In 2022, the population density in the European Union remained nearly unchanged at around 112.05 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.

Population density (people per sq. km of land area) in European Union was reported at 112 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. European Union - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Our Population Density Grid Dataset for Western Europe offers detailed, grid-based insights into the distribution of population across cities, towns, and rural areas. Free to explore and visualize, this dataset provides an invaluable resource for businesses and researchers looking to understand demographic patterns and optimize their location-based strategies.

By creating an account, you gain access to advanced tools for leveraging this data in geomarketing applications. Perfect for OOH advertising, retail planning, and more, our platform allows you to integrate population insights with your business intelligence, enabling you to make data-driven decisions for your marketing and expansion strategies.

Ratio between the annual average population and the land area. The land area concept (excluding inland waters, such as lakes, wide rivers, estuaries) should be used wherever available; if not available, then the total area (including inland waters) is used.

Each year Eurostat collects demographic data at regional level from EU, EFTA and Candidate countries as part of the Population Statistics data collection. POPSTAT is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration at national and regional levels by various breakdowns (for the full overview see the Eurostat dedicated section). More specifically, POPSTAT collects data at regional levels on:

• population stocks;
• vital events (live births and deaths).

Each country must send the statistics for the reference year (T) to Eurostat by 31 December of the following calendar year (T+1). Eurostat then publishes the data in March of the calendar year after that (T+2).

Demographic data at regional level include statistics on the population at the end of the calendar year and on live births and deaths during that year, according to the official classification for statistics at regional level (NUTS - nomenclature of territorial units for statistics) in force in the year. These data are broken down by NUTS 2 and 3 levels for EU countries. For more information on the NUTS classification and its versions please refer to the Eurostat dedicated pages. For EFTA and Candidate countries the data are collected according to the agreed statistical regions that have been coded in a way that resembles NUTS.

The breakdown of demographic data collected at regional level varies depending on the NUTS/statistical region level. These breakdowns are summarised below, along with the link to the corresponding online table:

NUTS 2 level

• Population by sex, age and region of residence — demo_r_d2jan
• Population on 1 January by age group, sex and region of residence — demo_r_pjangroup
• Live births by mother's age, mother's year of birth and mother's region of residence — demo_r_fagec
• Deaths by sex, age, and region of residence — demo_r_magec

NUTS 3 level

• Population on 1 January by sex, age group and region of residence — demo_r_pjangrp3
• Population on 1 January by broad age group, sex and region of residence — demo_r_pjanaggr3
• Live births (total) by region of residence — demo_r_births
• Live births by five-year age group of the mothers and region of residence — demo_r_fagec3
• Deaths (total) by region of residence — demo_r_deaths
• Deaths by five-year age group, sex and region of residence — demo_r_magec3

This more detailed breakdown (by five-year age group) of the data collected at NUTS 3 level started with the reference year 2013 and is in accordance with the European laws on demographic statistics. In addition to the regional codes set out in the NUTS classification in force, these online tables include few additional codes that are meant to cover data on persons and events that cannot be allocated to any official NUTS region. These codes are denoted as CCX/CCXX/CCXXX (Not regionalised/Unknown level 1/2/3; CC stands for country code) and are available only for France, Hungary, North Macedonia and Albania, reflecting the raw data as transmitted to Eurostat.

For the reference years from 1990 to 2012 all countries sent to Eurostat all the data on a voluntary basis, therefore the completeness of the tables and the length of time series reflect the level of data received from the responsible National Statistical Institutes’ (NSIs) data provider. As a general remark, a lower data breakdown is available at NUTS 3 level as detailed:

• population data are broken down by sex and broad age groups (0-14, 15-64 and 65 or more). The data have this disaggregation since the reference year 2007 for all countries, and even longer for some — demo_r_pjanaggr3
• vital events (live births and deaths) data are available only as totals, without any further breakdown — demo_r_births and demo_r_deaths

Demographic indicators are calculated by Eurostat based on the above raw data using a common methodology for all countries and regions. The regional demographic indicators computed by NUTS level and the corresponding online tables are summarised below:

NUTS 2 level

• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind2
• Fertility indicators by region of residence — demo_r_find2
• Fertility rates by age and region of residence — demo_r_frate2
• Life table by age, sex and region of residence — demo_r_mlife
• Life expectancy by age, sex and region of residence — demo_r_mlifexp
• Infant mortality rates by region of residence — demo_r_minfind

NUTS 3 level

• Population change - Demographic balance and crude rates at regional level — demo_r_gind3
• Population density by region — demo_r_d3dens
• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind3
• Fertility indicators by region of residence (total fertility rate, mean age of woman at childbirth and median age of woman at childbirth) — demo_r_find3

Notes:

1) All the indicators are computed for all lower NUTS regions included in the tables (e.g. data included in a table at NUTS 3 level will include also the data for NUTS 2, 1 and country levels).

2) Demographic indicators computed by NUTS 2 and 3 levels are calculated using input data that have different age breakdown. Therefore, minor differences can be noted between the values corresponding to the same indicator of the same region classified as NUTS 2, 1 or country level.

3) Since the reference year 2015, Eurostat has stopped collecting data on area; therefore, the table 'Area by NUTS 3 region (demo_r_d3area)' includes data up to the year 2015 included.

4) Starting with the reference year 2016, the population density indicator is computed using the new data on area 'Area by NUTS 3 region (reg_area3).

Historical dataset showing European Union population density by year from 1961 to 2022.

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.

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.

Between 2015 and 2022, Estonia had the highest density of “right to be forgotten” or “right to erasure” requests issued to Google and Microsoft Bing, among other European countries, with almost 59 appeals per 10 thousand inhabitants. Registering the highest number of requests during the analyzed period, France ranked second regarding request density, with 46.2 requests per 10 thousand inhabitants.

The purpose of this data package is to offer essential population statistics about European countries covering static and dynamic demographical indicators. The two current sources of information are the International Institute for Applied Systems Analysis (IIASA), from Austria and the U.K. Office for National Statistics.

Population density by NUTS 3 region

Studies of modern famines tend to consider them ‘man-made’, resulting from war or from adverse shocks to food entitlements. This view has increasingly been applied to historical famines, against the earlier Malthusian orthodoxy. We use a novel dataset and temporal scan analysis to identify periods when famines were particularly frequent in Europe, from ca. 1250 to the present. Up to 1710, the main clusters of famines occurred in periods of historically high population density. This relationship disappears after 1710. We analyse in detail the famines in England, France and Italy during 1300–1850, and find strong evidence that before 1710 high population pressure on resources was by far the most frequent remote cause of famines (while the proximate cause was almost invariably meteorological). We conclude, in contrast with the currently prevailing view, that most preindustrial famines were the result of production, not distribution issues. Only after 1710 did man-made famines become prevalent.

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.

Since larger scales consider areas with no or only sporadical occupation, density reduces considerably. Maximum (1), mean (2), and minimum (3) estimate of persons and population density (persons per 100km2).

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 focus of this domain is on the European Neighbourhood Policy (ENP) countries on the southern and eastern shores of the Mediterranean (ENP-South), namely:

• Algeria (DZ),
• Egypt (EG),
• Israel (IL),
• Jordan (JO),
• Lebanon (LB),
• Libya (LY),
• Morocco (MA),
• Palestine (PS) (¹),
• Syria (SY) and
• Tunisia (TN).

An extensive range of indicators is presented in this domain, including indicators from almost every theme covered by European statistics. Only annual data are published in this domain.

The data and their denomination in no way constitute the expression of an opinion by the European Commission on the legal status of a country or territory or on the delimitation of its borders.

Data supplied by and under the responsibility of the national statistical authorities of each of the countries or territories.

(¹) This designation shall not be construed as recognition of a State of Palestine and is without prejudice to the individual positions of the Member States on this issue.

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.

This study identifies economic and demographic factors contributing to net migration by utilizing European Union data and satellite nighttime light data. The results of fixed-effect spatial models reveal a significant spillover effect of the migrant population and a strong spillover effect of job opportunities in neighboring areas as pulling factors for migrants. Contrary to the assumptions of the classic gravity model, the findings show a surprising negative relationship between local population density and migration flow.

Project D4 of the CRC 806 focuses on the question, as to whether the presence, or resp., the absence of humans can be interpreted as a response to natural or cultural environment by integrating data, methods and results from various former CRC-projects and from literature. This integrative approach is based on the analysis of chronological, spatial, geoarchaeological, archaeobiological and cultural data. One of the main questions concerns the reconstruction of diet and mobility of humans and animals. Based on the hypothesis that an increase in the consumption of meat correlates with lower population densities and perhaps higher mobility, we are interested in isotopic measurements of human and animal bones. The collected data will also allow for the reconstruction of foodwebs of various archaeological periods. This also includes the creation of a database of isotopic data.