Introduction

Blogging Statistics: Blogging remains a pivotal element in digital content strategies, with over 600 million blogs among 1.9 billion websites globally. WordPress alone powers more than 43% of all websites, hosting over 60 million blogs and facilitating approximately 70 million new posts each month. In the United States, the blogging community has expanded to over 32.7 million active bloggers as of 2022. Globally, bloggers publish around 3 billion posts annually, equating to over 8.2 million posts daily.

The influence of blogs is substantial, with 77% of internet users regularly reading blog content. Incorporating relevant images can enhance blog views by 94%, and posts with seven or more images are 2.3 times more likely to yield strong results. Furthermore, 70% of consumers prefer learning about companies through articles rather than advertisements, highlighting the trust and engagement blogs foster.

For businesses, blogging offers significant advantages: companies with active blogs experience 55% more website visitors and generate 67% more monthly leads compared to those without. These statistics underscore blogging's role as a cost-effective and impactful tool for enhancing brand visibility and driving audience engagement.

With internet access, anyone can start a blog and reach a global audience through social media. In this article, we'll explore blogging statistics in more detail.

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.

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

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

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 contents of the dataset are related to the demographics of companies in the province of Trento.

The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available

The dataset, including the resources in PDF format, is also available on the Open Data Catalogue of the Employment Agency at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Economics-and-finance/Economics-structure/Demography-of-businesses/Year-2016

The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST.

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 compiled by the Office for Labour Market and Policy Studies on CCIAA – Movimprese data.

Analysis of ‘🎦 Academy Awards Demographics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/academy-awards-demographicse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

A data set concerning the race, religion, age, and other demographic details of all Oscar winners since 1928 in the following categories: * Best * Actor

• Best Actress
• Best Supporting Actor
• Best Supporting Actress
• Best Director For further information on this data set, please read our resulting blog post For further information on this data set, please read our resulting blog post.

Source: https://www.crowdflower.com/data-for-everyone/

This dataset was created by CrowdFlower and contains around 400 samples along with Birthplace:confidence, Sexual Orientation Gold, technical information and other features such as: - Date Of Birth - Religion - and more.

How to use this dataset

• Analyze Year Of Award Gold in relation to Trusted Judgments
• Study the influence of Sexual Orientation on Date Of Birth:confidence
• More datasets

Acknowledgements

If you use this dataset in your research, please credit CrowdFlower

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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

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.

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 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.

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 presents the Tumblr penetration in the United States from 2014 to 2020. In 2015, 6.5 percent of the U.S. population accessed the social network. In 2018, Tumblr's reach among the U.S. population is projected to reach 8.2 percent.

Eye Creams For Dark Circles Market Outlook

The global market size for eye creams targeting dark circles was valued at USD 2.1 billion in 2023 and is forecasted to reach approximately USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.6% during the forecast period. This promising growth is driven by increasing consumer awareness regarding skincare, the rising incidence of dark circles due to lifestyle factors, and the growing disposable income in emerging economies.

A primary growth driver for the eye creams for dark circles market is the heightened awareness about skincare and personal grooming. As individuals become more conscious of their appearance, there is a growing willingness to invest in specialized skincare products, including eye creams that target dark circles. This trend is further bolstered by the proliferation of beauty influencers and dermatologists who emphasize the importance of using targeted treatments for specific skin concerns. The increasing visibility of eye creams in social media and beauty blogs also adds to consumer knowledge and encourages trial and adoption.

Another significant growth factor is the rising incidence of dark circles, which can be attributed to various lifestyle-related factors. The modern lifestyle, characterized by high levels of stress, inadequate sleep, and prolonged screen time, has led to an increase in the prevalence of dark circles among individuals of all age groups. These lifestyle changes have heightened the demand for effective eye creams that promise to reduce the appearance of dark circles and rejuvenate the under-eye area. Additionally, the aging population, which is more prone to skin concerns like dark circles and puffiness, is further fueling the market demand.

The increasing disposable income, particularly in emerging economies, is another crucial factor driving the market growth. As consumers in countries like China, India, and Brazil experience an increase in their disposable income, they are more likely to spend on premium skincare products. This shift is also supported by the expanding middle class and the growing influence of Western beauty standards, which advocate for flawless and youthful skin. Consequently, the demand for specialized eye creams that address dark circles is on the rise in these regions.

Eye Serum products have emerged as a popular alternative to traditional eye creams, offering a lightweight and highly concentrated formula that penetrates deeply into the skin. These serums are often enriched with active ingredients like peptides, hyaluronic acid, and vitamins that target dark circles, puffiness, and fine lines. Unlike creams, Eye Serums are designed to deliver potent nutrients without leaving a greasy residue, making them ideal for individuals with oily or combination skin types. The growing consumer preference for fast-absorbing and effective skincare solutions has led to an increase in the demand for Eye Serums, particularly among younger demographics who seek preventive care for their delicate under-eye area.

Regionally, North America and Europe currently dominate the eye creams for dark circles market, owing to the high consumer awareness and established skincare industry. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The rapid urbanization, increasing disposable income, and growing beauty and personal care market in countries like China, India, and South Korea are major contributors to this growth. Latin America and the Middle East & Africa are also emerging markets, driven by improving economic conditions and increasing focus on personal grooming and skincare.

Product Type Analysis

The eye creams for dark circles market can be segmented based on product types into anti-aging eye creams, hydrating eye creams, brightening eye creams, and others. Anti-aging eye creams are designed to target signs of aging such as fine lines and wrinkles in addition to dark circles. These creams often contain ingredients like retinol, peptides, and antioxidants that aim to rejuvenate the under-eye skin and provide a youthful appearance. The rising aging population and increasing awareness about the benefits of anti-aging products are driving the demand for this segment.

Hydrating eye creams focus on providing moisture to the delicate skin around the eyes, which can often become dry and exacerbate the appearance of dark circles. Ingredients like hya

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.

Le contenu de l'ensemble de données se rapporte à deux domaines de performance du marché du travail dans la province de Trente: 1) les données sur la population active qui sont regroupées par statut professionnel, sexe et groupe d'âge; 2) données sur les taux d'activité, d'emploi et de chômage.
Les données, qui proviennent de diverses sources, ont été élaborées par le Bureau d'études du marché du travail et des politiques pour la préparation du rapport annuel sur l'emploi dans la province de Trente, disponible sous forme de contenu ouvert à l'URL: https://www.agenzialavoro.tn.it/Open-Data/Autres contenus disponibles

L’ensemble de données, y compris les ressources au format PDF, est également disponible sur le portail des données ouvertes de l’Agence pour l’emploi à l’adresse URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-et-société/Marché du travail/Taux des forces de travail/Année 2016 Les données publiées au format CSV sont les suivantes: Machine Readable, identifié dans le nom du fichier avec le suffixe _MR et validé avec la bibliothèque Good Tables. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html

ATTRIBUTION : données compilées par l’Office for Labour Market and Policy Studies sur les données annuelles moyennes de l’enquête continue ISTAT-ISPAT sur les forces de travail.Le contenu de l'ensemble de données se rapporte à deux domaines de performance du marché du travail dans la province de Trente: 1) les données sur la population active qui sont regroupées par statut professionnel, sexe et groupe d'âge;2) données sur les taux d'activité, d'emploi et de chômage.

Les données, qui proviennent de diverses sources, ont été élaborées par le Bureau d'études du marché du travail et des politiques pour la préparation du rapport annuel sur l'emploi dans la province de Trente, disponible sous forme de contenu ouvert à l'URL: https://www.agenzialavoro.tn.it/Open-Data/Autres contenus disponibles

L’ensemble de données, y compris les ressources au format PDF, est également disponible sur le portail des données ouvertes de l’Agence pour l’emploi à l’adresse URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Population-et-société/Marché du travail/Taux des forces de travail/Année 2016

Les données publiées au format CSV sont les suivantes: Machine Readable, identifié dans le nom du fichier avec le suffixe _MR et validé avec la bibliothèque Good Tables. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html

ATTRIBUTION : données compilées par l’Office for Labour Market and Policy Studies sur les données annuelles moyennes de l’enquête continue ISTAT-ISPAT sur les forces de travail.

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.

This map runs this app - http://nmcdc.maps.arcgis.com/home/item.html?id=958544e5eebd4501be8b70f71e2ef925Instructions for Using Premium content on a Public Map:https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/local-government/including-online-demographic-maps-in-your-public-maps-and-apps/