Actual value and historical data chart for Germany Population Density People Per Sq Km

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;

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.

238.0 (people per sq. km) in 2020. 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.

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

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.

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

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.

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).

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 82 million during this period.

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.

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.

In the immediate aftermath of the Second World War, Germany was split into four zones, each administered by France, the United Kingdom, the United States and the Soviet Union respectively. In 1949, the Soviet-controlled zone formed the German Democratic Republic (East Germany), while the rest became the Federal Republic of Germany (West Germany). In this time, Berlin was also split into four zones, and the three non-Soviet zones formed West Berlin, which was a part of West Germany (although the West's administrative capital was moved to Bonn). One population grows, while the other declines Between 1949 and 1961, an estimated 2.7 million people migrated from East to West Germany. East Germany had a communist government with a socialist economy and was a satellite state of the Soviet Union, whereas West Germany was a liberal democracy with a capitalist economy, and western autonomy increased over time. Because of this difference, West Germany was a much freer society with more economic opportunities. During the German partition, the population of the west grew, from 51 million in 1950 to 62.7 million in 1989, whereas the population of East Germany declined from 18.4 million to just 16.4 million during this time. Little change after reunification In 1989, after four decades of separation, the process of German reunification began. The legal and physical barriers that had split the country were removed, and Germans could freely travel within the entire country. Despite this development, population growth patterns did not change. The population of the 'new states' (East Germany) continued to decline, whereas the population of the west grew, particularly in the 1990s, the first decade after reunification. The reasons for this continued imbalance between German population in the east and west, is mostly due to a low birth rate and internal migration within Germany. Despite the fact that levels of income and unemployment in the new states have gotten closer to those reported for the west (a major obstacle after reunification), life and opportunities in the west continue to attract young Germans from rural areas in the east with detrimental effect on the economy and demography of the new states.

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/

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).

The graph presented in this dataset contains all German municipalities and their metadata keyed by NUTS Level 3, provided by the DE BKG as of 06/2019 - changed. All data renderings provided here were done in Cytoscape 3.7.1 (Season file for replication purposes is included in the archives). NUTS is a categorization system for European regions ranging from whole nations (NUTS 0), countries (NUTS 1 and 2), municipalities (NUTS 3) to even smaller regions in some cases (LAU). The AGS (amtlicher Gemeindeschlüssel), which is unique to germany, is also included in the Metadata. Thus the data can be linked with european and german institution sourced data at the same time.

The archive files contain the following items:

• Edge-List (NutsA-NutsB) wth NUTS only

• Node-List with all metadata provided by BKG files.

• Example Edge-List with Population-Density-Metric connection values

• Season-File for Cytoscape 3.7.1 to reproduce the renderings in this paper

All table files are in CSV format with UTF-8 encoding and ; delimiter. Special character like ö, ä, ß, ü have been replaced (with oe, ae, ss, ue) to ensure compatibility.

Habitat and density effects on the demography of an expanding wolf population in Central Europe

https://doi.org/10.5061/dryad.dncjsxm5m

Tables containing the data used in the demographic analyses of the German wolf population.

Description of the data and file structure

Two tables are provided:

• data_wolf_survival_table.csv. This table contains individual information used in the survival analyses: information on the number of weeks an individual was alive (weeks_date) until confirmed death (status = 1) or went missing (censored, status = 0), sex, the month of the year that the individual was last detected (death_month), the season of the last detection, habitat suitability of the natal (hs_8km_natal) and final territories (hs_8km_final), the information to calculate the territory density in the 50 km buffer in the natal (_first) and final (_last) territories (nterr_buffer50 = the number of territories in buffer...