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.

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.

When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place at any time" as an answer. 55 percent did so in our online survey in 2024. Looking to gain valuable insights about users of internet providers worldwide? Check out our

When asked about "Attitudes towards the internet", most Japanese respondents pick "I could no longer imagine my everyday life without the internet" as an answer. 56 percent did so in our online survey in 2024. Looking to gain valuable insights about users of internet providers worldwide? Check out our

When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place at any time" as an answer. 49 percent did so in our online survey in 2024. Looking to gain valuable insights about users of internet providers worldwide? Check out our

The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.

Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.

A minimal requirements Internet of Things (IoT) network system was built to produce a dataset according to IDS testing needs for IoT security. Testing was performed with 12 scenarios and resulted in 24 datasets which consisted of normal, attack and combined normal-attack traffic data. Testing focused on three denial of service (DoS) and distributed denial of service (DDoS) attacks—“finish” (FIN) flood, User Datagram Protocol (UDP) flood, and Zbassocflood/association flood—using two communication protocols, IEEE 802.11 (WiFi) and IEEE 802.15.4 (ZigBee). A preprocessing test result obtained 95 attributes for the WiFi datasets and 64 attributes for the Xbee datasets .

TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis

Abstract-Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenarios normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.

Citing
Citation data : "TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis" - https://online-journals.org/index.php/i-joe/article/view/9848

@article{article,

author = {Stiawan, Deris and Wahyudi, Dimas and Heryanto, Ahmad and Sahmin, Samsuryadi and Idris, Yazid and Muchtar, Farkhana and Alzahrani, Mohammed and Budiarto, Rahmat},

year = {2019},
month = {04},
pages = {124},
title = {TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis},
volume = {15},
journal = {International Journal of Online and Biomedical Engineering (iJOE)},
doi = {10.3991/ijoe.v15i07.9848}
}

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.

Citing
Citation data : "Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)" - https://ieeexplore.ieee.org/document/9251292

@inproceedings{inproceedings,

author = {Sharipuddin, and Purnama, Benni and Kurniabudi, Kurniabudi and Winanto, Eko and Stiawan, Deris and Hanapi, Darmawiiovo and Idris, Mohd and Budiarto, Rahmat},

year = {2020},
month = {10},
pages = {114-118},
title = {Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)},
doi = {10.23919/EECSI50503.2020.9251292}
}

The purpose of the study: to analyse Lithuanian residents attitude towards virtual social networks, behaviour and intensity of involvement in networks. Major investigated questions: respondents were asked if they use internet frequently. Means and frequency of respondents internet use was analysed after a group of questions was presented. Respondents who visit electronic social networks were asked which networks they visit and which social electronic network they visit most frequently. It was questioned how often respondents carries out listed activities in electronic social network which they visit most frequently. It was analysed how often respondents interact with their family members, friends (people they know), colleagues (co-workers) and other people in electronic social network. Respondents were asked what information they get through electronic social network which they visit the most. It was questioned if respondents belong to any like-minded group, organization or community in which members interact only through electronic social network and it was analysed if respondents feel close to this organization. Further, respondents were asked with which listed association (organization) they communicated via electronic social networks. It was analysed if respondents would contact with like-minded group, organization or community in different situations via electronic social network. Socio-demographic characteristics: gender, age, duration of education, education, employment status of the respondent and his / her husband / wife / permanent partner, profession (occupation), trade union membership, religion, participation in religious rites, political views, voting in the last Seimas elections, nationality, household size, respondent's average and total average monthly household income, marital status, place of residence, satisfaction with quality of life, change in living conditions, received social benefits, etc.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Updated version 1.0.1:

We present version 1.0.1 to the DDSA database, the improvements made to version 1.0.0 are described below:

1. New hydrological information attributes have been included:
   1. Aridity index
   2. Residence time
   3. Degree of regulation.
2. A shapefile of watersheds for each dam has been included.
3. Supplementary table 1: Future Dams in South America has been included.

Recommended citation

Article citation will be added once the article is available.

Use of the dataset

Before using the dataset, please notify us (be.paredes@alumnos.upm.es; be.paredes@uta.edu.ec) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using this dataset.

Description

Dams and their reservoirs generate major impacts on society and the environment. In general, its relevance relies on facilitating the management of water resources for anthropogenic purposes. However, dams could also generate many potential adverse impacts related to safety, ecology or biodiversity. These factors, and the additional effects that climate change could cause in these infrastructures and their surrounding environment, highlight the importance of dams and the necessity for their continuous monitoring and study. There are several studies examining dams both at regional and global scale, however, those that include the South America region focus mainly on the most renowned basins (primarily the Amazon basin), most likely due to the lack of records on the rest of the basins of the region. For this reason, a consistent database of georeferenced dams located in South America is presented: Dataset of georeferenced dams in South America DDSA. It contains 1,010 entries of dams with a combined reservoir volume of 1,017 cubic kilometres and it is presented in form of a list describing a total of 24 attributes that include the dams name, characteristics, purposes and georeferenced location. Also, hydrological information on the dams’ catchments is also included: catchment area, mean precipitation, mean near-surface temperature, mean potential evapotranspiration, mean runoff, catchment population, catchment equipped area for irrigation, aridity index, residence time and degree of regulation. Information was obtained from public records, governments records, existing international databases and from extensive internet research. Each register was validated individually and geolocated using public access online map browsers and then, hydrological and additional information was derived from a hydrological model computed using the HydroSHEDS dataset. With this database, we expect to contribute to the development of new research in this region.

Content

The files included in the Dataset of georeferenced dams in South America DDSA are:

• 1. Dam Information
• 2.1. Dam Hydrological Information - Catchment Area
• 2.2. Dam Hydrological Information - Catchment Mean Monthly Near Surface Temperature
• 2.3. Dam Hydrological Information - Catchment Mean Monthly Precipitation
• 2.4. Dam Hydrological Information - Catchment Mean Monthly Potential Evapotranspiration
• 2.5. Dam Hydrological Information - Catchment Mean Monthly Runoff
• 2.6. Dam Hydrological Information - Catchment Population
• 2.7. Dam Hydrological Information - Catchment Eqquiped Area for Irrigation
• 2.8. Dam Hydrological Information - Aridity Index
• 2.9. Dam Hydrological Information - Residence Time
• 2.10. Dam Hydrological Information - Degree of Regulation
• 3. Dataset Attribute Description
• 4. Dataset Data Source
• 5. Dataset in KMZ format
• 6. Dataset in SHAPEFILE format (dams)
• 7. Dataset in SHAPEFILE format (dams catchments)
• 8. Supplementary Table 1: Future Dams in South America

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.DataOverviewTilesHundreds 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 35 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 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 FrequencyLayers 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, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.Data is subject to be reaggregated regularly in order to honor Data Subject Access Requests (DSAR) as is applicable in certain jurisdictions under laws including but not limited to General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Lei Geral de Proteção de Dados (LGPD). Therefore, data accessed at different times may result in variation in the total number of tests, tiles, and resulting performance metrics.

The first Media Barometer was conducted in 1979 and since then the survey has been carried out annually. The study intends to highlight the proportion of the Swedish population having used on an average day in each year studied a number of specific media: radio, television, teletext, video, movies, audio cassette, record player / CD, newspaper, tabloid, weekly / monthly newspaper, magazine and book (from 1995 are also direct mail and new media technologies are included); a specific area is related to Internet use. Purpose: Describe the trends and changes in people's use of mass media. Mediebarometern genomfördes första gången 1979 och har sedan dess genomförts varje år. Studien avser att belysa hur stor andel av den svenska befolkningen som en genomsnittlig dag under respektive år tagit del av ett antal enskilda medier: radio, TV, text-TV, video, bio, ljudkassett, grammofon/CD, morgontidning, kvällstidning, vecko-/månadstidning, tidskrift och bok (från 1995 omfattas även direktreklam och ny medieteknologi); ett särskilt område rör internetanvändning. Syfte: Beskriva tendenser och förändringar i människors nyttjande av massmedier. Data collection was performed for 42 randomly selected days during the periods from 31/1 to 20/6 and 19/8 to 12/12 2005. The selection of interview days was stratified to achieve a balance between the different days of the week, which means that the interviews were conducted on 6 over the year distributed Mondays, Tuesdays, Wednesdays, etc. The sample consisted of a simple random sample of individuals of the Swedish population aged between 9 and 79 years. The sample was drawn from the population register and included 5860 persons living in Sweden. Non-response consisted of set of sampling units without a telephone number, wrong number, outside the target group and out of town during the whole period, etc.,and in the result a net sample of 5223 people was achieved. Of these, interviews were conducted with 3436 people. This means that the survey has a response rate of 70 percent. The proportion of pronounced refusals was 15 percent, while the remaining nonresponse is made up of those not reached, short-term sick, etc.Data collection was performed for 42 randomly selected days during the periods from 31/1 to 20/6 and 19/8 to 12/12 2005. The selection of interview days was stratified to achieve a balance between the different days of the week, which means that the interviews were conducted on 6 over the year distributed Mondays, Tuesdays, Wednesdays, etc. The sample consisted of a simple random sample of individuals of the Swedish population aged between 9 and 79 years. The sample was drawn from the population register and included 5860 persons living in Sweden. Non-response consisted of set of sampling units without a telephone number, wrong number, outside the target group and out of town during the whole period, etc.,and in the result a net sample of 5223 people was achieved. Of these, interviews were conducted with 3436 people. This means that the survey has a response rate of 70 percent. The proportion of pronounced refusals was 15 percent, while the remaining nonresponse is made up of those not reached, short-term sick, etc. Datainsamling genomfördes under 42 slumpmässigt utvalda dagar under perioderna 31/1 till 20/6 och 19/8 till 12/12 2005. Urvalet av intervjudagar stratifierades för att uppnå en jämn fördelning mellan olika veckodagar, vilket innebär att intervjuerna genomförts under 6 över året utspridda måndagar, tisdagar, onsdagar o s v. Urvalet bestod av ett obundet slumpmässigt individurval av Sveriges befolkning i åldern 9 till 79 år. Urvalet drogs ur befolkningsregister och omfattade 5 860 personer boende i Sverige. Bortfall bestående av ej telefonnummersatta urvalsenheter, felaktiga nummer, ej målgrupp och bortresta under hela perioden osv, gav ett nettourval på 5 223 personer. Av dessa har intervjuer genomförts med 3 436 personer. Detta betyder att undersökningen har en svarsfrekvens på 70 procent. Andelen uttalade svarsvägrare uppgick till 15 procent medan resterande bortfall består av ej anträffade, korttidssjuka mm.Datainsamling genomfördes under 42 slumpmässigt utvalda dagar under perioderna 31/1 till 20/6 och 19/8 till 12/12 2005. Urvalet av intervjudagar stratifierades för att uppnå en jämn fördelning mellan olika veckodagar, vilket innebär att intervjuerna genomförts under 6 över året utspridda måndagar, tisdagar, onsdagar o s v. Urvalet bestod av ett obundet slumpmässigt individurval av Sveriges befolkning i åldern 9 till 79 år. Urvalet drogs ur befolkningsregister och omfattade 5 860 personer boende i Sverige. Bortfall bestående av ej telefonnummersatta urvalsenheter, felaktiga nummer, ej målgrupp och bortresta under hela perioden osv, gav ett nettourval på 5 223 personer. Av dessa har intervjuer genomförts med 3 436 personer. Detta betyder att undersökningen har en svarsfrekvens på 70 procent. Andelen uttalade svarsvägrare uppgick till 15 procent medan resterande bortfall består av ej anträffade, korttidssjuka mm.

Percentage of Canadians' time spent online and using video streaming services and video gaming services, in a typical week.

The purpose of the study: to find out Lithuanian residents opinion about their work conditions and forms of virtual work spreading. Major investigated questions: respondents were asked to describer their main work and workplace. They were asked if it is/was overall possible to perform respondents main work without going to workplace that is not in their home. It was analysed how much time do they spend (or have spent in the past) in their main work using the internet (for work purposes) on a regular work day. Respondents were asked how appealing work that can be done in home/ from home (without going to workplace) and via internet is. Further, respondents were asked how much time they work/have worked virtually in their regular work day. It was analysed how often respondents have to/had to work virtually not on their work hours in the evenings and (or) nights, on weekends and holidays. Socio-demographic characteristics: gender, age, duration of education, education, employment status of the respondent and his / her husband / wife / permanent partner, profession (occupation), respondent's trade union membership, religion, participation in religious rites, political views, political and social activism, voting in the last Seimas elections, nationality, household size, average and total average monthly household income of the respondent, marital status, place of residence.

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.DataOverviewTilesHundreds 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 FrequencyLayers 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.

When asked about "Attitudes towards the internet", most Australian respondents pick "It is important to me to have mobile internet access in any place at any time" as an answer. 53 percent did so in our online survey in 2024. Looking to gain valuable insights about users of internet providers worldwide? Check out our

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Caribbean and Europe.

The number of Youtube users in Europe was forecast to continuously increase between 2024 and 2029 by in total 7.8 million users (+3.61 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 223.61 million users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.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 Youtube users in countries like North America and Australia & Oceania.

The number of Facebook users in Africa was forecast to continuously increase between 2024 and 2028 by in total 141.6 million users (+56.79 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 390.94 million users and therefore a new peak in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 Facebook users in countries like Europe and Asia.

The number of Instagram users in Africa was forecast to continuously increase between 2024 and 2028 by in total 39.1 million users (+57.16 percent). After the sixth consecutive increasing year, the Instagram user base is estimated to reach 107.54 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 Instagram users in countries like Europe and Caribbean.