Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

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Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

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.

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This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. 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).

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Cookie and Website Tracker Scanning Software Market Outlook

The global cookie and website tracker scanning software market is poised for significant growth, with its market size valued at approximately $1.5 billion in 2023 and projected to reach around $4.2 billion by 2032, reflecting a compound annual growth rate (CAGR) of approximately 12.5%. This market's expansion is largely driven by the increasing emphasis on data privacy regulations and compliance, which necessitates businesses to implement robust solutions for monitoring and managing cookies and website trackers. The growing digitalization across various sectors and the rising consumer awareness regarding data privacy are also contributing significantly to the market's upward trajectory.

One of the primary growth factors propelling the cookie and website tracker scanning software market is the proliferation of stringent data privacy regulations worldwide. Laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and other similar legislation globally mandate businesses to enhance their data protection measures. These regulations require organizations to provide transparency regarding data collection practices and ensure that users have control over their personal information. As a result, companies are increasingly adopting cookie and tracker scanning solutions to comply with these legal requirements and avoid potential penalties and reputational damage, thus driving market growth.

Another significant factor contributing to the market's expansion is the escalating awareness and concern among consumers regarding their online privacy. In an era where digital interactions are part and parcel of daily life, consumers are becoming more vigilant about how their data is collected, stored, and utilized by websites. This heightened awareness compels businesses to adopt ethical data practices and implement technologies that offer consumers clear insights into cookie usage and tracking activities. Consequently, organizations are integrating cookie and website tracker scanning software into their operations to enhance user trust and ensure transparency, thereby fostering market growth.

The rapid advancement of technology, leading to increased digitalization, is also a key driver for this market. As businesses across various industries embrace digital transformation, the online ecosystem becomes more complex with an influx of data tracking methods. This complexity necessitates the use of sophisticated tools to monitor, analyze, and manage website trackers effectively. The integration of advanced analytics and AI capabilities into scanning software enables organizations to gain deeper insights into user behavior while ensuring compliance with privacy regulations. This technological evolution is anticipated to further fuel the market's growth over the forecast period.

As the digital landscape continues to evolve, the role of a Consent Management Platform (CMP) becomes increasingly crucial in the realm of data privacy. A CMP serves as a centralized solution for managing user consent across various digital platforms, ensuring that businesses comply with data protection regulations such as GDPR and CCPA. By providing users with clear options to manage their consent preferences, these platforms enhance transparency and trust. Organizations are increasingly integrating CMPs into their operations to streamline consent management processes and reduce the risk of non-compliance. This integration not only helps in maintaining regulatory compliance but also strengthens the relationship between businesses and their users by respecting their privacy choices.

Regionally, North America holds a substantial share in the global cookie and website tracker scanning software market, owing to the early adoption of technology and stringent data privacy regulations in the region. The presence of major technology companies further fuels innovation and development in this market. Europe is also a significant market player, driven by the stringent GDPR regulations that necessitate robust compliance solutions. Meanwhile, the Asia Pacific region is expected to witness the fastest growth rate due to increasing internet penetration, digitalization initiatives, and growing awareness regarding data privacy. As economies in the region continue to develop, the demand for effective data protection solutions is likely to surge, contributing to the market's overall growth.

C

Citizen respondents rank how they want to interact with and consume government data. Survey responses are broken down along several dimensions including, Region, Education Level, Gender and Household (HH) Income.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

Business Software Alliance is a trade association that represents the world's leading software companies, including Autodesk, IBM, and Symantec. The organization's members are committed to promoting the use of legitimate software and ensuring the integrity of their intellectual property.

As a result, the data housed on BSA's website is rich in information related to the software industry, including software licensing, anti-piracy efforts, and digital piracy statistics. The data includes information on software usage, software development, and the impact of piracy on the technology industry. With its focus on promoting legitimate software use, the data on BSA's website provides valuable insights into the global software industry.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.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 internet users in countries like the Americas and Asia.

A complete list of live websites using the Data technology, compiled through global website indexing conducted by WebTechSurvey.

According to the Cognitive Market Research Report, the Data Processing and Hosting Service market size in 2024 was XX Million and is projected to have a compounded annual growth rate of XX% from 2025 to 2033. The emergence of cloud-based platforms and the growing number of small and medium enterprises are driving the market growth of Data Processing and Hosting Services. This market is further segmented by type, application, and deployment. The shared hosting under product type, public website, and public deployment holds the dominant share in the data processing and hosting service. The market is divided into shared hosting, dedicated hosting, collocated hosting, virtual private server hosting, managed hosting, self-managed hosting, and others. The shared hosting sector leads the market since small and medium-sized businesses choose shared servers over other forms of hosting. The Asia-Pacific region is the most dominant due to its high share of the global internet population and major organizations' and SMEs' quick adoption of cloud services The Data Processing and Hosting Services Market is relatively competitive, with significant companies including GoDaddy Operating Company LLC, Bluehost (Endurance International Group), HostGator.com LLC, Hostinger International, Ltd., and Amazon Web Services Inc. Some players presently have a large market share. However, as hosting solutions for professional services progress, new firms are strengthening their market presence, consequently expanding their corporate footprint into emerging markets.

Market Dynamics of Data Processing And Hosting Service

Key Drivers

The adoption of web and mobile applications drive the market growth

The boom in web and mobile apps has had a huge impact on the market of data processing and hosting services for backend infrastructure, especially in terms of data processing and data storage. As people use applications more and more for entertainment, shopping, communication, and even healthcare, the number of applications has risen astronomically. Millions of transactions and interactions are handled each day by sites like social networking websites, messaging apps, and online stores. For instance, the demand for virtual health care solutions surged, compelling data hosting providers to expand their infrastructure to support the growing data traffic at a rapid pace.

Web Hosting is gaining traction due to the emergence of cloud-based platforms.

Web hosting services are gaining pace in response to increased customer demand for web hosting services that are appropriate for their needs. Furthermore, the increased acceptance of cloud services in organizations is opening up new potential for the web hosting market over time. The rise of the cloud has had a massive impact on data management and hosting services. It is a low-cost way for businesses to make use of current technology and design without incurring the high upfront costs of acquiring, installing, and configuring the necessary hardware, software, and infrastructure. Furthermore, major firms were able to swiftly adapt to a developing data-driven economy by leveraging their current resources and competencies to manage it efficiently. Furthermore, SMBs globally are increasingly demanding cloud-based hosting services, which is likely to boost the web hosting sector throughout the projection period. The move to the cloud makes it easier to create programmes that users can use in their browsers rather than downloading on their devices. This greatly accelerates market expansion. Furthermore, with the introduction of web-based applications, app building became so simple that hosting several apps on a single server became straightforward. For instance, Hostinger International Ltd. is a well-known web hosting firm that offers hosting solutions. Hostinger is a trustworthy web hosting company. They offer fast loading speeds and excellent uptime rates to ensure that users may access the site anytime they want. Hostinger also provides knowledgeable and courteous customer service that is available around the clock. (Source: https://www.hostinger.in/about#:~:text=Hostinger%20is%20one%20of%20the,Hostinger%20and%20hustle%20with%20us) Therefore, the emergence of cloud-based platforms has expanded the data processing and hosting service market.

Growing small and medium enterprises and their requirement of increasing internet p...

We provide detailed web activity data from users browsing popular websites worldwide. This comprehensive data allows for in-depth analysis of web behavior, enabling the creation of precise audience segments based on web activity. These segments can be used to target ads effectively, focusing on users' interests and their search or browsing intent.

Web Data Reach

Our web data reach includes extensive counts across various categories, covering attributes such as country, anonymous ID, IP addresses, search queries, and more.

• Record Count: 200 Million
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Monthly
• Historic Data: Past 6 Months

Data Export Methodology

We dynamically collect and update data, providing the latest insights through the most appropriate method at intervals that best suit your needs, whether daily, weekly, or monthly.

Use Cases

Our web activity data is instrumental for personalized targeting, data enrichment, market intelligence, and enhancing fraud and cybersecurity measures, helping businesses optimize their strategies and security efforts.

This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

December 6, 2023 (Final DWR Data) The 2018 Legislation required DWR to provide or otherwise identify data regarding the unique local conditions to support the calculation of an urban water use objective (CWC 10609. (b)(2) (C)). The urban water use objective (UWUO) is an estimate of aggregate efficient water use for the previous year based on adopted water use efficiency standards and local service area characteristics for that year. UWUO is calculated as the sum of efficient indoor residential water use, efficient outdoor residential water use, efficient outdoor irrigation of landscape areas with dedicated irrigation meter for Commercial, Industrial, and Institutional (CII) water use, efficient water losses, and an estimated water use in accordance with variances, as appropriate. Details of urban water use objective calculations can be obtained from DWR’s Recommendations for Guidelines and Methodologies document (Recommendations for Guidelines and Methodologies for Calculating Urban Water Use Objective - https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/Performance-Measures/UWUO_GM_WUES-DWR-2021-01B_COMPLETE.pdf). The datasets provided in the links below enable urban retail water suppliers calculate efficient outdoor water uses (both residential and CII), agricultural variances, variances for significant uses of water for dust control for horse corals, and temporary provisions for water use for existing pools (as stated in Water Boards’ draft regulation). DWR will provide technical assistance for estimating the remaining UWUO components, as needed. Data for calculating outdoor water uses include: • Reference evapotranspiration (ETo) – ETo is evaporation plant and soil surface plus transpiration through the leaves of standardized grass surfaces over which weather stations stand. Standardization of the surfaces is required because evapotranspiration (ET) depends on combinations of several factors, making it impractical to take measurements under all sets of conditions. Plant factors, known as crop coefficients (Kc) or landscape coefficients (KL), are used to convert ETo to actual water use by specific crop/plant. The ETo data that DWR provides to urban retail water suppliers for urban water use objective calculation purposes is derived from the California Irrigation Management Information System (CIMIS) program (https://cimis.water.ca.gov/). CIMIS is a network of over 150 automated weather stations throughout the state that measure weather data that are used to estimate ETo. CIMIS also provides daily maps of ETo at 2-km grid using the Spatial CIMIS modeling approach that couples satellite data with point measurements. The ETo data provided below for each urban retail water supplier is an area weighted average value from the Spatial CIMIS ETo. • Effective precipitation (Peff) - Peff is the portion of total precipitation which becomes available for plant growth. Peff is affected by soil type, slope, land cover type, and intensity and duration of rainfall. DWR is using a soil water balance model, known as Cal-SIMETAW, to estimate daily Peff at 4-km grid and an area weighted average value is calculated at the service area level. Cal-SIMETAW is a model that was developed by UC Davis and DWR and it is widely used to quantify agricultural, and to some extent urban, water uses for the publication of DWR’s Water Plan Update. Peff from Cal-SIMETAW is capped at 25% of total precipitation to account for potential uncertainties in its estimation. Daily Peff at each grid point is aggregated to produce weighted average annual or seasonal Peff at the service area level. The total precipitation that Cal-SIMETAW uses to estimate Peff comes from the Parameter-elevation Relationships on Independent Slopes Model (PRISM), which is a climate mapping model developed by the PRISM Climate Group at Oregon State University. • Residential Landscape Area Measurement (LAM) – The 2018 Legislation required DWR to provide each urban retail water supplier with data regarding the area of residential irrigable lands in a manner that can reasonably be applied to the standards (CWC 10609.6.(b)). DWR delivered the LAM data to all retail water suppliers, and a tabular summary of selected data types will be provided here. The data summary that is provided in this file contains irrigable-irrigated (II), irrigable-not-irrigated (INI), and not irrigable (NI) irrigation status classes, as well as horse corral areas (HCL_area), agricultural areas (Ag_area), and pool areas (Pool_area) for all retail suppliers.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.