Population density (people per sq. km of land area) in Germany was reported at 240 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

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

Germany DE: Population Density: People per Square Km data was reported at 238.017 Person/sq km in 2020. This records an increase from the previous number of 237.823 Person/sq km for 2019. Germany DE: Population Density: People per Square Km data is updated yearly, averaging 228.349 Person/sq km from Dec 1961 (Median) to 2020, with 60 observations. The data reached an all-time high of 238.017 Person/sq km in 2020 and a record low of 210.173 Person/sq km in 1961. Germany DE: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

View yearly updates and historical trends for Germany Population Density. Source: World Bank. Track economic data with YCharts analytics.

The population density in Hamburg has been steadily increasing in recent years, with ***** inhabitants per square kilometer in 2023. This statistic shows the population density in Hamburg from 1995 to 2023.

***** people per square kilometer lived in Berlin in 2023. This was an increase compared to the previous year at *****. The population density has been increasing slowly during the specified period.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

239.9 (people per sq. km) in 2022. Population density is midyear population divided by land area in square kilometers.

Comprehensive socio-economic dataset for Germany including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

This statistic shows a distribution of beer sales in relation to the population in Germany as of 2023. In Thuringia and Saxony, beer sales in food retail stores made up ** percent, with * percent of the German population living in this state.

Content

A dataset of counties that are representative for Germany with regard to

the average disposable income,

the quota of divorces,

the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,

the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

Germany administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

Density of physicians of Germany rose by 1.32% from 4.5 number per thousand population in 2020 to 4.5 number per thousand population in 2021. Since the 2.36% upward trend in 2011, density of physicians soared by 18.21% in 2021.

Density of nursing and midwifery personnel of Germany decreased by 0.40% from 12.4 number per thousand population in 2020 to 12.3 number per thousand population in 2021. Since the 2.37% rise in 2019, density of nursing and midwifery personnel rose by 1.74% in 2021.

In 2024, 40-59-year-olds made up the largest age group in Germany, at around 22.3 million people. The most recent figures confirm that the next-largest age group was 65 years and older, at roughly 19 million. Aging population With the number of people belonging to older age groups visibly outstripping younger ones, in recent years it has become clear that Germany’s population is aging. In fact, figures on age structure in Germany depict a constant trend of a slowly increasing population share aged over 65 since 2012. Meanwhile, the share of population members aged 0 to 14 years has been falling, which was also reflected in the fluctuating national birth rate in recent years. A look at the future Germany’s current total population is around 83.6 million. While this number is predicted to increase, the same goes for the age group of 65 years and older. This means that the national population will continue to age.

Population density by NUTS 3 region

Data collection from official statistics and church registers

The lack of a recent summarizing description of population density in Germany that contains detailed information of pre-industrial times motivated the author of this study to undertake an analysis of population history of Northern Germany between 1740 and 1840. The goal of the study is to analyze the development of population regarding different aspects of population history and historical demographics. The author tries to connect geographic data with family data and then he relates it with economic, political and cultural development. The main part of the study ‘population dynamics’ gives an overview over demographic developments in a century characterized by demographic changes. Insights in the general changes in population size, the phases of Northern German population development and in relevant components for increases in population (e.g. decrease in mortality) are given. Finally the population determinants are developed, first in a concrete regional historic context of some areas (Marsch, nordwestliches Binnenland, Münsterland, Ostwestfalen, Ostelbien) and then more general external factors are included in the analysis. The generative structure of pre-industrial population, the industrial development, seasonal work and colonization are covered. There is an extra chapter on the development of urban population which includes the factors: urbanization, decrease in mortality, first signs of birth controls and migration. These regional considerations are opposed to an investigation of the general framework of demographical changes. In this context also grain prices and prevention from smallpox are taken into account.

Systematic of the data:

Sub-regions: 1. Holstein 2. The Hanseatic cities 3. Mecklenburg and Wester Pomerania
4. Prussia’s middle provinces
5. Core area of Lower Saxony 6. Weser-Ems-Area 7. Westphalia

Topics: 1. Births (excl. still births) 2. Deaths (incl. still births) 3. Still births 4. Marriages 5. Illegitimate births
6. Infant and child mortality 7. Population status

Mortality tables: A. Holstein (Propsteien) 1775/98, 1801/05 B. East Friesland 1775/98, 1835/39 C. County of Mark und märkische Kreise 1775/98, 1820/34 D. Kurmark 1775/98, 1835/39

Register of data tables: - Probability of death decennially in the German Reich 1881/90 - Handed down census results from Braunschweig-Lüneburg - Advances is historical tables of Westphalia
- Migration balances of Prussian government districts 1816-1840 - Population and households in Hamburg 1764-1824 - Population in Northern Germany and Germany - Approximated values for net migration 1751-1840 - Age specific decline in mortality 1775/98-1835/39 - Decline in child mortality - Fertility and marriage behavior by family reconstruction - Proportion of singles by department s and arrodissements 1811 - Average age at birth ca. 1740-ca.1840 - Regression analysis on deaths (excl. children) – marriages - Regional differences in population increases - Population density and mortality 1780-1799 - Population balances of Marschgebiete und der Fehmarn Island - Population balances of North Western Germany (without Küstenmarsch) - Budget structures of the parish Vreden 1749 - Population balances of areas with high industry densities - Budget structures of County of Mark 1798 - Budget structures in Minden-Ravensburg and Tecklenburg 1798 - Natality, mortality and cottage industry in Ravensberg 1788-1798 - North Western German areas with low birth rates
- Colonists resident in Prussia 1740-1786 - Social structure of rural population 1750 – 1790/98 - Social structure of rural population in Halberstädter - Urban population (legal definition of city) - Mortality due to tuberculosis in rural and urban areas - Average mortality rates in large cities
- Infant mortality and decline in mortality in Berlin S - Rural and urban migration balances 1741/1778-1840 - Birth rates - Cumulative elasticity of population movement - Average marriage rates in Hannover in comparison - Mortality due to smallpox - Share of infant and child mortality due to smallpox -Magnitude of the decrease in child mortality - Reduction of infant mortality - Regional differences in the decline in infant mortality

The data can be requested via order form or by personal request via email or telephone. PDF-form and contact data: http://www.gesis.org/dienstleistungen/daten/daten-historische-sozialf/querschnittsdaten/

The present study is an attempt to present the development of German livestock since the beginning of the 19th century in numbers and partly also graphically. As the objective of the investigation was to describe this development in broad terms it is based on 20 years intervals. “The information starts in 1816 because an earlier start did not seem appropriate as a consequence of the war years. The following evaluation years are 1833, 1853, 1873, 1892, 1913 and 1927. For processing the data material an appropriate demarcation of the geographical districts was of crucial importance. An appropriate unit for Prussia and Bavaria is a government district (Regierungsbezirk), for Saxony the district office (Kreishauptmannschaft), for Württemberg the district (Kreis), for Baden the federal commissioner district (Landeskommissarbezirk), for Hesse the province (Provinz) for Oldendburg the region (Landesteil) and for Alsace-Lorraine the district (Bezirk).The other regions were not subdivided. The Thuringian States have always been combined into one unit. All regions were defined after the administrative division of 1927. For Baden an earlier administrative division in 11 districts was translated into the division in four federal commissioner districts of 1927” (Ritter, a. cit., p. 5 f.). As an Introduction to the investigation an overview over the territory size of the relevant districts will be given. This data is based on the sizes of 1927; the whole district designation is based on this year. In those tables you also find data about the population in the different districts for different years of censuses because the data of the density of livestock becomes more meaningful in combination with data about population density. For the years after the foundation of the German Empire the results of the censuses for the years of 1871, 1890, 1910 and 1925 were used. It was always possible to use numbers of population level, which are only few years away from the respective livestock census years. The population level before the foundation of the German Empire was determined through a compilation of the results of censuses of the different districts. A uniform count for all German states was first performed on December 3, 1867. For the data by the year 1833, the first of the three-year census of the Zollverein in 1834 served as a basis. Also for the numbers around 1816 appropriate data was available, partly because there was a census in Prussia in 1816. In the description of the development of livestock horses, cattle, sheep, pigs and goats for all years of evaluation and chicken for 1912 and 1927 were taken into account. Mules and donkeys are not included due to their small importance; as well as all types of poultry besides - chickens - and bees and rabbits are not included, especially since there is no satisfactory information for the early years. Prussia started to identify spring cattle only in 1897(the first comprehensive census in the German Empire was carried out in 1900 with regard to the upcoming trade agreements; the first census for bees in the German Empire was carried out in 1873). “We did not succeed in fining reliable data for all regions for the time around 1916 and 1833; also for the time around 1853, some gaps still remained.However, a look at the tables on the quantities of individual livestock species shows that the missing data is almost always from small regions with little importance in the overall framework.” (Ritter, a. cit., p. 4). The basis of the representation is for all livestock species always the total number of stocks (numbers in thousands). To clearly highlight the importance of the data on the number of the different livestock species in different districts and the quantities of each livestock species per 100 inhabitants was calculated. Another part of the table describes the relations between different cattle species. “To clarify the business side of the development of the livestock sector in the last part of the study the stock of cattle of the different species is presented in relation to each of 100 cattle. Thereby a process was pursued and developed further, which for the first time was used by Th. H. Engelbrecht in his study; "The Country of the building zones except tropical countries". Besides also young cattle was recorded. In addition, the number of foals per 100 horses is given.” (Ritter, a. cit., p 10). Data tables in HISTAT:A. Territory and population A.01 Territory in square kilometers (1813-1925)A.02 Population: Headcount in 1000 (1816-1925)A.03 Population: Headcount on one square kilometer (1816-1925)B. Livestock by regions and districts B.01 Stock of horses, in 1000 animals, per one square kilometer, per 100 inhabitants (1816-1927)B.02 Stock of foals in 1000 animals, per one square kilometer (1873-1927)B.03 Stock of cattle, in 1000 animals, per one square kilometer, per 100 inhabitants (1816-1927)B.04 Stock of cows, in 1000 animals (1816-1927)B05 Stock of young cattl...

The aim of the project, which was carried out as part of the DFG project ´Local Party systems in Deutschland´, is to identify and compare existing local party systems and their development patterns at the local level since reunification. The local party systems were identified over time on the basis of four factors: 1. local political culture, 2. changes in local election law, 3. degree of organisation of local political actors and 4. structural contextual characteristics (size of municipality, local economic power, etc.). To this end, the results of the local elections from 1990 to 2016 were supplemented by local indicators of municipal statistics. There are a total of 35 indicators from the areas of population, labour market, land use and finances. Topics: 1. General information: municipality key, municipality (municipality, town, district, federation/joint municipality); classification of the municipality; federal state; administrative district to which the municipality belongs; official district key. 2. Local election data: year in which the election took place; number of eligible voters; name of candidate or list; type of candidate or list (e.g. Bundestag party); application of majority voting rights in the municipality; number of voters who participated in the elections; voter turnout; total valid votes; total and percentage number of votes for the candidate or list; seats won; total seats in the municipal council. 3. Population indicators: population figures for 2002 and 2011; population density (inhabitants per square kilometre in 2011); number of births and deaths in 2015; number of persons moved in and out above municipal boundaries in 2015; balance of persons moved in and out above municipal boundaries in 2015. 4. Labour market indicators: number of unemployed in 2016; number of employees subject to social insurance contributions in 2014 by place of work and residence. 5. Area indicators: total area in square kilometres in 2015; size of selected areas in ha in 2015: Building area (settlement and traffic area), living area, commercial area, recreation area, traffic area, agricultural area and forest area). 6. Budget indicators: taxes and parafiscal income 2014; real estate tax A (actual income of real estate tax A 2015 on agricultural land in thousands of euros); real estate tax B (actual income of real estate tax B 2015 on building or developed land in thousands of euros); total real estate tax (actual income 2015 of real estate tax A and B in thousands of euros); gross trade tax income (trade tax income 2015 without deduction of the trade tax levy in thousand euros); trade tax levy (share of trade tax 2015 in federal and state governments in thousand euros); net trade tax income (share of trade tax income 2015 for municipalities in thousand euros); income tax (municipal share of income tax 2015 in thousands of euros); value added tax (municipal share of value added tax 2015 in thousands of euros); gross income (gross income generated by the municipality in 2014; gross expenses (total expenses incurred in 2014 in the public budget); net expenses (expenses in 2014 less expenses for other public budgets); loans (loans and internal loans 2014); debts (debts of municipalities/associations of municipalities 2009 in thousands of euros by district/cities; allocations (general allocations around apportionments 2014 of federal, state, municipal); investment promotion (allocations and subsidies for investment promotion 2014). Ziel des Projektes, das im Rahmen des DFG-Projektes ´Lokale Parteiensysteme in Deutschland´ durchgeführt wurde, ist es, die bestehenden lokalen Parteiensysteme und ihre Entwicklungsmuster seit der Wiedervereinigung auf kommunaler Ebene zu identifizieren und zu vergleichen. Die lokalen Parteiensysteme wurden im Zeitverlauf anhand von vier Faktoren identifiziert: 1. lokale politische Kultur, 2. Veränderungen des Kommunalwahlrechts, 3. Organisationsgrad der lokalpolitischen Akteure sowie 4. strukturelle Kontextmerkmale (Gemeindegröße, lokale Wirtschaftskraft u.ä.). Dazu wurden die Ergebnisse der Kommunalwahlen der Jahre 1990 bis 2016 um lokale Indikatoren der Kommunalstatistik ergänzt. Es finden sich insgesamt 35 Indikatoren aus den Bereichen Bevölkerung, Arbeitsmarkt, Flächennutzung und Finanzen. Themen: 1. Allgemeine Informationen: Gemeindeschlüssel, Ort (Gemeinde, Stadt, Kreis, Verbands-/Samtgemeinde); Klassifizierung des Ortes; Bundeslandkennung; Bundesland; Landkreis, zu dem die Gemeinde gehört; amtlicher Kreisschlüssel. 2. Kommunalwahldaten: Jahr, in dem die Wahl stattfand; Anzahl der Wahlberechtigten; Name des Kandidaten bzw. der Liste; Art des Kandidaten/der Liste (z.B. Bundestagspartei); Anwendung des Mehrheitswahlrechts in der Gemeinde; Anzahl der Wähler, die an den Wahlen teilgenommen haben; Wahlbeteiligung; gültige Stimmen gesamt; Anzahl der Stimmen für den Kandidaten/die Liste gesamt und in Prozent; gewonnene Sitze; Sitze in der Gemeindevertretung insgesamt. 3. Bevölkerungsindikatoren: Einwohnerzahlen des Ortes der Jahre 2002 und 2011; Einwohnerdichte (Einwohner pro Quadratkilometer 2011); Anzahl der Geburten und der Sterbefälle 2015; Anzahl der zugezogenen und der weggezogenen Personen über Gemeindegrenzen 2015; Saldo der zu- und weggezogenen Personen über Gemeindegrenzen 2015. 4. Arbeitsmarktindikatoren: Anzahl der Arbeitslosen 2016; Anzahl der sozialversicherungspflichtig Beschäftigten 2014 nach Arbeitsort und nach Wohnort. 5. Flächenindikatoren: Fläche 2015 insgesamt in Quadratkilometern; Größe ausgewählter Flächen jeweils 2015 in ha: Baufläche (Siedlungs- und Verkehrsfläche), Wohnfläche, Gewerbefläche, Erholungsfläche, Verkehrsfläche, Landwirtschaftsfläche und Waldfläche). 6. Haushaltsindikatoren: Steuern und steuerähnliche Einnahmen 2014; Grundsteuer A (Istaufkommen Grundsteuer A 2015 auf landwirtschaftliche Flächen in Tausend Euro); Grundsteuer B (Istaufkommen Grundsteuer B 2015 auf bauliche oder bebaute Flächen in Tausend Euro); Grundsteuer Gesamt (Istaufkommen 2015 von Grundsteuer A und B in Tausend Euro); Bruttogewerbesteuereinnahmen (Gewerbesteuereinnahmen 2015 ohne Abzug der Gewerbesteuerumlage in Tausend Euro); Gewerbesteuerumlage (Anteil der Gewerbesteuer 2015 an Bund und Länder in Tausend Euro); Nettogewerbesteuereinnahmen (Anteil der Gewerbesteuereinnahmen 2015 für Gemeinde in Tausend Euro); Einkommenssteuer (Gemeindeanteil an der Einkommenssteuer 2015 in Tausend Euro); Umsatzsteuer (Gemeindeanteil an der Umsatzsteuer 2015 in Tausend Euro); Bruttoeinnahmen (Bruttoeinnahmen, die die Gemeinde 2014 erzielt hat; Bruttoausgaben (insgesamt getätigte Ausgaben 2014 im öffentlichen Haushalt); Nettoausgaben (Ausgaben 2014 abzüglich von Ausgaben für andere öffentliche Haushalte); Kredite (Kredite und innere Darlehen 2014); Schulden (Schulden der Gemeinden/Gemeindeverbände 2009 in Tausend Euro nach Kreisen/Städte; Zuweisungen (allgemeine Zuweisungen um Umlagen 2014 von Bund, Ländern, Gemeinden); Investitionsförderung (Zuweisungen und Zuschüsse für Investitionsförderung 2014).