North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming 29 gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

This dataset contains information on the prices of several mobile phones from different brands. It includes details such as the storage capacity, RAM, screen size, camera specifications, battery capacity, and price of each device.

Columns

• Brand: the manufacturer of the phone

• Model: the name of the phone model

• Storage (GB): the amount of storage space (in gigabytes) available on the phone

• RAM (GB): the amount of RAM (in gigabytes) available on the phone

• Screen Size (inches): the size of the phone's display screen in inches

• Camera (MP): the megapixel count of the phone's rear camera(s)

• Battery Capacity (mAh): the capacity of the phone's battery in milliampere hours

• Price ($): the retail price of the phone in US dollars

Each row represents a different mobile phone model. The data can be used to analyze pricing trends and compare the features and prices of different mobile phones.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

In 2021, the global annual cellular data usage is projected to reach roughly 650 thousand petabytes (PB), with approximately 639 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.

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

English(the United States) Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(344 speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Source code, example screenprobe.csv data file, and README.txt for processing, visualising and analysing smartphone use data. csv2data.m converts ScreenProbe.csv to usable data, while barcode.m allows visualisations to be generated. descriptives.m generates descriptive statistics that can be used for quantitative analysis. Source code requires Matlab version 2014b or later, but does not require any specific toolboxes. (ZIP)

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

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

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

Korean Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(211 native speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The number of mobile broadband connections per 100 inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 21.1 connections (+11.49 percent). After the fifteenth consecutive increasing year, the mobile broadband penetration is estimated to reach 204.76 connections and therefore a new peak in 2029. Notably, the number of mobile broadband connections per 100 inhabitants of was continuously increasing over the past years.Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 mobile broadband connections per 100 inhabitants in countries like Canada and Mexico.

Korean(Korea) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(999 Korean), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

In a survey conducted amongst mobile users in Australia in 2022, around 16 percent of respondents indicated that they use their entire mobile internet data allowance each month. Almost one third of respondents indicated that they use most of their data allowance each month. According to the source, the average Australian has around 60 gigabytes of data included in their phone plan.

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

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.

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.

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 Spain 2022 - 2026 Discover more data with ReportLinker!

Indonesian(Indonesia) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, news and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(1,285 people in total), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

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.