The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

In the paper [1], we have provided a comprehensive overview of applicable solutions for proximity detection and contact tracing used to tackle the spread of the COVID-19 pandemic. On the webpage [2], we have provided the most recent findings of the existing solutions.

Structure description of JSON data format is attached in the file ReadMe.pdf

References:

[1] https://www.mdpi.com/2306-5729/5/4/87

[2] https://sites.tuni.fi/survey-of-digital-solutions/

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Mexico and Canada.

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”.

About Dataset

This dataset contains information about mobile phones available in Ghana, including details about various phone models, their specifications, and pricing. The data was collected through web scraping, providing a comprehensive overview of the mobile phone market in Ghana.

Key Features

• Brand & Model: The dataset includes details on various phone models from different brands, allowing users to explore a wide range of options.

• Specifications: Detailed phone specifications are provided, such as whether the phone supports an SD card, the main camera setup, resolution, display type, SIM card configuration, operating system, color options, and more.

• Geographical Information: Users can filter and analyze the dataset based on region and location in Ghana, making it useful for understanding the availability of different phone models in specific areas.

• Hardware & Software: Essential hardware features like screen size (in inches), battery capacity (in mAh), storage (in GB), RAM (in GB), and selfie camera resolution (in MP) are included.

• Pricing: The dataset also provides pricing information (in Ghanaian Cedis - ¢), enabling users to compare the cost of various phone models.

This dataset is valuable for consumers, researchers, and businesses interested in the mobile phone market in Ghana. It can be used for market analysis, consumer insights, and decision-making related to mobile phone purchases. Researchers can also use the data for further analysis and modeling.

Collecty dataset is a dataset for multimodal transport analytics from mobile devices collected by users as they move through the transportation network. Each sample in dataset is labelled with a corresponding transport mode. Eight transport modes are present in the dataset: Car, Bus, Walking, Bicycle, Train, Tram, Running and Electric Scooter. During data collection, data from the accelerometer, magnetometer, and gyroscope sensors mounted within the mobile device were stored.

CITATION:

When incorporating these data into a research output, such as a publication or presentation, kindly cite the provided source and indicate that comprehensive details regarding the dataset are available within the same article:

Dataset for multimodal transport analytics of smartphone users - Collecty, M. Erdelić, T. Erdelić and T. Carić, Data in Brief, 2023

@article{ERDELIC2023109481, title = {Dataset for multimodal transport analytics of smartphone users - Collecty}, journal = {Data in Brief}, volume = {50}, pages = {109481}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.109481}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923005814}, author = {Martina Erdelić and Tomislav Erdelić and Tonči Carić} }

Content

This database contains multiple images in different dimensions. Besides, the images were separated and categorized into two types: There is a cellphone (label = 1), there is no cellphone (label = 0). Thus, it is possible to build algorithms for the binary classification of objects or a computational model that allows locating the position of mobile phones in the image, and this will depend on your creativity to work with this dataset.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3457313%2F45a0ab95281bf9664a55406fbacfa2fe%2Fsave-cellphone.JPG?generation=1568096853341492&alt=media" alt="">

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.

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario D. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

Intending to cover the existing gap regarding behavioral datasets modelling interactions of users with individual a multiple devices in Smart Office to later authenticate them continuously, we publish the following collection of datasets, which has been generated after having five users interacting for 60 days with their personal computer and mobile devices. Below you can find a brief description of each dataset.Dataset 1 (2.3 GB). This dataset contains 92975 vectors of features (8096 per vector) that model the interactions of the five users with their personal computers. Each vector contains aggregated data about keyboard and mouse activity, as well as application usage statistics. More info about features meaning can be found in the readme file. Originally, the number of features of this dataset was 24 065 but after filtering the constant features, this number was reduced to 8096. There was a high number of constant features to 0 since each possible digraph (two keys combination) was considered when collecting the data. However, there are many unusual digraphs that the users never introduced in their computers, so these features were deleted in the uploaded dataset.Dataset 2 (8.9 MB). This dataset contains 61918 vectors of features (15 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about application usage statistics. More info about features meaning can be found in the readme file.Dataset 3 (28.9 MB). This dataset contains 133590vectors of features (42 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about the gyroscope and Accelerometer sensors.More info about features meaning can be found in the readme file.Dataset 4 (162.4 MB). This dataset contains 145465vectors of features (241 per vector)that model the interactions of the five users with both personal computers and mobile devices. Each vector contains the aggregation of the most relevant features of both devices. More info about features meaning can be found in the readme file.Dataset 5 (878.7 KB). This dataset is composed of 7 datasets. Each one of them contains an aggregation of feature vectors generated from the active/inactive intervals of personal computers and mobile devices by considering different time windows ranging from 1h to 24h.1h: 4074 vectors2h: 2149 vectors3h: 1470 vectors4h: 1133 vectors6h: 770 vectors12h: 440 vectors24h: 229 vectors

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.
All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors:
Sensors:
This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer.

Statistics:
These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces.

Voice:
This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding.


A summary of the details of the collected database.
- Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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 smartphone penetration in countries like North America and the Americas.

This dataset provides detailed, time-segmented records of mobile data, call, and SMS usage for telecom customers, including network type, device, and location context. It enables in-depth analysis of user consumption patterns, peak usage periods, and regional trends, supporting telecom plan optimization, network planning, and customer segmentation.

Veraset 'Movement' (GPS Footfall Data, from a mobile device) offers unparalleled real-time insights into footfall traffic patterns globally covering the US and 170 other countries from 2018 to the present day.

This dataset covers over 170+ countries and comprises billions of pseudonymous GPS signals daily, creating one of the cleanest Mobile Location Datasets available.

Veraset provides the most reliable, compliant, commercially available Location Data dataset on the market, drawing on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Prioritizing compliance and privacy, it serves as a foundation for advanced analytics and strategic planning across various industries.

Our work has been used by Fortune 500 companies, leading institutions, and top brands that need reliable geospatial data.

Veraset’s Movement (raw Location Data) product is the best choice for anyone building products or models powered by historical raw location data.

Uses for Location Data: - Infrastructure Planning - Route Optimization and Human Migration Patterning - Public Transit Optimization - Placement and Targeting - Advertising and Attribution - Segmentation and Audience Building - Competitive Analysis

For up-to-date schema, visit: https://www.veraset.com/docs/movement

Dataset Info:

MyDigitalFootprint (MDF) is a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modelling in mobile environments. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users by following the in-the-wild data collection approach: the data has been collected in the users' natural environment, without limiting their usual behaviour. Existing public datasets generally consist of a limited set of context data, aimed at optimising specific application domains (human activity recognition is the most common example). On the contrary, the dataset contains a comprehensive set of information describing the user context in the mobile environment.

The complete analysis of the data contained in MDF has been presented in the following publication:

https://www.sciencedirect.com/science/article/abs/pii/S1574119220301383?via%3Dihub

The full anonymised dataset is contained in the folder MDF. Moreover, in order to demonstrate the efficacy of MDF, there are three proof of concept context-aware applications based on different machine learning tasks:

1. A social link prediction algorithm based on physical proximity data,
2. The recognition of daily-life activities based on smartphone-embedded sensors data,
3. A pervasive context-aware recommender system.

For the sake of reproducibility, the data used to evaluate the proof-of-concept applications are contained in the folders link-prediction, context-recognition, and cars, respectively.

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user-calls-user - Reality mining network data consists of human mobile phone call events between a small set of core users at the Massachusetts Institute of Technology (MIT) whom actually were assigned mobile phones for which all calls were collected. The data also contains calls from users outside this small set of users to other phones of individuals that were not actively monitored and thus these nodes generally have fewer edges than nodes within the small set of users at MIT that participated in the experiment and were assigned phones. The data was collected collected by the Reality Mining experiment performed in 2004 as part of the Reality Commons project. The data was collected over 9 months using 100 mobile phones. A node represents a person; an edge indicates a phone call or voicemail between two users. See http://realitycommons.media.mit.edu/realitymining.html for more details.

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