ICA176 - Percentage of households with internet access. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of households with internet access...

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

ICA175 - Percentage of households with internet access. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of households with internet access...

ICA243 - Percentage of Internet users who purchased Travel/Culture related services online in the previous 3 months. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of Internet users who purchased Travel/Culture related services online in the previous 3 months...

Percentage of individuals using the internet for participating in social networks (creating a user profile, posting messages or other contributions) for private purposes, within the last 3 months prior to the survey. Expressed as a percentage of all individuals aged between 16 and 74 years old surveyed. Data based on the annual EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals.

The percentage of households without an internet subscription at home.Source: American Community Survey Years Available: 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

Kindly refer to my paper for more information. Please cite my work if you use my dataset in any work : K. R. Purba, D. Asirvatham and R. K. Murugesan, "Classification of instagram fake users using supervised machine learning algorithms," International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 3, pp. 2763-2772, 2020.

The dataset was collected using web scraping from third-party Instagram websites, to capture their metadata and up to 12 latest media posts from each user. The collection process was executed from September 1st, 2019, until September 20th, 2019. The dataset contains authentic users and fake users, which were filtered using human annotators. The authentic users were taken from followers of 24 private university pages (8 Indonesian, 8 Malaysian, 8 Australian) on Instagram. To reduce the number of users, they are picked using proportional random sampling based on their source university. All private users were removed, which is a total of 31,335 out of 63,795 users (49.11%). The final number of public users used in this research was 32,460 users.

Var name | Feature name | Description pos | Num posts | Number of total posts that the user has ever posted. flg | Num following | Number of following flr | Num followers | Number of followers bl | Biography length | Length (number of characters) of the user's biography pic | Picture availability | Value 0 if the user has no profile picture, or 1 if has lin | Link availability | Value 0 if the user has no external URL, or 1 if has cl | Average caption length | The average number of character of captions in media cz | Caption zero | Percentage (0.0 to 1.0) of captions that has almost zero (<=3) length ni | Non image percentage | Percentage (0.0 to 1.0) of non-image media. There are three types of media on an Instagram post, i.e. image, video, carousel erl | Engagement rate (Like) | Engagement rate (ER) is commonly defined as (num likes) divide by (num media) divide by (num followers) erc | Engagement rate (Comm.) | Similar to ER like, but it is for comments lt | Location tag percentage | Percentage (0.0 to 1.0) of posts tagged with location hc | Average hashtag count | Average number of hashtags used in a post pr | Promotional keywords | Average use of promotional keywords in hashtag, i.e. {regrann, contest, repost, giveaway, mention, share, give away, quiz} fo | Followers keywords | Average use of followers hunter keywords in hashtag, i.e. {follow, like, folback, follback, f4f} cs | Cosine similarity | Average cosine similarity of between all pair of two posts a user has pi | Post interval | Average interval between posts (in hours)

Output : 2-class User classes : r (real/authentic user), f (fake user / bought followers) 4-class User classes : r (authentic/real user), a (active fake user), i (inactive fake user), s (spammer fake user) Note that the 3 fake user classes (a, i, s) were judged by human annotators.

The Global Biodiversity Information Facility (GBIF) was established by governments in 2001 to encourage free and open access to biodiversity data, via the Internet. Through a global network of countries and organizations, GBIF promotes and facilitates the mobilization, access, discovery and use of information about the occurrence of organisms over time and across the planet. GBIF provides three core services and products: # An information infrastructure an Internet-based index of a globally distributed network of interoperable databases that contain primary biodiversity data information on museum specimens, field observations of plants and animals in nature, and results from experiments so that data holders across the world can access and share them # Community-developed tools, standards and protocols the tools data providers need to format and share their data # Capacity-building the training, access to international experts and mentoring programs that national and regional institutions need to become part of a decentralized network of biodiversity information facilities. GBIF and its many partners work to mobilize the data, and to improve search mechanisms, data and metadata standards, web services, and the other components of an Internet-based information infrastructure for biodiversity. GBIF makes available data that are shared by hundreds of data publishers from around the world. These data are shared according to the GBIF Data Use Agreement, which includes the provision that users of any data accessed through or retrieved via the GBIF Portal will always give credit to the original data publishers. * Explore Species: Find data for a species or other group of organisms. Information on species and other groups of plants, animals, fungi and micro-organisms, including species occurrence records, as well as classifications and scientific and common names. * Explore Countries: Find data on the species recorded in a particular country, territory or island. Information on the species recorded in each country, including records shared by publishers from throughout the GBIF network. * Explore Datasets: Find data from a data publisher, dataset or data network. Information on the data publishers, datasets and data networks that share data through GBIF, including summary information on 10028 datasets from 419 data publishers.

Computer and internet use in schools. Topics: number of computers used for educational purposes; number of computers used for educational purposes in the following levels of education: primary, secondary, professional or technical; percentage of computers used for education equipped with internet connection; percentage of computers used for education that are less than three years old; percentage of computers used for education that have been donated by private sources; location of computers used for education: computer lab, classrooms, school’s library, no fixed computers but laptops, elsewhere; internet access via: standard telephone line, ISDN line, ADSL line, special modem using the TV cable, other; availability of the following services at school: e-mail address, website, intranet, access to a helpdesk; influence of the school on the choice of: hardware, software, type of internet connection, type of teacher training in this field. Demography: number of pupils in each of the following levels of education: primary, secondary, professional or technical; number of teachers in the aforementioned levels; type of community. Additionally coded was: country; weighting factor. Nutzung von Computern und Internet an Schulen. Themen: Anzahl der Computer zu Unterrichtszwecken sowie in jeweiliger Schulstufe; Prozent dieser Computer mit Internetzugang; Anzahl der im Unterricht genutzten Computer, die nicht älter als drei Jahre sind; Anzahl der Computer aus privaten Spenden; Standort der Computer; Art des Internetanschlusses in der Schule; Existenz einer E-Mail-Adresse, einer Homepage, eines Intranets, einer Service-Telefonnummer für technische Hilfe zu Computern und Internet an der Schule; Existenz des selbstständigen Entscheidungsrechts über: Wahl der Computer-Hardware sowie Software, Art der Internetverbindung, Art der Lehrerbildung in diesem Bereich. Demographie: Anzahl der Schüler in den einzelnen Schulstufen; Anzahl der Lehrer in jeweiliger Schulstufe; Urbanisierungsgrad. Zusätzlich verkodet wurde: Land; Gewichtungsfaktor.

Nutzung von Computern und Internet an Schulen. Themen: Anzahl der Computer zu Unterrichtszwecken sowie in jeweiliger Schulstufe; Prozent dieser Computer mit Internetzugang; Anzahl der im Unterricht genutzten Computer, die nicht älter als drei Jahre sind; Anzahl der Computer aus privaten Spenden; Standort der Computer; Art des Internetanschlusses in der Schule; Existenz einer E-Mail-Adresse, einer Homepage, eines Intranets, einer Service-Telefonnummer für technische Hilfe zu Computern und Internet an der Schule; Existenz des selbstständigen Entscheidungsrechts über: Wahl der Computer-Hardware sowie Software, Art der Internetverbindung, Art der Lehrerbildung in diesem Bereich. Demographie: Anzahl der Schüler in den einzelnen Schulstufen; Anzahl der Lehrer in jeweiliger Schulstufe; Urbanisierungsgrad. Zusätzlich verkodet wurde: Land; Gewichtungsfaktor. Computer and internet use in schools. Topics: number of computers used for educational purposes; number of computers used for educational purposes in the following levels of education: primary, secondary, professional or technical; percentage of computers used for education equipped with internet connection; percentage of computers used for education that are less than three years old; percentage of computers used for education that have been donated by private sources; location of computers used for education: computer lab, classrooms, school’s library, no fixed computers but laptops, elsewhere; internet access via: standard telephone line, ISDN line, ADSL line, special modem using the TV cable, other; availability of the following services at school: e-mail address, website, intranet, access to a helpdesk; influence of the school on the choice of: hardware, software, type of internet connection, type of teacher training in this field. Demography: number of pupils in each of the following levels of education: primary, secondary, professional or technical; number of teachers in the aforementioned levels; type of community. Additionally coded was: country; weighting factor.

When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place" as an answer. 50 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Analysis of ‘ICA03 - External Connection to the Internet as a Percentage of All Enterprises’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/35bcaead-1c12-449e-a946-8d88b79721d5 on 28 January 2022.

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

External Connection to the Internet as a Percentage of All Enterprises

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

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Analysis of ‘Percentage of Households with 0-5 Internet Service Providers (ISPs) in New York City by NTA - Dec 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/359c0810-4386-40be-9d89-616f5cab8544 on 26 January 2022.

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

Information on the number of Internet Service Providers offering service in each of New York City's Neighborhood Tabulation Areas, according to data made publicly available by the Federal Communications Commission.

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

Context

Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers.

Content

Updated on a quarterly basis, this BigQuery dataset includes an archive of Stack Overflow content, including posts, votes, tags, and badges. This dataset is updated to mirror the Stack Overflow content on the Internet Archive, and is also available through the Stack Exchange Data Explorer.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: https://archive.org/download/stackexchange

https://bigquery.cloud.google.com/dataset/bigquery-public-data:stackoverflow

https://cloud.google.com/bigquery/public-data/stackoverflow

Banner Photo by Caspar Rubin from Unplash.

Inspiration

What is the percentage of questions that have been answered over the years?

What is the reputation and badge count of users across different tenures on StackOverflow?

What are 10 of the “easier” gold badges to earn?

Which day of the week has most questions answered within an hour?

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

About this dataset

Context

The dataset tabulates the State Line population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of State Line across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of State Line was 458, a 0.22% increase year-by-year from 2021. Previously, in 2021, State Line population was 457, an increase of 1.11% compared to a population of 452 in 2020. Over the last 20 plus years, between 2000 and 2022, population of State Line decreased by 86. In this period, the peak population was 556 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the State Line is shown in this column.
• Year on Year Change: This column displays the change in State Line population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 State Line Population by Year. You can refer the same here

Percentage of Internet users by selected Internet service and technology, such as; home Internet access, use of smart home devices, use of smartphones, use of social networking accounts, use or purchase of streaming services, use of government services online and online shopping.

The original MILANO dataset (https://ieee-dataport.org/documents/milan-dataset) examines the relationship between meteorological context and cellular traffic loads, telecommunication and weather data from the city of Milan is presented. The dataset consists of aggregated telecommunication and weather data from the city of Milan during the period of 1st of November 2013 to 1st of January 2014. The telecommunication data consists of aggregated information of received SMS, sent SMS, incoming call, outgoing call, and internet activity, and is measured through Call Detail Records (CDRs), a measure of volume of cellular traffic. The weather data consists of information on humidity, measured in percentage, temperature, measured in degrees Celsius, and wind speed, measured in meters per second. The data is aggregated by hours and is listed over an area of 300 meters squared within the city. The city is divided into grids of 1x1m, and a binary adjacency matrix is presented to represent the connectivity between the grids in the city.
Within the paper “Resource Demand Prediction for Network Slices in 5G Using ML Enhanced With Network Models”, a post-processed version of the MILANO dataset was created, which includes the following attitional fields, to facilitate the ability to forecast the slice resource demand. The following fields are included:

1. bsId: The 5G BaseStation ID. We can have multiple associations between the same BaseStation ID, like several occurrences of the same ID that plays the role of Input Cell and Output transmission Cell.
2. Episode. The game-theoretic episode index of the decentralized AI consensus admission control algorithm(s) utilized.
3. Step. Simply a numerical index starting from 0, and augmenting.
4. loadSMS. Total in/out SMS traffic load.
5. RawActSMS. Original raw network packets (SMS load).
6. loadInt. Total in/out Internet traffic load.
7. rawActInt. Original raw network packets (Internet load).
8. loadCalls. Total in/out Voice traffic load.
9. rawActCalls. Original raw network packets (Voice load).
10. defSMS. Deferred Total in/out SMS traffic load.
11. rawActDefSMS. Deferred Original raw network packets (SMS load).
12. defInt. Deferred Total in/out Internet traffic load.
13. rawActDefInt. Deferred Original raw network packets (Internet load).
14. defCalls. Deferred Total in/out Voice traffic load.
15. rawActDefCalls. Deferred Original raw network packets (Voice load).
16. schedSMS. Admitted SMS traffic load.
17. schedInt. Admitted Internet traffic load.
18. schedCalls. Admitted Voice Traffic load.
19. schedDelaySMS. Queued for admission SMS traffic load.
20. schedDelayInt. Queued for admission Internet traffic load.
21. schedDelayCalls. Queued for admission Voice Traffic load.
22. dropSMS. Raw Packets Dropped SMS traffic load.
23. dropInt. Raw Packets Dropped Internet traffic load.
24. dropCalls. Raw Packets Dropped Voice Traffic load.
25. totalSched. Total Admitted Traffic load.
26. bsCap. BaseStation network capacity.
27. totalDropped. Total Raw Packets Dropped Traffic load.
28. rejectRate. Total Traffic load network packets reject rate.
29. rejectRateCalls. Voice Traffic network packets reject rate.
30. rejectRateInt. Internet traffic network packets reject rate.
31. rejectRateSMS. SMS traffic network packets reject rate.
32. delayRate. Total Delayed (not queued yet for admission) Traffic load.
33. delayRateCalls. Total Delayed (not queued yet for admission) Voice Traffic load.
34. delayRateInt. Total Delayed (not queued yet for admission) Internet traffic load.
35. delayRateSMS. Total Delayed (not queued yet for admission) SMS traffic load.
36. Reward. Total game reward value.
37. episodeReward. Total reward value per episode.
38. avgEpisodeReward. Average reward value per episode.