Germany: Population density, people per square km: The latest value from 2023 is 238 people per square km, unchanged from 238 people per square km in 2022. In comparison, the world average is 471 people per square km, based on data from 196 countries. Historically, the average for Germany from 1961 to 2023 is 229 people per square km. The minimum value, 210 people per square km, was reached in 1961 while the maximum of 238 people per square km was recorded in 2019.

Germany DE: Population Density: People per Square Km data was reported at 238.352 Person/sq km in 2023. This records an increase from the previous number of 238.086 Person/sq km for 2022. Germany DE: Population Density: People per Square Km data is updated yearly, averaging 230.930 Person/sq km from Dec 1961 (Median) to 2023, with 63 observations. The data reached an all-time high of 238.352 Person/sq km in 2023 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.;FAO population estimates, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO); World Bank population estimates, World Bank (WB), publisher: World Bank (WB);Weighted average;

Population density (people per sq. km of land area) in Germany was reported at 240 sq. Km in 2023, 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 March of 2026.

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

Historical data for Population density in Germany from 2020 to 2026

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

In 2024, the most populated federal state in Germany is North Rhine-Westphalia in the west, with a population of almost 18 million. The state capital is Düsseldorf. Bavaria and Baden-Württemberg in the south rounded up the top three, both with over 10 million inhabitants.

Context

Population, area and population density numbers for all German towns and districts, 31.12.2018 Also the 16 states in Germany and the numbers for the whole country are summarised. Kreisfreie Städte und Landkreise nach Fläche, Bevölkerung und Bevölkerungsdichte am 31.12.2018

Content

This dataset contains the population, area and population density numbers for all German towns and districts, 31.12.2018. Each district / town and state is referenced by an unique key (Schlüsselnummer). Further info see in file description.

This dataset is the main part of the original Excel (.xlsx) file (see Metadata).

For several reasons some data had to be corrected i.e.: - shrinking original header to only one headerline - removing blanks within bigger numbers and replacing ',' by '.' within numbers - corrections in the columns 'Regionale Bezeichnung' and 'Kreisfreie Stadt - Landkreis' - removing ',' within composed district or town names - completing population numbers for the rows containing the name of a state (Bundesland) - adding population numbers for 12 districts (IdKreis 11001-11012) of Berlin (numbers taken from RKI Dashboard: https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4 ) - completing first row for whole Germany (key (Schlüsselnummer) = 0)

Hint: because some words in German use german Umlaute beware to read the file in UTF-8 format!

Acknowledgements

This dataset can be used by the public. The owner is the 'Statistisches Bundesamt' in Germany (see license information)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

***** 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.

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.

Context

Covid-19 is around in Germany for over one year. It is a chance to look retrospectivly on some governmental data and compare the last year with the years before. I set up a blog post for my German family, friends and colleagues and thounght the data might be useful for kagglers, too.

This dataset provides data about deaths in Germany. The data is available on a monthly and weekly basis grouped by gender and state. Additionaly, some data about population and population density is provided.

Content

• sonderauswertung-sterbefaelle.xlsx: Data about deaths grouped by age group, state and gender. The data is taken from the Statistisches Bundesamt and has not been modified.

Reference: Statistisches Bundesamt (Destatis), 2021 (published 2012/03/30), Sterbefälle - Fallzahlen nach Tagen, Wochen, Monaten, Altersgruppen, Geschlecht und Bundesländern für Deutschland 2016 - 2021, visited 2021/04/03, https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Sterbefaelle-Lebenserwartung/Tabellen/sonderauswertung-sterbefaelle.xlsx?_blob=publicationFile

• 02-bundeslaende.xlsx: Data population and density of German states. The data is taken from the Statistisches Bundesamt and has not been modified.

Reference: Statistisches Bundesamt (Destatis), 2020 (published 2020/09/02), Bundesländer mit Hauptstädten nach Fläche, Bevölkerung und Bevölkerungsdichte am 31.12.2019, visited 2021/04/03, https://www.destatis.de/DE/Themen/Laender-Regionen/Regionales/Gemeindeverzeichnis/Administrativ/02-bundeslaender.xlsx?_blob=publicationFile

Acknowledgements

All the data has been downloaded from the Statistisches Bundesamt. It is great that they provide public available and high quality data regularly.

Licence

The data used here are from the "Statistisches Bundesamt" (Federal Statistical Office) and are subject to the license "dl-de/by-2-0". The license text can be found at www.govdata.de/dl-de/by-2-0.

The system of social indicators for the Federal Republic of Germany - developed in its original version as part of the SPES project under the direction of Wolfgang Zapf - provides quantitative information on levels, distributions and changes in quality of life, social progress and social change in Germany from 1950 to 2013, i.e. over a period of more than sixty years. With the approximately 400 objective and subjective indicators that the indicator system comprises in total, it claims to measure welfare and quality of life in Germany in a differentiated way across various areas of life and to observe them over time. In addition to the indicators for 13 areas of life, including income, education and health, a selection of cross-cutting global welfare measures were also included in the dashboard, i.e. general welfare indicators such as life satisfaction, social isolation or the Human Development Index. Based on available data from official statistics and survey data, time series were compiled for all indicators, ideally with annual values from 1950 to 2013. Around 90 of the indicators were marked as "key indicators" in order to highlight central dimensions of welfare and quality of life across the various areas of life. The further development and expansion, regular maintenance and updating as well as the provision of the data of the system of social indicators for the Federal Republic of Germany have been among the tasks of the Center for Social Indicator Research, which is based at GESIS, since 1987. For a detailed description of the system of social indicators for the Federal Republic of Germany, see the study description under "Other documents".
The data for the area of life ´population´ is made up as follows:

Agglomeration and migration: external migration, number of immigration, net migration, share of immigration from the EU in total immigration, number of asylum seekers per 10,000 inhabitants. Population density: population density, population density in independent cities, population density in large cities, population density in communities with less than 5000 inhabitants. Regional mobility: internal migration. Burden on the working population: total burden of support (inactive population ratio), burden of supporting children (children´s quotient), burden of supporting students (education quotient), burden of supporting older people (old-age quotient). Population size, growth and structure: Population size (resident population (end of year), population growth rate, natural population growth), generative behavior (net production rate, combined birth rate, mean age at first child), population structure (proportion of the population under 15 years, proportion of the population between 15 and 15). y. and 65 y., proportion of the population over 65 years of age), ethnic structure and integration (proportion of foreigners, proportion of foreigners from the European Union, proportion of marriages between Germans and foreigners, consent for foreigners to remain). Forms of cohabitation: propensity to marry (marriage rate of 35 to 45 year olds, marriage age of single people, combined first marriage rate (= total marriage rate)), importance of stability of marriage and family (out-of-wedlock birth rate, divorce rate, combined divorce rate, remarriage rate), lifestyles and family types (Proportion of single-person households, proportion of incomplete families, proportion of non-marital partnerships, families with children, families with one child, families with two children, families with three children, families with four or more children), widowhood disparity (gender ratio of widowed people aged 65 and over). year of life), subjective evaluation of the family (ideal number of children, importance of the family, family satisfaction). Household structure: contraction tendency (proportion of 3- and 4-generation households, proportion of the population in large households (5 or more people)), solitarization (proportion of the population in single-person households).

The degree of urbanization provides urbanization level information at 100x100 meter grids for Germany. It is based on population numbers, building density and the share of building types. It is provided as a continuous percentage value representing the probability of the degree of urbanization, as well as five discrete values representing classes of the degree of urbanization: definitely ‘urban’, probably ‘urban’, area of uncertainty, probably ‘rural’, definitely ‘rural’.

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 Unions Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This statistic shows a distribution of beer sales in relation to the population in Germany as of 2024. In Thuringia and Saxony, beer sales in food retail stores made up ** percent, with * percent of the German population living in this state.

This statistic shows the size of the urban and rural populations of Germany between 1960 and 2022. Over the years recorded here, the urban population of Germany has increased, while the rural population has declined. The population of Germany has remained at approximately ** million during this period.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

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/

Number and percent of NUTS3 and population size of urban, intermediate, and rural territories; population density; and share of the population over 60 in Germany and Italy, 2021.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics