About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in New Germany. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Germany, the median income for all workers aged 15 years and older, regardless of work hours, was $53,438 for males and $33,889 for females.

These income figures highlight a substantial gender-based income gap in New Germany. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the city of New Germany.

- Full-time workers, aged 15 years and older: In New Germany, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,778, while females earned $47,813, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in New Germany.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Germany median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the New Germany population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Germany. The dataset can be utilized to understand the population distribution of New Germany by age. For example, using this dataset, we can identify the largest age group in New Germany.

Key observations

The largest age group in New Germany, MN was for the group of age 25 to 29 years years with a population of 88 (15.15%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Germany, MN was the 85 years and over years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Germany is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Germany total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Germany Population by Age. You can refer the same here

Unemployment Rate in Germany remained unchanged at 6.30 percent in June. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The “Refugees in Germany” survey is part of a research project commissioned by the German Federal Ministry of Labor and Social Affairs (BMAS) under the title of “Accompanying evaluation of labor market programs to integrate refugees”. Aim and Conceptualisation The aim of the research project was to analyze how effective and efficient the central labor market programs in the legal areas of SGB II and SGB III are with regard to the labor market integration and social participation of refugees who arrived in Germany since 2015. A central component of this project was a survey of refugees (“Refugees in Germany”), which is conceptually related to the (IAB-BAMF-SOEP Survey of Refugees), that has been running since 2016. In contrast to the IAB-BAMF-SOEP Survey of Refugees, however, it is not a household survey, but an individual survey that is not representative of the refugee population in Germany. It is based on a gross sample of refugees who arrived in Germany in 2015 or later, and had started or could have started one of five different types of labor market integration programs between August 1, 2017 and September 11, 2018. The focus is on the following five programs: activation measures (employer-based or with training company), occupational choice and apprenticeship measures (pre-entry support and qualifications or accompanying training support), measures for further vocational training, employment subsidies, and job creation schemes. The gross sample of program participants and non-participants, on which the survey is based, was obtained from administrative data held by the German Federal Employment Agency (Bundesagentur für Arbeit). The sample included in the survey basically consists of two main groups: a treatment group and a control group. The treatment group (participants) is divided into five sub-populations to represent participants in the five program types to be evaluated. The control group includes people who, at least in principle, have a sufficient probability of participating in the program, but who actually did not participate at the time the address was selected. The group of these non-participants is divided into two subpopulations and contains either people who are assigned to exactly one of the program types or who are eligible for two of the program types. Contents The main survey topics comprise the background of the interviewed refugees (way to Germany, education and work experience abroad); length of stay in Germany; labor market and educational experiences in Germany (employment, vocational training, internships, attending general schools and studying); help for integration (language courses, vocational orientation, competence assessment and activation, support related to vocational training, aids accompanying the internship); economic situation (finances, housing); and social participation (current language skills, social contacts, normal everyday life, health and well-being, labor market orientation and labor market knowledge, identification with Germany, personality traits and culture).

About this dataset

Context

The dataset tabulates the population of New Germany by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of New Germany across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.01% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the New Germany is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of New Germany total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Germany Population by Race & Ethnicity. You can refer the same here

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (2 Hours in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 76174 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the German population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

Context

This dataset contains weather details of five most important countries including Germany and Italy which was affected greatly with Covid_19 spread.

Content

It is believed that climate conditions might be one of the major reasons for the spread of covid_19. This Dataset contains climate changes occured from 19th February to 17th April 2020. This contains the climate changes recorded for every 10 mins on the aforementioned countries.

File Description

The file contains below columns:

Temperature - Actual Temperature Recorded in degree celsius Wind_speed - Wind Speed Description - Description of the current weather Weather - Categorical value depicts the types of weather name - Depicts the country name temp_min - Minimum temperature recorded temp_max - Maximum temperature recorded

Other variables are pretty much self explanatory.

Acknowledgements

As part of my thesis project, this dataset was being prepared with a help of web scraper which will trigger an open source REST API end point for every 10 minutes. It was hosted in an EC2 instance which will update a CSV file periodically. Thought that this could contribute for the analysis of Covid_19 spread, hence shared the same.

Hope this could be useful!

Inspiration

As mentioned earlier, Climate could be one of the significant factors which spreads covid_19. Need to analyse further on the same. Italy could be considered for the research as we have the climate data for that country. Alongside, this country was affected largely.

This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.

Mica - Muskrat and Coypu and Raccoon Occurrences collected by ITAW, Germany is an occurrence dataset published by the Research Institute of Nature and Forest (INBO) and ITAW (Institute for Terrestrial and Aquatic Wildlife Research. It is part of the LIFE MICA - Management of Invasive Coypu and muskrat in Europe project on Muskrat monitoring networks in Flanders, The Netherlands and Germany. This dataset contains Muskrat, Raccoon and Coypu counts. Here it is published as a standardized Darwin Core Archive and includes for each occurrence record an recordID, date, location, samplingProtocol, the number of recorded individuals, status (present/absent) and scientific name. Issues with the dataset can be reported at https://github.com/inbo/muskrat-uvw-occurrences/issues We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be.

The datasets included in this repository represent a pandemic severity indicator for the COVID-19 pandemic in Germany based on a composite indicator for the years 2020 and 2021. The pandemic severity index consists of three indicators: the incidence of patients tested positive for COVID-19, the incidence of patients with COVID-19 in intensive care, and the incidence of registered deaths due to COVID-19. The datasets have been developed within the CODIFF project (Socio-Spatial Diffusion of COVID-19 in Germany) at Leibniz Insitute for Research on Society and Space. The project received funding by Deutsche Forschungsgemeinschaft (DFG, project number 492338717). The datasets have been used in the following publications, in which further methodological details on the indicator can be found:

Stabler, M., & Kuebart, A. (2023). Tempo-spatial dynamics of COVID-19 in Germany: A phase model based on a pandemic severity indicator. medRxiv, 2023-02.

Kuebart, A., & Stabler, M. (2023). Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany. Spatial and Spatio-temporal Epidemiology, 2023.

This repository consists of two files:

pandemic_severity_germany

This table contains the composite indicator for daily pandemic severity for Germany on the national scale as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the Robert Koch Institute, the German government agency responsible for disease control and prevention.

pandemic_severity_counties

This table contains the composite indicator for daily pandemic severity for Germany on the level of the 400 individual counties, as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the Robert Koch Institute, the German government agency responsible for disease control and prevention. The counties can be identified by name (kreis) or by county identification number (ags5)

Part Time Employment in Germany decreased to 12036 Thousand in the first quarter of 2025 from 12053.60 Thousand in the fourth quarter of 2024. This dataset provides - Germany Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the year 2022.

The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire grassland area, i.e. permanent grassland, potentially permanent grassland (e.g. fodder crops) and other extensive areas. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019).

The dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose.

We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed.

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

References

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795.

_Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Germany township. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Germany township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Germany township, the median household income stands at $120,735 for householders within the 45 to 64 years age group, followed by $109,766 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $66,380.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Germany township median household income by age. You can refer the same here

This dataset is a curated statistical dataset from the Muenster City Council in RDF (Resource Description Framework) format. The City Council of Muenster has provided statistical datasets about Muenster covering the period 2010-2014 in PDF (Portable Document Format). Since PDF is not machine readable, students from the University of Muenster have tried to convert this statistical data into RDF, making therefore the data consumable by machines. The dataset covers five different topics:

• Unemployment in Muenster
• Population in Muenster
• Migration in Muenster
• Households of Muenster
• Employees subject to social insurance in Muenster

As proofs of the usefulness of the RDF data, the students built some nice visualizations. The visualizations can be accessed from:

• https://git.io/vD547 (unemployment)
• https://git.io/vD545 (population)
• https://git.io/vD54d (migration)
• https://git.io/vD54b (households)
• https://git.io/vD5Bv (employees subject to social insurance)

The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023).

The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Government Spending in Germany decreased to 207.09 EUR Billion in the first quarter of 2025 from 207.80 EUR Billion in the fourth quarter of 2024. This dataset provides - Germany Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Price Index CPI in Germany increased to 121.80 points in May from 121.70 points in April of 2025. This dataset provides the latest reported value for - Germany Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset tabulates the Germany township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Germany township. The dataset can be utilized to understand the population distribution of Germany township by age. For example, using this dataset, we can identify the largest age group in Germany township.

Key observations

The largest age group in Germany Township, Pennsylvania was for the group of age 50 to 54 years years with a population of 313 (10.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Germany Township, Pennsylvania was the 80 to 84 years years with a population of 60 (2.10%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Germany township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Germany township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Germany township Population by Age. You can refer the same here

Housing Index in Germany increased to 218.58 points in May from 217.43 points in April of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.