[Source Data]The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: (1) percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; (2) percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); (3) percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); (4) median maximum advertised download speeds; and (5) median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; (4) percent of noninstitutionalized civilian population with a disability: and (5) a brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.

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

Digital Distress Metric:Four variables from the U.S. Census American Community Survey were used: The percent of homes with no internet access, Using only cellular data, as well as The percent of homes relying on mobile devices only, or Having no computing devices. Data was obtained for all U.S. census tracts and categorized into low, moderate, and high digital distress.The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: Percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; Percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); Percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); Median maximum advertised download speeds; and Median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: Percent population ages 65 and over; Percent population 25 and over with less than high school; Individual poverty rate; Percent of noninstitutionalized civilian population with a disability: and A brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.

5G availability in the rural United States increased to 24.8 percent in the second quarter of 2023, up from 20.7 percent during the same period in 2022. Meanwhile, urban availability increased to 35 percent. While these figures highlight a persistent regional divide in U.S. 5G availability, 5G technology has been touted as a means of closing the digital divide by bringing fast, low latency internet to areas without fixed broadband infrastructure.

Dataset associated with the paper "Digital Divide: Addressing Internet Skills. Educational implications in the validation of a scale.", dealing with the Italian validation of the Internet Skills Scale (ISS).

As of February 2025, there were 5.56 billion internet users worldwide, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2024. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of April 2024. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Asia was home to the largest number of online users worldwide – over 2.93 billion at the latest count. Europe ranked second, with around 750 million internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2023, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in the Arab States and Africa, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller gender gap. As of 2023, global internet usage was higher among individuals between 15 and 24 years across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

This statistic presents the evolution of the digital divide among children aged 10 to 15 years in Spain from 2007 to 2019 regarding the use of the Internet. The gap in 2019 decreased to -0.4 percentage points.

This data is used for a broadband mapping initiative conducted by the Washington State Broadband Office. This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. The data was processed and published to ArcGIS Living Atlas by Esri.AboutSpeedtest data is used today by commercial fixed and mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. Government regulators such as the United States Federal Communications Commission and the Malaysian Communications and Multimedia Commission use Speedtest data to hold telecommunications entities accountable and direct funds for rural and urban connectivity development. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access.DataHundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of "zoom level" (or "z"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.The data can be found at: https://github.com/teamookla/ookla-open-dataUpdate CadenceThe tile aggregates start in Q1 2019 and go through the most recent quarter. They will be updated shortly after the conclusion of the quarter.Esri ProcessingThis layer is a best available aggregation of the original Ookla dataset. This means that for each tile that data is available, the most recent data is used. So for instance, if data is available for a tile for Q2 2019 and for Q4 2020, the Q4 2020 data is awarded to the tile. The default visualization for the layer is the "broadband index". The broadband index is a bivariate index based on both the average download speed and the average upload speed. For Mobile, the score is indexed to a standard of 25 megabits per second (Mbps) download and 3 Mbps upload. A tile with average Speedtest results of 25/3 Mbps is awarded 100 points. Tiles with average speeds above 25/3 are shown in green, tiles with average speeds below this are shown in fuchsia. For Fixed, the score is indexed to a standard of 100 Mbps download and 3 Mbps upload. A tile with average Speedtest results of 100/20 Mbps is awarded 100 points. Tiles with average speeds above 100/20 are shown in green, tiles with average speeds below this are shown in fuchsia.Tile AttributesEach tile contains the following adjoining attributes:The year and the quarter that the tests were performed.The average download speed of all tests performed in the tile, represented in megabits per second.The average upload speed of all tests performed in the tile, represented in megabits per second.The average latency of all tests performed in the tile, represented in millisecondsThe number of tests taken in the tile.The number of unique devices contributing tests in the tile.The quadkey representing the tile.QuadkeysQuadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.LayersThere are two layers:Ookla_Mobile_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).Ookla_Fixed_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).The layers are set to draw at scales 1:3,000,000 and larger.Time Period and update Frequency Layers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.

As of 2024, the share of internet users in the CIS region (Commonwealth of Independent States) was the highest in the world, with 91 percent of the female population and 93 percent of the male population accessing the internet. As of the same year, there were 90 percent female and 92 percent male internet users in Europe, making it the second region worldwide by internet usage. Africa was the region where internet access was the lowest. Share of female and male internet users worldwide There are still disparities between the internet access rates of male and female online users in global regions. According to the latest data, 34 percent of Africa’s female population had online access, compared to 45 percent of men. Whereas in the Americas, the share of male and female internet users was the same, 83 percent. There was also a big difference in the share of female and male internet users in the Arab States. In the region, 65 percent of women had access to the internet, whereas the share of the male population using the internet was 75 percent. The gender gap was also seen in mobile internet usage in low-and middle-income countries (LMICs). Internet access and SDGs As of 2022, Africa’s online access rate was the lowest worldwide, with estimates showing that just over 30 percent of the total population was using the internet. By comparison, the global average online usage rate was 51 percent. This technological gap between Africa and the rest of the world highlights the need for continued investment in information and communication technologies on the continent, as such processes can speed up progress towards the 17 Sustainable Development Goals (SDGs) set by the United Nations. The Sustainable Development Goals, also known as the Global Goals, are a worldwide agenda to protect the planet, end poverty, and ensure global peace and prosperity. ICTs, especially mobile internet, contribute to the goals by enabling countries to participate in digital economies as well as empowering individuals to access crucial information and services. However, almost 40 percent of the world was not using the internet as of 2021. Particularly disenfranchised groups were frequently excluded from digital society, including women and girls, people with disabilities, elders, indigenous populations, people living in poverty, and inhabitants of least developed or developing countries. The digital gender gap was another obstacle for women to overcome on a global level to achieve economic advancement which would ultimately also benefit their communities.

Digital divide is a book subject. It includes 53 books, written by 43 different authors.

Survey on the digital divide in the city of Barcelona

The data represents the results of California's broadband data collection for the number of unique providers meeting the federal threshold speed by census block. The federal threshold for broadband data is 25 Mbps download and 3 Mbps upload.

The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government.

Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized.

Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers.

Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.

Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis.

The data represents the results of California's broadband data collection for facilities-based providers with wireline and/or fixed wireless broadband technologies available to consumers that meet the federal threshold. The federal threshold for broadband data is 25 Mbps downstream bandwidth and 3 Mbps upstream bandwidth.

This statistic shows the percentage difference between the main ICT use indicators of men and women in Spain between 2012 and 2019. The gender digital gap decreased throughout the period of time under consideration, with frequent Internet use being the information technology that shows the only negative points, reaching a gap difference of -1.2 points between men and women in 2019, while the previous year that same gap was 1.9.

The 2021 Australian Digital Inclusion Index (ADII) results showed that people who had a tertiary education had an ADII score of 77.9, closely followed by people who were employed. At the other end of the scale, the ADII score for people with no secondary education was 52.7. People with disabilities and those not in the labor force scored slightly better but remained at the lower end of the digital inclusion scale.

Applications of the Australian Digital Inclusion Index

The ADII provides a benchmark for digital inclusion across various social and economic indicators such as age, cultural background, and income. It can also identify digital disadvantage by highlighting the digital divide between and within specific groups. For example, people over the age of 50 have a lower ADII score than the younger age groups. A similar digital divide can be observed when comparing people on low and high incomes. In modern society it is becoming increasingly important to be digitally literate to communicate and engage with services, however, the ADII helps to identify groups who may be disadvantaged by new digital solutions and the digitalization of services.

Digitalization of government services

One area that is undergoing rapid digitalization in Australia is government services. Since 2015 e-government services can be accessed through the myGov online portal and many government interactions can now be done electronically, without the need to go into a physical service center. While this digitalization of government has simplified processes for many, e-government also risks isolating those who lack the digital literacy or online access to engage fully with online government services.

In 2020, the digital literacy rate in India was found to be higher among men as compared to women. The rate of searching and browsing the internet was 48 percent among the male population as compared to 37 percent among the female population.

In 2021, the gender gap in internet access in Benin stood at 63.2 percent. This was the highest gender disparity recorded among the selected African countries. Internet access was also significantly higher among men than women in Tanzania, Liberia, and Rwanda, with gender gaps ranging from 46 percent to 52 percent. In 2022, Nigeria, Egypt, and South Africa were the countries with the highest number of internet users on the continent.

According to a study conducted by McKinsey Global Institute, the digital maturity across ICT-using sectors was still relatively low by 2018. On average, companies across verticals have reached just over 24 percent of their full digital potential in their ICT sector. The travel industry was the forerunner, having reached 51 percent of the digital frontier.

Resource market reforms have provided opportunities for reducing the digital divide (DD). In this study, we analyzed the theoretical mechanism of the impact of market integration (MI) on DD and the intermediary role of innovation resource (IR) synergy. Data from 271 cities in China were collected, and the DEA–Malmquist index and Thiel index were utilized to quantify DD among these cities during 2011–2019. The intermediary effect and SDM models were adopted for an empirical analysis. The results showed that (1) MI had a negative effect on DD in addition to significant spatial spillover effects. MI widened DD in the local city but narrowed the same in the adjacent cities. (2) IR synergy exerted positive intermediary effects, which were heterogeneous. Through human resource synergy, MI narrowed DD, through knowledge synergy, MI widened DD, and through the R&D capital synergy, MI did not significantly influence DD. (3) There was no significant spatial spillover effect of the intermediary role of IR synergy. Methods For the selection of spatial scope, missing data were excluded from the national list of cities, resulting in 271 cities. Most empirical data were sourced from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, the statistical yearbooks of provinces (municipals and districts) and cities, the web of Mark Data (https://www.macrodatas.cn/). The expedition period was between the year 2011 to 2019 owing to the availability of data.

Type of variable

Variable

Index

Proxy Variables

Data source

Dependent variable

Variables for measuring the efficiency of DE

Input: Labor

Number of employees in information transmission, computer services, and software industries

The China Statistical Yearbook

Output: Total telecom services revenue

Total telecom services revenue

The webpage of Mark Data

Output: DE output

DE index

The webpage of Mark Data

DE efficiency

Total factor productivity

Total factor productivity

/

DD

Thiel index

Thiel index

/

Intermediary variable

Degree of intercity IR collaboration

R&D fund synergy value

R&D fund synergy value

China Science and Technology Statistical Yearbook

HR synergy value

HR synergy value

China Science and Technology Statistical Yearbook

Knowledge synergy value

Knowledge synergy value

China Science and Technology Statistical Yearbook

composite synergy index of IRs

Composite synergy index of IRs

/

Independent variable

MI

MI Index

MI Index

/

Control variable

Economic openness

Trade

Total import and export trade (100 million yuan)

The China Statistical Yearbook

Investment transactions

Foreign capital level: Actual amount of foreign capital used (USD 10,000)

The China Statistical Yearbook

Number of foreign enterprises: Number of foreign direct investment enterprises

The China Statistical Yearbook

Factor flow

Freight volume (10,000 tons)

The China Statistical Yearbook

Passenger traffic volume (10,000 tons)

The China Statistical Yearbook

Transaction system quality

Market stability maintenance capability

Ratio of the number of employees in public management and social organizations to the total population (%)

The China Statistical Yearbook

Industrial coordination degree

Advanced industrial structure

Ratio of tertiary industry output value to secondary industry output value (%)

The China Statistical Yearbook

Urbanization level

Social urbanization

Lighting index of urbanization level

The Harvard Dataverse

Innovation environment

R&D personnel input

Number of full-time teachers in ordinary colleges and universities (per person)

The China Statistical Yearbook