North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

In 2021, the global annual cellular data usage is projected to reach roughly *** thousand petabytes (PB), with approximately *** thousand petabytes coming from the use of mobile handsets, in other words, mobile phones. Tablets and cellular IoT devices currently do not compare to mobile phones in terms of data usage, but they are expected to grow in the upcoming years.

As of the third quarter of 2024, approximately 93.9 percent of internet users in the United States went online via any kind of mobile phone, while 93.9 percent reported doing so via smartphones. By comparison, laptop or desktop internet access was reported by around 72.7 percent of respondents. Additionally, 64.3 percent of U.S. internet users reported going online with their laptop or desktop device, while around 27 percent reported doing so with a work laptop. Smartphone usage in the United States During the past years, the number of smartphone users in the United States has increased. According to recent data, 85 percent of the adults in the country own a smartphone. This has led to high competition between the biggest manufacturers in the field. Apple is the leading manufacturer in the U.S., with a market share of 53 percent, followed by Samsung and Motorola/Lenovo. Meanwhile, there is more competition when it comes to operating systems. Apple iOS, which is used on all devices created by Apple, and Google Android, which is used for Samsung devices, have the biggest user share. Usage of other devices in the U.S. Smart home devices have become popular in recent years. It is projected that in 2025 the penetration rate for Smart Home segments like control and connectivity, as well as security, will grow up to 50 and 35 percent respectively. For users in the United States, the most common device for watching shows or movies was a TV set. According to the research, more than 30 percent of the respondents spent more than 20 hours weekly in front of a TV. In comparison, the majority of those who watched shows or movies on a computer, tablet, or smartphone spent less than an hour weekly on such activity.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

Forecast: Mobile Data Usage Per Mobile Broadband Subscription in Finland 2022 - 2026 Discover more data with ReportLinker!

Forecast: Mobile Data Usage Per Mobile Broadband Subscription in Spain 2022 - 2026 Discover more data with ReportLinker!

Pearson Correlation Coefficient between mobile phone usage duration and mobile phone addiction.

This dataset contains two significant mobile gestures for brain-mobile phone interfaces (BMPIs: (i) motor imagery of tapping on the screen of a mobile device and (ii) motor imagery of swiping down with a thumb on the screen of a mobile device. The raw EEG signals were recorded using the Emotiv EPOC Flex (Model 1.0) headset with saline-based sensors and Emotiv Pro (2.5.1.227) software. The sampling rate is 128 Hz. Each epoch contains 3.5 s signals. The first 1 s signal is recorded before the MI task starts (5 s to 6 s interval in the timing plan), and the next 2.5 s signal is recorded during the MI execution (6 s to 8.5 s interval in the timing plan). Please refer to the reference study below for details.The file names are constructed as follows. For example, taking "D01_s1" and "D01" in the file name refers to subject "01", and "s1" refers to session 1 ("s2" refers to session 2). The label data is given in a separate folder in Matlab format.The data is provided in two different forms for use (the desired is preferable):The set_files folder contains the data prepared for import in EEGLAB. EEGLAB must be installed, and the set files must be imported to access the data. The data is in epoched format in 3D (channels, sample_points, trials). With the EEGLAB interface, all the data can be accessed, and EEGLAB functions can be executed. Also, the EEG variable, which is built after importing the *.set file, contains all the information about the experiment. With the EEG.data variable, epoched data in the dimensions (channels, sample_points, trials) can be accessed.The mat_files folder contains data in mat file format. In these files, epoched data is stored in a 3-D array of size (channels, sample_points, trials). You can access the data as follows. For example, all data from the first session of subject D01 can be retrieved as follows. Load the mat file with the load('D01_s1.mat') code, and access the data using the EEG variable in the workspace. For instance, 13x448 x101 sized epoched data (channels, sample_points, trials) can be retrieved with the command EEG.data. Other information about the experiments and subjects is also included in the fields of the EEG variable.This research was supported by the Turkish Scientific and Research Council (TUBITAK) under project number 119E397.The following article must be used in academic studies with reference. Permission must be obtained for use in commercial studies.Journal: Neural Computing and Applications.DOI : 10.1007/s00521-024-10917-5.Title : MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM.

To rapidly monitor recent changes in the use of telemedicine, the National Center for Health Statistics (NCHS) and the Health Resources and Services Administration’s Maternal and Child Health Bureau (HRSA MCHB) partnered with the Census Bureau on an experimental data system called the Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of the COVID-19 pandemic on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

Indians used over ***** petabytes of mobile data in December 2020, indicating a massive increase in data consumption compared to around ***** petabytes used in December 2019. Overall, the country saw a year-on-year data usage growth of around ** percent from December 2019 to December 2020.

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Colombia Use of Mobile Phone: Total: 25 to 54 Years data was reported at 18,869.160 Person th in 2017. This records an increase from the previous number of 18,575.525 Person th for 2016. Colombia Use of Mobile Phone: Total: 25 to 54 Years data is updated yearly, averaging 18,403.524 Person th from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 18,869.160 Person th in 2017 and a record low of 18,018.865 Person th in 2013. Colombia Use of Mobile Phone: Total: 25 to 54 Years data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.TB003: Technology and Communication Usage.

Colombia Use of Mobile Phone: Male: 12 to 24 Years data was reported at 4,875.020 Person th in 2017. This records an increase from the previous number of 4,840.445 Person th for 2016. Colombia Use of Mobile Phone: Male: 12 to 24 Years data is updated yearly, averaging 4,875.020 Person th from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 4,959.963 Person th in 2013 and a record low of 4,802.004 Person th in 2014. Colombia Use of Mobile Phone: Male: 12 to 24 Years data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.TB003: Technology and Communication Usage.

The percentage of respondents who report that someone, such as a family member, sets rules about how they can use a mobile phone. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

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This dataset reports on public internet use in the Timberland Regional Library District, a five-county rural library district serving Thurston, Lewis, Mason, Pacific, and Grays Harbor counties. It includes a count of internet sessions and minutes used at each library location.

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