analysis

Survival analysis: Survival analysis was calculated for the whole population and for each of the age classes using Cox Proportional Hazards Regression (Therneau and Grambsch 2000).

Reproduction analysis: We analyzed reproduction patterns for i) the probability of reproduction in a territory and ii) the total number of juveniles per reproductive event. In both cases, data were analysed using generalised linear mixed-effects models (GLMMs) with binomial error distribution and logit link for reproduction probability and Poisson error distribution and log link for the reproductive output. Territory identity was included as a random effect. The total number of years that a territory was monitored in the dataset was included as a weighting variable to avoid an inflated effect of the territories observed only for one year. As explanatory variables, we included the mean habitat suitability of the territory, local territory density in a buffer of 50 km, and the quadratic effect of the experience of the female as fixed effects.

Population growth: We used the values of survival and reproduction to estimate population growth (λ) and contrast it with the observed data. We developed a population matrix model using three age classes, based on obtained values of reproduction and annual survival for the age classes, and used the eigenvalue of the matrix as our λ. We explored the observed population growth values with respect to the effects of the minimum and maximum values of habitat suitability. To compute the lambda for the latter cases, we predicted the survival of juveniles and subadults and the number of pups per reproduction in areas with the lowest and highest observed values of habitat suitability.

The threat of isolation to red deer (Cervus elaphus) has been described in numerous European studies. The consequences range from reduced genetic diversity and increased inbreeding to inbreeding depression. It has been shown that the underlying factors cannot be generalised, but vary greatly in their effects depending on local conditions. The aim of this study was to analyse in detail the genetics of red deer in a large German federal state with a population density of 532 inhabitants per km2 and 23.8% settlement and traffic area, in order to generate data for future management of the region. 1199 individual samples of red deer were collected in all 20 Administrative Management Units (AMUs) and compared with existing results from the neighbouring state of Hesse (19 AMUs). All 2490 individuals from both states were clustered using Bayesian methods and connectivity between neighbouring AMUs was quantified. Overall, 30% of the AMUs were found to be highly isolated, mostly with effective population sizes (Ne) < 100. In contrast, 47.5% of the AMUs still had clear connectivity, allowing them to be merged into 4 larger red deer regions. For the small isolated areas, low genetic diversity was found in units with high homozygosity and low Ne. With high sampling density and identical methodology, detailed information on AMUs can be obtained and the degree of vulnerability of individual AMUs as part of the overall population can specifically be validated. Such data can help improve future wildlife management.

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.

Zimbabwe data available from WorldPop here.

A. Description of the red deer administrative management units (AMUs) in North Rhine-Westphalia (NRW). B. Description of the red deer administrative management units (AMUs) in Hesse.

A. The effective population sizes of AMUs in NRW, as well as the regions and states according to NeEstimator, Wang et al. [57] and Caballero [58]. B. Effective population sizes of AMUs in Hesse, regions and states according to NeEstimator, Wang et al. [57] and Caballero [58].

Mean values of population genetic parameters by federal state and region.

Estimation of the population size is essential for understanding population dynamics. Estimating animal density using multiple methods and/or multiple attempts is required for accurate estimations. Raccoon dog (Nyctereutes procyonoides) is native to East Asia, including Japan, and has become an invasive species in Europe. Information on raccoon dog density in their native range is important to understand their invasion; however, relatively few studies have been conducted on raccoon dog density in their native range. In this study, we extracted DNA from fecal samples of raccoon dogs inhabiting a small island in Japan and conducted density estimation over two periods using DNA capture-recapture methods: CAPWIRE and SECR. We also investigated sex ratio using genetic sex identification. Density estimates using SECR were approximately threefold different between the two study periods: 17.2 individuals per km2 in 2018 and 49.0 individuals per km2 in 2020. In contrast, estimates using CAPWIRE were relatively stable: 21.7 individuals per km2 in 2018 and 24.3 individuals per km2 in 2020. A drastic increase or decrease is not expected during the study period, and thus, density estimates using CAPWIRE are more reasonable than those using SECR. The small number of samples per individual might result in low accuracy of density estimates by SECR. The density estimated by CAPWIRE was similar to that in the main island in Japan and higher than that in Europe. Feeding competition with other omnivorous carnivores and/or predation risk by wolves might maintain the low density in Europe. The sex ratio of raccoon dogs was 1:1, which was similar to the values in invasive raccoon dogs and other canids. Further genetic census, including sex identification in various landscapes in their native and invasive range, will enable us to understand not only the ecology of raccoon dogs but also their adaptations to their invading areas.

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