As of November 2023, nearly 17 percent of female internet users in the United States and around 16 percent of male users went online to publish blog posts or upload self-made video content. Overall, approximately 17 percent of the U.S. online population reported publishing original content on the internet.

At the April 2023 meeting of the Population Statistics User Group, the GLA Demography team presented an overview of currently available sources of population estimates for the previous decade, namely:

• The original ONS mid-year population estimates (including rolled-forward estimates for 2021)
• Experimental outputs from the ONS's Dynamic Population Model
• The modelled population backseries produced by the GLA to act as inputs to our 2021-based interim population projections

The slides from the presentation are published here together with packages of comparison plots for all local authority districts and regions in England to allow users to easily view some of the key differences between the sources for their own areas.

The plots also include comparisons of the Dynamic Population Model's provisional 2022 estimates of births with the modelled estimates of recent births produced by the GLA.

The trend-based projections include a range of variants based on different assumptions about future levels of migration. The projections are produced for all local authorities in England & Wales.

The datasets include summary workbooks with population and summary components of change as well as zip archives with the full detailed outputs from the models, including components of change by single year of age and sex.

The most recent set of trend-based population projections currently available are the 2022-based projections (August 2024). Additional documentation, including updated information about methodologies and assumptions will be published in the coming days.

For more information about these projections, see the accompanying blog post.

The 2022-based projections comprise three variants based on different periods of past migration patterns and assumed levels of future fertility rates.

Trend-based projections don't explicitly account for future housing delivery. For most local planning purposes we generally recommend the use of housing-led projections

These projections are based on modelled back series of population estimates produced by the GLA and available here

* 14 July 2023 - following a minor update to the modelled population estimates series, we have made available an additional version of the projections based on these updated inputs. At this time we have no plans to update or replace the outputs and documentation published in January 2023. However, we recommend users looking to use the projections in analysis or as inputs to onward modelling consider using these updated outputs.

• Documentation page
• Back to projections homepage

This dataset contains data from the P.L. 94-171 2020 Census Redistricting Program. The 2020 Census Redistricting Data Program provides states the opportunity to delineate voting districts and to suggest census block boundaries for use in the 2020 Census redistricting data tabulations (Public Law 94-171 Redistricting Data File). In addition, the Redistricting Data Program will periodically collect state legislative and congressional district boundaries if they are changed by the states. The program is also responsible for the effective delivery of the 2020 Census P.L. 94-171 Redistricting Data statutorily required by one year from Census Day. The program ensures continued dialogue with the states in regard to 2020 Census planning, thereby allowing states ample time for their planning, response, and participation. The U.S. Census Bureau will deliver the Public Law 94-171 redistricting data to all states by Sept. 30, 2021. COVID-19-related delays and prioritizing the delivery of the apportionment results delayed the Census Bureau’s original plan to deliver the redistricting data to the states by April 1, 2021.

Data in this dataset contains information on population, diversity, race, ethnicity, housing, household, vacancy rate for 2020 for various geographies (county, MCD, Philadelphia Planning Districts (referred to as county planning areas [CPAs] internally, Census designated places, tracts, block groups, and blocks)

For more information on the 2020 Census, visit https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html

PLEASE NOTE: 2020 Decennial Census data has had noise injected into it because of the Census's new Disclosure Avoidance System (DAS). This can mean that population counts and characteristics, especially when they are particularly small, may not exactly correspond to the data as collected. As such, caution should be exercised when examining areas with small counts. Ron Jarmin, acting director of the Census Bureau posted a discussion of the redistricting data, which outlines what to expect with the new DAS. For more details on accuracy you can read it here: https://www.census.gov/newsroom/blogs/director/2021/07/redistricting-data.html

In May 2024, three percent of the Polish population had an online blog, a vlog (video blog), or a website. This was a decrease of two percent as compared to the year 2012.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. For more information, visit: https://ai.facebook.com/blog/mapping-the-world-to-help-aid-workers-with-weakly-semi-supervised-learning

About this dataset

Context

The dataset tabulates the data for the Atlantis, FL population pyramid, which represents the Atlantis 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 Atlantis, FL, is 18.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Atlantis, FL, is 94.9.
• Total dependency ratio for Atlantis, FL is 113.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Atlantis, FL is 1.1.

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 Atlantis population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Atlantis for the selected age group is shown in the following column.
• Population (Female): The female population in the Atlantis for the selected age group is shown in the following column.
• Total Population: The total population of the Atlantis 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 Atlantis Population by Age. You can refer the same here

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This statistic shows the results of a survey on the frequency of posting comments in blogs and discussion forums in Germany from 2013 to 2016. In 2016, there were about 1.51 million internet users among the German-speaking population aged 14 years and older, who posted comments in blogs of other users or wrote contributions in discussion forums frequently.

This statistic shows the number of internet users who used the internet for reading posts in forums and blogs in Germany from 2013 to 2015, by usage frequency. In 2013, there were roughly 3.95 million people among the German-speaking population, who used the internet frequently to read posts in internet forums and blogs.

The contents of the dataset relate to the population living in the province of Trento. The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Historical-Series/Demography Data are grouped by year and gender. Data are expressed in absolute values. The metadata ‘time coverage’ refers to the time interval taken into account by the Historical Series which is identified in the file name with the suffix _ST. Time coverage refers to 31 December of each year. The dataset is updated to 31 December each year with the addition of a new time series. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html ATTRIBUTION: data processed by the Office for the Study of Policies and the Labour Market on ISTAT data.

The Module on Reading (MOLEC) was conducted from 2015, the months of: February, May and August; from 2017, once a year in the month of February. MOLEC aims to generate statistical information on the reading behavior of the Mexican population aged 18 years and over. In order to provide useful data on the characteristics of the said reading population, and provide elements to encourage the reading habit.

The primary aim of the survey was to gain a 'snapshot' of what Australian libraries were doing with blogs and wikis. Methodology: An online survey. The population was Australian university, public and special libraries. The sampling frame was constructed from university library websites, State Library public library listings and the National Library of Australia's listing for special libraries. Branch libraries were treated as individual entries. Entries that were not relevant to the survey content, for example prison libraries, were removed from the sampling frame. To produce the correct sample sizes, every 10th library was sampled. The response rate was 21%. Once the data was weighted, the data was analysed using SPSS 13.0. Funding Body: Ray Choate Scholarship, Australian Library and Information Association (ALIA).

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

In 2025, most of the Polish population got their information about finance and the economy via blogs and websites on the internet. Only eleven percent have learned about it through books.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Real-time estimate—relative efficiency being higher than one suggests increased predictive power of ARGONet compared to the autoregressive model AR(52).

Discover the latest social media statistics and trends for 2025 and how they impact businesses.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.