The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

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.

This statistic shows the daily digital data engagement interactions per person worldwide from 2010 to 2025. The average number of data interactions per connected person per day is expected to increase dramatically, from *** interactions per day in 2010 to almost ************* interactions per day by 2025.

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.

The Everyday Itinerary Dataset is the first public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. It is a manually curated dataset of Katherine Dunham's touring from 1937-1962, encompassing Dunham's daily locations, travel, and performances. This dataset tracks geographic location and, less comprehensively, the accommodation in which Dunham stayed each night; the theatres, nightclubs, television studios, and other places where she and the company performed; the modes of transportation used when travel occurred; additional transit cities through which she passed; and whether or not Dunham was likely to be in rehearsals or giving public performances. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor.

Database Backup Software Market Outlook

The global database backup software market size is projected to grow from USD 9.5 billion in 2023 to USD 16.3 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 6.1% during the forecast period. The increasing reliance on data across various industries and the critical need for data protection are primary growth factors driving the market. As organizations continue to digitize their operations, the demand for reliable and efficient database backup systems has surged, ensuring that data integrity and availability are maintained in the face of potential data loss scenarios.

One of the major growth factors propelling the database backup software market is the exponential increase in data generation and consumption. With the proliferation of digital platforms, IoT devices, and mobile applications, the volume of data created every day is reaching unprecedented levels. Consequently, organizations recognize the imperative need to safeguard their data assets to ensure business continuity and operational efficiency. The pressing requirement for regulatory compliance further accelerates the adoption of database backup solutions, as industries like BFSI and healthcare must adhere to stringent data protection regulations.

Another significant factor contributing to market growth is the widespread adoption of cloud-based solutions. Cloud computing offers scalability, cost-effectiveness, and flexibility, which are attractive to businesses of all sizes. Cloud-based database backup software allows organizations to back up their data remotely and securely, without the need for extensive on-premises infrastructure. This ease of deployment and management is particularly beneficial for small and medium-sized enterprises (SMEs), which often lack the resources for extensive IT infrastructure. Furthermore, the growing trend of hybrid cloud deployments is also facilitating the adoption of cloud-based database backup solutions, offering a balance of control and convenience.

The increased awareness of cyber threats and data breaches also fuels the growth of the database backup software market. As cyber-attacks become more sophisticated and pervasive, businesses are investing in robust backup solutions to mitigate the risk of data loss. Database backup software not only provides a safety net in case of cyber incidents but also ensures quick recovery and minimal downtime. This capability is crucial for maintaining customer trust and avoiding financial losses. Consequently, companies across sectors are prioritizing database backup as an integral component of their broader cybersecurity strategy.

In the realm of data management, Database Replication Software plays a pivotal role in ensuring data consistency and availability across multiple locations. This software allows for the seamless duplication of data from one database to another, providing a robust mechanism for disaster recovery and high availability. As businesses expand globally, the need to access real-time data from different geographical locations becomes crucial, and database replication offers a solution by synchronizing data across distributed systems. This capability not only enhances operational efficiency but also supports business continuity by minimizing downtime during data migrations or system failures. With the increasing complexity of data environments, the demand for sophisticated replication solutions is on the rise, driving innovation and competition in the market.

Regionally, North America holds a significant share of the database backup software market, driven by the presence of major technology companies and a mature IT infrastructure. The region's early adoption of advanced technologies and a strong focus on data security contribute to its leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digitization across industries in countries like China and India, coupled with increasing investments in IT infrastructure, are key factors driving market expansion in this region. Additionally, the rising demand for cloud-based solutions and government initiatives to enhance data security further bolster the market's growth prospects in Asia Pacific.

Component Analysis

The database backup software market is bifurcated into two primary components: software and services. The software segment encompasses a wide range of products that offer function

Globally the average user spends 52 minutes on TikTok every day. About 90% of their worldwide users access TikTok on a daily basis.

A. SUMMARY This dataset is used to report on public dataset access and usage within the open data portal. Each row sums the amount of users who access a dataset each day, grouped by access type (API Read, Download, Page View, etc).

B. HOW THE DATASET IS CREATED This dataset is created by joining two internal analytics datasets generated by the SF Open Data Portal. We remove non-public information during the process.

C. UPDATE PROCESS This dataset is scheduled to update every 7 days via ETL.

D. HOW TO USE THIS DATASET This dataset can help you identify stale datasets, highlight the most popular datasets and calculate other metrics around the performance and usage in the open data portal.

Please note a special call-out for two fields: - "derived": This field shows if an asset is an original source (derived = "False") or if it is made from another asset though filtering (derived = "True"). Essentially, if it is derived from another source or not. - "provenance": This field shows if an asset is "official" (created by someone in the city of San Francisco) or "community" (created by a member of the community, not official). All community assets are derived as members of the community cannot add data to the open data portal.

According to Cognitive Market Research, the global Data Protection as a Service DPAAS market size will be USD 28241.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 20.80% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 10449.47 million in 2025 and will grow at a compound annual growth rate (CAGR) of 18.6% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 8190.12 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 6778.03 million in 2025 and will grow at a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 1073.19 million in 2025 and will grow at a compound annual growth rate (CAGR) of 19.8% from 2025 to 2033.
Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 1129.67 million in 2025 and will grow at a compound annual growth rate (CAGR) of 20.1% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 621.32 million in 2025 and will grow at a compound annual growth rate (CAGR) of 20.5% from 2025 to 2033.
Payment Processing category is the fastest growing segment of the Data Protection as a Service DPAAS industry

Market Dynamics of Data Protection as a Service DPAAS Market

Key Drivers for Data Protection as a Service DPAAS Market

Escalating Cybersecurity Threats and Data Breaches to Boost Market Growth

The rising frequency and complexity of cyberattacks have significantly intensified concerns around data security. Organizations are increasingly grappling with threats such as ransomware, data breaches, and phishing attacks, which can result in severe financial losses and reputational harm. For example, in 2023, the U.S. reported 2,365 data breaches impacting approximately 343.3 million individuals—a staggering 72% increase compared to 2021. In the UK, half of all businesses (50%) and nearly a third of charities (32%) reported experiencing some form of cybersecurity breach or attack in the past year. The figures are even higher among medium-sized businesses (70%), large enterprises (74%), and high-income charities with annual revenues over £500,000 (66%). Phishing remains the most prevalent type of attack, affecting 84% of businesses and 83% of charities. This is followed by impersonation attacks via email or online platforms (35% of businesses and 37% of charities) and malware infections (17% of businesses and 14% of charities). This escalating threat landscape highlights the critical need for robust data protection strategies, driving demand for Data Protection as a Service (DPaaS) solution. These services offer advanced security features such as data encryption, multi-factor authentication, and real-time monitoring to help organizations safeguard their sensitive information.

https://www.gov.uk/government/statistics/cyber-security-breaches-survey-2024/cyber-security-breaches-survey-2024//./

Increasing Data Volumes from Digital Transformation and IoT to Boost Market Growth

The rapid surge in data generation—driven by digital transformation initiatives and the widespread adoption of Internet of Things (IoT) devices—has created an urgent need for efficient storage, backup, and recovery solutions. Global data volume skyrocketed from 2 zettabytes (ZB) in 2010 to an astounding 64.2 ZB by 2020, surpassing even the number of observable stars in the universe. This figure is projected to reach 181 ZB by 2025. Despite this explosive growth, only about 2% of the data created in 2020 was actually saved and stored by 2021. On a daily basis, the world produces around 2.5 quintillion bytes of data, with 90% of all existing data generated in just the past two years. Additionally, over 40% of internet data in 2020 was generated by machines. In this context, Data Protection as a Service (DPaaS) emerges as a vital solution, offering scalable, secure, and cost-effective means to protect this ever-expanding volume of data. DPaaS ensures data availability, security, and compliance with increasingly stringent regulatory requirements.

https://spacelift.io/blog/how-much-data-is-generated-every-day./

Restraint Factor for the Da...

The capstone was completed in Power Bi. Due to restrictions on sharing, I've made a powerpoint of the report that demonstrates the data in use and the insight gained from the research.

dailyActivity_merged contains a summary of daily activity such as total distance, intensities (i.e., very active, sedentary), and total minutes in intensities.

There is a discrepancy between the total distance and the sum of VeryActiveDistance, ModeratelyActiveDistance, LightActiveDistance and SedentaryActiveDistance. With an average of 5.489702122 miles in tracker distance, this can be off on average up to .077053 miles or 370 feet.

1.6% (15/940) of tracker distances listed do not match total distance. I will need clarification between total distance and tracker distance. For my report, I will be using total distance.

Aggregated daily data does not contain null values. No assumptions need to be made based on this.

98/940 records are <= 500 feet. 77/98 have a total of 0 steps and the remaining data is 0. A filter has been added to void records where total steps are <= 500.

I removed 5/12/2016 due to lack of sufficient user data.

dailyActivity_merged contains the same calories as dailyCalories_merged when using activity date and ID as a primary key.

dailyActivity_merged does not contain the same calories as hourlyCalories_merged when summing the calories per day in the hourly table.

PseudoData contains mock data I created for users. Pseudo names were created for the ID's to make data relatable for the audience. Teams were generated in the event the analysis discussed this possibility.

heartrate_seconds_merged contains heart rate value every 15 seconds over time.

I removed 5/12/2016 due to lack of sufficient user data. Events were averaged to the nearest hour. The windows function lag() was used to find time between events to determine usage. The visuals will show lag, or time when the device is not used, if it's greater than the total charge time, 2 hours.

hourlyCalories_merged contains calories per hour per ID. The Date and Time were separated into two columns.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

As of the third quarter of 2024, internet users in South Africa spent more than **** hours and ** minutes online per day, ranking first among the regions worldwide. Brazil followed, with roughly **** hours of daily online usage. As of the examined period, Japan registered the lowest number of daily hours spent online, with users in the country spending an average of over **** hours per day using the internet. The data includes the daily time spent online on any device. Social media usage In recent years, social media has become integral to internet users' daily lives, with users spending an average of *** minutes daily on social media activities. In April 2024, global social network penetration reached **** percent, highlighting its widespread adoption. Among the various platforms, YouTube stands out, with over *** billion monthly active users, making it one of the most popular social media platforms. YouTube’s global popularity In 2023, the keyword "YouTube" ranked among the most popular search queries on Google, highlighting the platform's immense popularity. YouTube generated most of its traffic through mobile devices, with about 98 billion visits. This popularity was particularly evident in the United Arab Emirates, where YouTube penetration reached approximately **** percent, the highest in the world.

Description

This dataset provides daily weather information for capital cities around the world. Unlike forecast data, this dataset offers a comprehensive set of features that reflect the current weather conditions around the world.
Starting from August 29, 2023.
It provides over 40+ features , including temperature, wind, pressure, precipitation, humidity, visibility, air quality measurements and more. The dataset is valuable for analyzing Global weather patterns, exploring climate trends, and understanding the relationships between different weather parameters.

Key Features

• country: Country of the weather data
• location_name: Name of the location (city)
• latitude: Latitude coordinate of the location
• longitude: Longitude coordinate of the location
• timezone: Timezone of the location
• last_updated_epoch: Unix timestamp of the last data update
• last_updated: Local time of the last data update
• temperature_celsius: Temperature in degrees Celsius
• temperature_fahrenheit: Temperature in degrees Fahrenheit
• condition_text: Weather condition description
• wind_mph: Wind speed in miles per hour
• wind_kph: Wind speed in kilometers per hour
• wind_degree: Wind direction in degrees
• wind_direction: Wind direction as a 16-point compass
• pressure_mb: Pressure in millibars
• pressure_in: Pressure in inches
• precip_mm: Precipitation amount in millimeters
• precip_in: Precipitation amount in inches
• humidity: Humidity as a percentage
• cloud: Cloud cover as a percentage
• feels_like_celsius: Feels-like temperature in Celsius
• feels_like_fahrenheit: Feels-like temperature in Fahrenheit
• visibility_km: Visibility in kilometers
• visibility_miles: Visibility in miles
• uv_index: UV Index
• gust_mph: Wind gust in miles per hour
• gust_kph: Wind gust in kilometers per hour
• air_quality_Carbon_Monoxide: Air quality measurement: Carbon Monoxide
• air_quality_Ozone: Air quality measurement: Ozone
• air_quality_Nitrogen_dioxide: Air quality measurement: Nitrogen Dioxide
• air_quality_Sulphur_dioxide: Air quality measurement: Sulphur Dioxide
• air_quality_PM2.5: Air quality measurement: PM2.5
• air_quality_PM10: Air quality measurement: PM10
• air_quality_us-epa-index: Air quality measurement: US EPA Index
• air_quality_gb-defra-index: Air quality measurement: GB DEFRA Index
• sunrise: Local time of sunrise
• sunset: Local time of sunset
• moonrise: Local time of moonrise
• moonset: Local time of moonset
• moon_phase: Current moon phase
• moon_illumination: Moon illumination percentage

Potential Use Cases

• Climate Analysis: Study long-term climate patterns and variations in different regions.
• Weather Prediction: Build models for weather forecasting based on historical data.
• Environmental Impact: Analyze air quality and its correlation with various weather parameters.
• Tourism Planning: Use weather data to help travelers plan their trips more effectively.
• Geographical Patterns: Explore how weather conditions differ across countries and continents.

If you find this dataset useful, please consider giving it an upvote! 🙂❤️

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Autonomous Data Platform Market Outlook

In 2023, the global autonomous data platform market size was valued at approximately USD 2.5 billion, and it is forecasted to reach USD 10.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.5% during this period. The growth of this market is primarily driven by the surge in demand for advanced data analytics and the increasing need for data-driven decision-making processes across various sectors. The widespread adoption of artificial intelligence (AI) and machine learning (ML) technologies to automate data management tasks is a significant growth factor, enabling businesses to harness data more efficiently and effectively.

One of the critical growth factors of the autonomous data platform market is the exponential increase in data generation and the complexity associated with data management. Organizations are overwhelmed with the amount of structured and unstructured data generated every day, which necessitates a robust platform that can autonomously manage, integrate, and analyze data without human intervention. The ability of autonomous data platforms to reduce operational costs by automating repetitive data management tasks, such as data cleaning, data preparation, and data integration, makes them highly appealing to enterprises seeking cost-effective solutions. Furthermore, these platforms enable businesses to derive actionable insights more rapidly, allowing for quicker response to market changes and improved decision-making capabilities.

Another significant growth driver is the increasing reliance on hybrid and multi-cloud environments. As organizations transition towards digital transformation, the use of cloud-based solutions is becoming more prevalent. Autonomous data platforms offer seamless integration with existing cloud infrastructures, providing flexibility and scalability while ensuring data security and compliance. The cloud-based deployment mode of these platforms supports remote data access, offering businesses the agility to operate across geographically dispersed locations. Moreover, the integration of AI and ML capabilities into autonomous data platforms enhances predictive analytics, allowing organizations to anticipate trends and make informed business decisions.

The growing need for enhanced data governance and regulatory compliance is also propelling the adoption of autonomous data platforms. As data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) become more stringent, organizations must ensure that their data management practices comply with these regulations. Autonomous data platforms provide robust data governance frameworks, enabling enterprises to maintain compliance while minimizing the risk of data breaches and ensuring data quality. This capability is especially critical for industries such as banking, financial services, and healthcare, where data integrity and security are paramount.

Regionally, North America holds the largest share of the autonomous data platform market, driven by the high concentration of technology companies and the rapid adoption of advanced analytics solutions. The presence of major market players and a strong focus on research and development are also contributing to the market's growth in this region. Moreover, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, attributed to the increasing digitalization efforts and the growing adoption of cloud-based solutions in emerging economies like China and India. In Europe, the market is driven by the emphasis on data privacy and stringent regulatory frameworks, encouraging organizations to adopt autonomous data platforms to ensure compliance and data protection.

Component Analysis

The components of the autonomous data platform market are primarily segmented into platforms and services. The platform segment is the backbone of the entire market, providing the essential infrastructure for data management and analytics. Autonomous data platforms incorporate AI and ML algorithms to automate various data tasks, such as integration, preparation, and analysis. The ability to self-optimize and self-heal makes these platforms indispensable for organizations dealing with large volumes of data. The platform's role is to streamline data processes, reduce human intervention, and thereby lower operational costs. Organizations favor platforms that offer seamless integration with existing systems and provide scalability to handle dynamic data needs. As more companies aim to become data-driven, the demand for comprehensive platforms that c

Dataset contains basic descriptive data on territorial elements and units of territorial registration, in which at least one attribute in selected day have changed. Dataset contains no spatial location (polygons, definition lines and centroids of RÚIAN elements). The file contains following elements (in case they have changed): state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence (ORP), authorized municipal office (POU), regions (old ones – defined in 1960), county, municipality, municipality part, town district (MOMC), Prague city district (MOP), town district of Prague (SOP), cadastral units and basic urban units (ZSJ), streets, building objects and address points. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Data is created every day (in case any change occurred) in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

Facebook is fast approaching 3 billion monthly active users. That’s about 36% of the world’s entire population that log in and use Facebook at least once a month.

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.