The global household computer penetration in was forecast to continuously increase between 2024 and 2029 by in total 2.4 percentage points. After the eleventh consecutive increasing year, the computer penetration rate is estimated to reach 52.78 percent and therefore a new peak in 2029. Depicted is the estimated share of households owning at least one computer.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 household computer penetration in countries like Australia & Oceania and Caribbean.

The global number of households with a computer in was forecast to continuously increase between 2024 and 2029 by in total 88.6 million households (+8.6 percent). After the fifteenth consecutive increasing year, the computer households is estimated to reach 1.1 billion households and therefore a new peak in 2029. Notably, the number of households with a computer of was continuously increasing over the past years.Computer households are defined as households possessing at least one computer.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 households with a computer in countries like Caribbean and Africa.

Notes on the basis for this dataset: This dataset is based on a Eurostat dataset (ISOC_CI_CFP_CU):

Online data code:ISOC_CI_CFP_CU Source of data:Eurostat Last data update:10/05/2023 11:00 Last structure update:08/02/2021 23:00 Data navigation tree location: Science, technology, digital society > Digital economy and society > ICT usage in households and by individuals > Connection to the internet and computer use Cross cutting topics > Skills-related statistics > Skills supply - self-reported measures > Digital skills - ICT usage in households and by individuals > Internet and computer use

Header and data descritions of the filtered dataset: This filtered dataset contains the following headers and the corresponding data:

date [year in format yyyy form 2007 untill (and including) 2017 in reverse order; last line in the filtered dataset contains increase in percent-points] ATHN [Neutron Monitor in Athens, Greece, Europe; data: neutron detections per second averaged over a 1 year period] AT [ Austria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BE [ Belgium , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BG [ Bulgaria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CY [ Cyprus , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CZ [ Czechia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DE [ Germany , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DK[ Denmark , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EE [ Estonia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EL [ Greece , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] ES [ Spain , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EU28 [all 28 member countries of the EU between 2007 and 2017, Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FI [ Finland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FR [ France , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HR [ Croatia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HU [ Hungary , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IE [ Ireland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IT [ Italy , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LT [ Lithuania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LU [ Luxembourg , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LV [ Latvia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] MT [ Malta , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] NL [ Netherlands , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PL [ Poland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PT [ Portugal , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] RO [ Romania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SE [ Sweden , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SI [ Slovenia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SK [ Slovakia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] UK [ United Kingdom , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points]

Obtaining the filtered dataset:

This web map visualizes the prevalence of households in a given geography that do not own a computer, smartphone, or tablet. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. 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: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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., -555555...) have been set to null. 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. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

Analysis of ‘Percentage of young people (16 to 24 years old) who in the last 12 months have used any of the computer skills in information gathered for Autonomous Communities and Cities and type of computer skills (API identifier: 46294)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-175-46294 on 07 January 2022.

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

Table of INEBase Percentage of young people (16 to 24 years old) who in the last 12 months have used any of the computer skills in information gathered for Autonomous Communities and Cities and type of computer skills. Annual. Survey on Equipment and Use of Information and Communication Technologies in Households

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

Proportion of individuals who used any type of computer from any location in the last three months. A computer refers to a desktop computer, a laptop (portable) computer or a tablet (or similar handheld computer).

The figure shows the frequency of data backups among adult computer owners in the United States in 2008 and 2018. According to the source, ** percent of people backup the data on their computer on a yearly basis in 2018.

Data format: .csv Image size:

The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "Has one or more types of computing devices" refers to those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer. The category "No computer" consists of those who said "No" to all of these types of computers..Desktop or laptop refers to those who selected that category regardless of whether or not they indicated they also had another type of computer. However, "Desktop or laptop with no other type of computing device" refers to those who said "Yes" to owning or using a desktop or laptop and "No" to smartphone, tablet or other wireless computer, and other computer. Similarly, the same holds true for "Smartphone" compared to "Smartphone with no other type of computing device", "Tablet or other portable wireless computer" compared to "Tablet or other portable wireless computer with no other type of computing device", and "Other computer" compared to "Other computer with no other type of computing device.".The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This data measures the digital inequities in the Town of Dumfries by calculating the population of users with or without a computer as well the percentages of those with or without broadband internet subscription. This data comes from the most recent U.S. Census provided by the United States Census Bureau. Data will be updated accordingly with the schedule of the U.S Census. https://data.census.gov/cedsci/profile?g=1600000US5123760

The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries.

Analysis of ‘Broadband Adoption and Computer Use by year, state, demographic characteristics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/720f8c4b-7a1c-415c-9297-55904ba24840 on 26 January 2022.

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

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

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

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

Data format: .csv Dataset size:

In 2023, one in four Poles used automatic cloud synchronization to back up data stored on a computer or phone. On the other hand, nearly one in three do not back up their data.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Data about computer and Internet use were collected by asking respondents to select "Yes" or "No" to each type of computer and each type of Internet subscription. Therefore, respondents were able to select more than one type of computer and more than one type of Internet subscription..The category "Has one or more types of computing devices" refers to those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer. The category "No computer" consists of those who said "No" to all of these types of computers.."Desktop or laptop" refers to those who selected that category regardless of whether or not they indicated they also had another type of computer. However, "Desktop or laptop with no other type of computing device" refers to those who said "Yes" to owning or using a desktop or laptop and "No" to smartphone, tablet or other wireless computer, and other computer. Similarly, the same holds true for "Smartphone" compared to "Smartphone with no other type of computing device", "Tablet or other portable wireless computer" compared to "Tablet or other portable wireless computer with no other type of computing device", and "Other computer" compared to "Other computer with no other type of computing device.".Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient n...

The number of households with a computer in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million households (+**** percent). After the twelfth consecutive increasing year, the computer households is estimated to reach ***** million households and therefore a new peak in 2029. Computer households are defined as households possessing at least one computer.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 *** 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).

Market Overview: The global PC website builder market is projected to reach a value of USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period. This growth is primarily driven by the increasing demand for user-friendly and cost-effective website creation solutions for businesses and individuals. The proliferation of e-commerce, digital marketing, and the growing number of online users have further contributed to the market expansion. Market Drivers and Trends: Key drivers of the market include the increasing accessibility of web development tools, advancements in artificial intelligence (AI) and machine learning (ML), and the growing adoption of mobile-responsive website design. Additionally, the shift towards remote work and hybrid work models has further increased the demand for website builders that offer flexibility and ease of use. Notable trends include the integration of AI-powered features, the emergence of cloud-based solutions, and the growing popularity of code-free website development platforms.

We inspect 965 cybersecurity research papers published between 2012 and 2016 in order to understand better how datasets are used, produced and shared. We construct a taxonomy of the types of data created and shared, informed and validated by the examined papers. We then analyze the gathered data on datasets. Three quarters of existing datasets used as input to research are publicly available, but less than 20% of datasets created by researchers are publicly shared. Using a series of linear regressions, we demonstrate that those researchers who do make public the datasets they create are rewarded with more citations to the associated papers. Hence, we conclude that an under-appreciated incentive exists for researchers to share their created datasets with the broader research community.