About this dataset

Context

The dataset tabulates the population of David City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for David City. The dataset can be utilized to understand the population distribution of David City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in David City. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for David City.

Key observations

Largest age group (population): Male # 40-44 years (158) | Female # 20-24 years (154). 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

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.

Variables / Data Columns

• Age Group: This column displays the age group for the David City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the David City is shown in the following column.
• Population (Female): The female population in the David City is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in David City for each 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 David City Population by Gender. You can refer the same here

This is the dataset used for David Davó's Master's Thesis, available on GitHub

This dataset is based on daos-census and dao-analyzer, but including the proposals-text table.

The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.

This 2014 Census data set represents the data collected in three different ways:

qualitative analysis of the open access repository websites

automatic validation of the metadata via OAI-PMH using the DINI-Validator [2]

online survey of repository operators

As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.

[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. D-Lib Magazine, 19. http://dx.doi.org/10.1045/november2013-vierkant

[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml

[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. figshare. http://dx.doi.org/10.6084/m9.figshare.677099

About this dataset

Context

The dataset tabulates the David City 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 David City. The dataset can be utilized to understand the population distribution of David City by age. For example, using this dataset, we can identify the largest age group in David City.

Key observations

The largest age group in David City, NE was for the group of age 40 to 44 years years with a population of 250 (8.30%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in David City, NE was the 80 to 84 years years with a population of 45 (1.49%). 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 David City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of David City 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 David City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the David City median household income by race. The dataset can be utilized to understand the racial distribution of David City income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• David City, NE median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in David City, NE (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of David City median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in David City, NE, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,482, while the mean income for the highest quintile (20% of households with the highest income) is 250,035. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 564,228, which is 225.66% higher compared to the highest quintile, and 3896.06% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 David City median household income. You can refer the same here

An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).

About this dataset

Context

The dataset tabulates the data for the David City, NE population pyramid, which represents the David City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for David City, NE, is 34.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for David City, NE, is 30.5.
• Total dependency ratio for David City, NE is 65.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for David City, NE is 3.3.

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 for the David City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the David City for the selected age group is shown in the following column.
• Population (Female): The female population in the David City for the selected age group is shown in the following column.
• Total Population: The total population of the David City for the selected age group is shown in the following column.

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 David City Population by Age. You can refer the same here

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of American Samoa, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on American Samoa's data products, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, American Samoa.

Census/projection-disaggregated gridded population datasets, adjusted to match the corresponding UNPD 2020 estimates, for 51 countries across sub-Saharan Africa using building footprints. Source of building footprints "Ecopia Vector Maps Powered by Maxar Satellite Imagery" © 2020.

Boundary Shapes for the US Census 'Places' 2021

NOTE: For information on confidentiality protection, nonsampling.error, and definitions see .http://www.census.gov/prod/cen2000/island/GUAMprofile.pdf..U.S. Census BureauCensus 2000

This is the final dataset and R script used for the analysis for the paper titled All Ridership Is Local: Accessibility, Competition, and Stop-Level Determinants of Daily Bus Boardings in Portland, Oregon. The .csv and .RDS files contain the same final dataset with all the variables used in the final models.

Plan Objectives: This submission, “A3S Possibility,” gives effect to public commenters desire to have more than one Supervisor representing the north area of the county, and for unincorporated areas near Palmdale to be represented by a different Supervisor than Palmdale’s. It keeps communities of interest together throughout the county, and has three Supervisors representing areas on the Pacific Ocean’s coast.... Because the software currently limits users to Redistricting Units for drawing districts, this submission requires the following adjustments to be considered or adopted as intended:Assign Census Tract 6037930200 to District 3; andAssign Redistricting Units 3023, 3032, 3069, 3070, 3071, and 3091 to District 5 (see A3Spossheart-colored.png).

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png. An additional set of 25 figures showing regional trends in population and income metrics are also included.

Plan Description: This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation. The part of North County that District 3 here includes is largely Latinx and working class, in kinship with much of the population in the rest of that District.Plan Objectives:This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation, particularly from unincorporated areas contained within or east-south east of Palmdale. [I have a friend in the electoral reform community who lives in a different California county, in the foothills of the Sierra Nevada. A sizeable minority of residents in the area shares her political perspective. But because of where they live, and because districts are drawn to be compact, those residents do not get representation to their liking in the State Legislature or U.S. House of Representatives. My friend often complains about that and longs to have at least one district that stretches from elsewhere to include her residence or a nearby sympatico area, and that might elect a representative to her liking. Just like with North Los Angeles County, a little diversity in representation of the Sierra foothills could be a good thing.] Now that LA County CRC software users can no longer assign official U.S. Census areas (blocks, block groups, or tracts) to districts, and are offered “RDUs” (made-up ReDistricting Units) instead, this is the best I could do. RDU3096 gets in the way of better connections from North County to the southern part of the county. And we would need to use census blocks to “break through” to the unincorporated “hole” in the incorporated city of Palmdale, from which the commission heard at least one request for separate representation (rescue from the city?), without dividing the incorporated city’s population. (RDU3006 is too big to allow that.) The software/database bait-and-switch to RDUs only from the demo version also makes it necessary to take a small part of the populated area of the City of Santa Clarita to draw District 3 here.The part of North County that District 3 here includes is, as the commission heard, largely Latinx and working class, in kinship with much of the population in the rest of District 3 here, as a community of interest. At this point, it would not be practicable for this district to be geographically compact and keep that community connected.

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).

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main(Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted.

  These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities.
  The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package.

  The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the
  USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or
  location because they do not fit well into the regional framework.

  Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values.
  Please send a description of any unusual values to the dataset contact.

About this dataset

Context

The dataset tabulates the David City household income by gender. The dataset can be utilized to understand the gender-based income distribution of David City income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• David City, NE annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• David City, NE annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of David City income distribution by gender. You can refer the same here