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.

Context

I got the chance to walk across the Golden Gate Bridge in San Francisco, CA for the first time in 22 years on May 12, 2018. There have been a great many technological advancements since then, as now we are all walking around with powerful computers and sensors in our pockets. I decided it would be fun to measure the bridge and provide others the opportunity to analyze data as to its motion for a brief snippet of time.

Content

This is one minute of data from the "g-force Meter" of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:15 PM local time on May 12, 2018.

Acknowledgments

This dataset is hereby owned by the community under the terms of a very lenient license in the condition that I have published it shortly after recording it.

Inspiration

Maybe this will inspire people to install sensors on the bridge, and other bridges, to monitor for things such as traffic (such as to find an optimum speed limit), dangerous fatigue, or dangerous wind conditions. At the very least, one could use this in comparison with a baseline stable motion to see how the bridge shakes. One could study effects of vehicles traversing the bridge (not that there's any visual data for when that happened relative to this dataset, but I do believe at least one big bus drove by my device during this recording). One could study if there are periodic vibrations, and if so, at what frequencies. This would be even more interesting if correlated with wind data and run compared to several different wind speeds.

- This archive contains the files submitted to the 2nd International
Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files
provided in this package are associated with the paper titled
"Dataset: User side acquisition of People-Centric Sensing in the
Internet-of-Things"

- Content of the package:

+ 1_beacon_table.pkl: The beacon table in Pickle format. It contains
20612286 data points where each data point represents a Bluetooth
beacon with 15 attributes as follows: <_id, host_id, ble_address,
sound_avg_peak, sound_max_peak, sound_count_over_thres_per_frame,
sound_avg_all, sound_avg_over_thres, temperature, humidity,
pressure, eco2_ppm, tvoc_ppb, rssi, timestamp>.

+ 2_device_description_table.pkl: The device description table
provides the mapping between a device's Bluetooth address and its
physical identity (device_id, description, type).

+ 3_checkin_table.pkl: The check-in table provides a timeseries of
user interactions with three Android tablets (i.e. tuples of

+ 4_sample_beacon_table.pkl: The sample beacon table in Pickle
format. It contains 1000 data points where each data point
represents a Bluetooth beacon with 15 attributes as follows: <_id,
host_id, ble_address, sound_avg_peak, sound_max_peak,
sound_count_over_thres_per_frame, sound_avg_all,
sound_avg_over_thres, temperature, humidity, pressure, eco2_ppm,
tvoc_ppb, rssi, timestamp>.

+ 5_sample_device_description_table.pkl: The sample device description
table provides the mapping between a device's Bluetooth address and
its physical identity (device_id, description, type).

+ 6_sample_checkin_table.pkl: The check-in table provides a
timeseries of user interactions with three Android tablets
(i.e. tuples of

+ print_table_heads.py: A Python script which fetches Pickle tables
as DataFrames and prints out the sample entries.

- ACM Reference Format: Chenguang Liu, Jie Hua, Tomasz Kalbarczyk,
Sangsu Lee, and Christine Julien. 2019. Dataset: User side
acquisition of People-Centric Sensing in the Internet-of-Things. In
The 2nd Workshop on Data Acquisition To Analysis(DATA’19), November
10, 2019, New York, NY, USA. ACM, New York, NY, USA, 3 pages.
https://doi.org/10.1145/3359427.3361914

In this table you will find information about CoronaMelder. This concerns two variables: 1. The number of people who downloaded CoronaMelder 2. The number of people who warned others via CoronaMelder 1. The number of downloads is based on data from: - App Store (iOS) - Play Store (Android) - Huawei App Gallery (Android) 2. If you have tested positive for corona, you can voluntarily indicate this in the app, together with an employee of the GGD. The numbers show how many people have done this.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The smartphone penetration in the Philippines was forecast to continuously decrease between 2024 and 2029 by in total 6.4 percentage points. According to this forecast, in 2029, the penetration will have decreased for the fourth consecutive year to 65.75 percent. 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 Laos and Malaysia.