The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

In 2022, the average data used per smartphone per month worldwide amounted to ** gigabytes (GB). The source forecasts that this will increase almost four times reaching ** GB per smartphone per month globally in 2028.

Average time spent sedentary, household population by sex and age group.

General social survey (GSS), average time spent at various locations for the population aged 15 years and over, by population cohorts.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 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 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 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.

Average time spent being physically active, household population by sex and age group.

Average daily time spent by adults on activities including paid work, unpaid household work, unpaid care, travel and entertainment. These are official statistics in development.

General social survey (GSS), average time spent on various activities for the population aged 15 years and over, by sex and main 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

In 2023, Android users in Singapore spent an average of **** hours per day using their mobile devices. This represents an increase from the **** hours that users in the country spent on their devices in 2020.

Daily average time in hours and proportion of day spent on various activities by age group and sex, 15 years and over, Canada and provinces.

General social survey (GSS), average time spent with various social contacts for the population aged 15 years and over, by population cohorts.

We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

Luxury Cosmetics Pop‑Up Events Dataset

A comprehensive, real-world–anchored synthetic dataset capturing 2,133 luxury beauty pop-up events across global retail hotspots. It focuses on limited-edition product drops, experiential formats, and performance KPIs—especially footfall and sell‑through. The data is designed for analytics use cases such as demand forecasting, footfall modeling, merchandising optimization, pricing analysis, and market expansion studies across regions and venue types.

What this dataset contains

• 2,133 events from global hubs across North America, Europe, Middle East, Asia‑Pacific, and Latin America
• Luxury/premium cosmetics brands and their limited‑release SKUs
• Event formats and retail venue archetypes typical of pop‑up retail
• Time windows and lease lengths aligned with short‑term pop‑up activations
• Core commercial KPIs: price, units sold, sell‑through percentage
• Footfall KPI: average daily footfall modeled by location/format/marketing intensity

Ideal use cases

• Pop‑up ROI and performance benchmarking by brand, city, venue type, and format
• Footfall prediction and location strategy (high‑street vs mall vs airport vs districts)
• Limited-edition launch analytics: pricing vs sell‑through dynamics
• Event planning: lease length and timing windows vs outcomes
• Territory planning: region and city segmentation performance
• Portfolio dashboards: cross‑brand comparisons and trend reporting

File formats

• CSV, JSON, XLSX, and SQLite (table: popups)

Target users

• Retail strategy and analytics teams
• Growth, trade marketing, and brand managers
• Data scientists building forecasting and optimization models
• BI developers building dashboards for pop‑up performance

Column Dictionary

Data quality notes

• City and end_date contain a small proportion of nulls to reflect real‑world reporting gaps (e.g., ongoing events).
• avg_daily_footfall varies by locati...

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...

The data in question was generated using the Faker library and is not authentic real-world data. In recent years, there have been numerous reports suggesting the presence of bot voting practices that have resulted in manipulated outcomes within data science competitions. As a result of this, the idea for creating a simulated dataset arose. Although this is the first time that this dataset has been created, it is open to feedback and constructive criticism in order to improve its overall quality and significance.

NAME: The name of the individual. GENDER: The gender of the individual, either male or female. EMAIL_ID: The email address of the individual. IS_GLOGIN: A boolean indicating whether the individual used Google login to register or not. FOLLOWER_COUNT: The number of followers the individual has. FOLLOWING_COUNT: The number of individuals the individual is following. DATASET_COUNT: The number of datasets the individual has created. CODE_COUNT: The number of notebooks the individual has created. DISCUSSION_COUNT: The number of discussions the individual has participated in. AVG_NB_READ_TIME_MIN: The average time spent reading notebooks in minutes. REGISTRATION_IPV4: The IP address used to register. REGISTRATION_LOCATION: The location from where the individual registered. TOTAL_VOTES_GAVE_NB: The total number of votes the individual has given to notebooks. TOTAL_VOTES_GAVE_DS: The total number of votes the individual has given to datasets. TOTAL_VOTES_GAVE_DC: The total number of votes the individual has given to discussion comments. ISBOT: A boolean indicating whether the individual is a bot or not.

As of the third quarter of 2024, Indonesians spent an average of around ***** hours and ** minutes on the internet. Meanwhile, they spent an average of ***** hours and ***** minutes on social media every day. These figures show how the internet has been well incorporated into the daily activities of the population in the country. Internet accessibility in Indonesia  Indonesia has emerged as one of the largest online markets in Asia. The majority of the Indonesians were mobile internet users. This may be due to the affordability and user-friendliness of the mobile devices that are available in the Indonesian market. Embracing the online world The accessibility and affordability of internet data plans made it easier for Indonesians to be more active on social media. As of the third quarter of 2024, Whatsapp and Instagram were the leading social networks in terms of user penetration in Indonesia. In addition, streaming music and podcasts are among the most popular online activities in the country.

Daily average time and proportion of day spent on various activities, by age group and gender, 15 years and over, Canada, Geographical region of Canada, province or territory, 2022.

Key US App Market StatisticsUS App Market SizeUS App Market Revenue by AppUS Smartphone UsersUS Smartphone PopulationTime Spent on Apps in the USUS App Market DownloadsUS Downloads by AppUS Daily...

Diluted-Average-Shares Time Series for Telephone and Data Systems Inc. Telephone and Data Systems, Inc., a telecommunications company, provides communications services to consumer, business, and government in the United States. It operates through three segments: UScellular Wireless, UScellular Towers, and TDS Telecom. The company offers wireless solutions, including a suite of connected Internet of things (IoT) solutions, and software applications for monitor and control, business automation/operations, communication, fleet/asset/video management solutions, security solutions, private cellular networks, and custom and bespoke end-to-end IoT solutions, as well as professional and managed services, such as staff augmentation, IPX services, and SIM management; and critical connectivity solutions comprising wireless priority services and quality priority and preemption options. It also provides devices, such as smartphones and other handsets, tablets, wearables, mobile hotspots, fixed wireless home internet, and IoT devices; accessories, including wireless essentials which include cases, screen protectors, cables, chargers, memory cards, as well as consumer electronics, comprising bluetooth audio, wi-fi enabled cameras, and networking products. In addition, the company offers replace and repair services; Trade-In program through which it buys customers' used equipment; internet connections and all-home Wi-Fi services; TDS TV+, an integrated cloud television platform; local and long-distance telephone service, voice over internet protocol, and enhanced services; broadband, IP-based services, and hosted voice and video collaboration services; and communication services in underserved areas. The company sells and distributes its products through third-party direct sales, retail stores, sales agents, and an online platform to sell services and products. Telephone and Data Systems, Inc. was incorporated in 1968 and is based in Chicago, Illinois.