Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly
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Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.
As of the last quarter of 2023, ***** percent of web traffic in the United States originated from mobile devices, down from ***** percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.
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.
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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https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy
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.
https://www.semrush.com/company/legal/terms-of-service/https://www.semrush.com/company/legal/terms-of-service/
google.com is ranked #1 in US with 96.55B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!
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This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.
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youtube.com is ranked #1 in KR with 42.83B Traffic. Categories: Newspapers, Online Services. Learn more about website traffic, market share, and more!
https://webtechsurvey.com/termshttps://webtechsurvey.com/terms
A complete list of live websites using the Traffic technology, compiled through global website indexing conducted by WebTechSurvey.
https://webtechsurvey.com/termshttps://webtechsurvey.com/terms
A complete list of live websites using the Visitors Traffic Real Time Statistics technology, compiled through global website indexing conducted by WebTechSurvey.
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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.
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amazon.com is ranked #5 in US with 2.56B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!
According to a study focusing on travel and hospitality websites worldwide, mobile users accounted for most online visitors to such web pages in 2024. That year, mobiles generated **** percent of all the online traffic in the travel and hospitality market. That said, in 2024, the average conversion rate of travel and hospitality websites was higher among desktop users.
https://www.semrush.com/company/legal/terms-of-service/https://www.semrush.com/company/legal/terms-of-service/
faire.com is ranked #3625 in US with 4.21M Traffic. Categories: Retail. Learn more about website traffic, market share, and more!
https://www.semrush.com/company/legal/terms-of-service/https://www.semrush.com/company/legal/terms-of-service/
reddit.com is ranked #3 in US with 4.35B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!
https://www.semrush.com/company/legal/terms-of-service/https://www.semrush.com/company/legal/terms-of-service/
perplexity.ai is ranked #421 in US with 141.32M Traffic. Categories: AI. Learn more about website traffic, market share, and more!
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openai.com is ranked #60 in US with 1.12B Traffic. Categories: AI, Computer Software and Development, Wellness. Learn more about website traffic, market share, and more!
Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly