Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

As of the second quarter of 2025, ***** percent of web traffic in the United States originated from mobile devices, down from over ** percent in the last quarter of 2024. In comparison, over ********** of web traffic worldwide was generated via mobile in the last examined period.

In March 2024, Google.com was the leading website in the United States. The search platform accounted for over 19 percent of desktop web traffic in the United States, ahead of second-ranked YouTube.com with 10.71 percent.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. 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.

Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

A dataset comparing features, pricing, and ratings of the top sites to buy website traffic in 2025: Google Ads, Facebook Ads, PropellerAds, and SparkTraffic.

The global website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

In November 2024, search platform Google.com was the top ranking website in Canada, with average monthly traffic of 2.71 billion visits. YouTube ranked second with 1.5 billion visits. Reddit.com ranked third, with total monthly traffic of 301 million visits.

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Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

Digital technology and Internet use, website traffic strategies, by North American Industry Classification System (NAICS) and size of enterprise for Canada from 2012 to 2013.

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.

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

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

In November 2023, WhatsApp.com was the most popular website in Colombia by time per visit, with an average session length of approximately ** minutes and ** seconds. YouTube.com ranked second, with an average of ** minutes and ** seconds per visit.

Across popular online marketplace websites among Australian shoppers in February 2025, amazon.com.au registered the highest number of site visits. Of these site visits, desktop visits were the leading traffic source and held a share of almost ** percent. ebay.com.au came in second by number of site visits, with desktop visits holding the largest share at around ** percent of its total site visits.

In July 2025, totalwine.com was the most visited alcohol e-commerce website worldwide, with an average of ****million visits. Vivino.com ranked in second place, garnering approximately **** million monthly visitors globally.

In November 2021, the most visited website in Sweden was Google.com, with total monthly traffic exceeding 107 million visits. Aftonbladet.se came in second place with 38.2 million monthly visits, followed by Expressen.se with 38.1 million total visits during the month.

The share of monthly traffic of Google.com from Facebook was about **** percent, which was the highest among other social platform referrals in the United Arab Emirates (UAE) in December 2020. The most visited websites in the country for that year were Google, Facebook, and YouTube.

Dataset containing metrics and parameters for free website traffic distribution, including Nano credit system details, eligibility criteria (6000 hits/month, domain restrictions), and manual renewal requirements.