36 datasets found
  1. Z

    Network Traffic Analysis: Data and Code

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 12, 2024
    + more versions
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    Homan, Sophia (2024). Network Traffic Analysis: Data and Code [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11479410
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    Dataset updated
    Jun 12, 2024
    Dataset provided by
    Soni, Shreena
    Ferrell, Nathan
    Chan-Tin, Eric
    Moran, Madeline
    Honig, Joshua
    Homan, Sophia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  2. Share of global mobile website traffic 2015-2024

    • statista.com
    • ai-chatbox.pro
    Updated Jan 28, 2025
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    Statista (2025). Share of global mobile website traffic 2015-2024 [Dataset]. https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/
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    Dataset updated
    Jan 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  3. aliexpress.com Website Traffic, Ranking, Analytics [June 2025]

    • semrush.com
    Updated Jul 12, 2025
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    Semrush (2025). aliexpress.com Website Traffic, Ranking, Analytics [June 2025] [Dataset]. https://www.semrush.com/website/aliexpress.com/overview/
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    Dataset updated
    Jul 12, 2025
    Dataset authored and provided by
    Semrushhttps://fr.semrush.com/
    License

    https://www.semrush.com/company/legal/terms-of-service/https://www.semrush.com/company/legal/terms-of-service/

    Time period covered
    Jul 12, 2025
    Area covered
    Worldwide
    Variables measured
    visits, backlinks, bounceRate, pagesPerVisit, authorityScore, organicKeywords, avgVisitDuration, referringDomains, trafficByCountry, paidSearchTraffic, and 3 more
    Measurement technique
    Semrush Traffic Analytics; Click-stream data
    Description

    aliexpress.com is ranked #18 in KR with 765.79M Traffic. Categories: Retail, Online Services. Learn more about website traffic, market share, and more!

  4. Global share of human and bot web traffic 2013-2024

    • statista.com
    Updated Jul 21, 2025
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    Statista (2025). Global share of human and bot web traffic 2013-2024 [Dataset]. https://www.statista.com/statistics/1264226/human-and-bot-web-traffic-share/
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    Dataset updated
    Jul 21, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In 2024, most of the global website traffic was still generated by humans, but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 37 percent of global web traffic in the most recently measured period, representing an increase of 12 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past 2 years, indicating a surge in the sophistication of cyber threats. Simultaneously, the share of simple bad bots drastically increased over the last years, suggesting a shift in the landscape of automated threats. Meanwhile, areas like food and groceries, sports, gambling, and entertainment faced the highest amount of advanced bad bots, with more than 70 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 50 percent of the telecom and ISPs, community and society, and computing and IT segments web traffic. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and even areas targeted by bad bots themselves experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

  5. s

    Data from: Traffic Volumes

    • data.sandiego.gov
    Updated Jul 29, 2016
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    (2016). Traffic Volumes [Dataset]. https://data.sandiego.gov/datasets/traffic-volumes/
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    csv csv is tabular data. excel, google docs, libreoffice calc or any plain text editor will open files with this format. learn moreAvailable download formats
    Dataset updated
    Jul 29, 2016
    Description

    The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

  6. Leading websites worldwide 2024, by monthly visits

    • statista.com
    • ai-chatbox.pro
    • +5more
    Updated Mar 24, 2025
    + more versions
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    Statista (2025). Leading websites worldwide 2024, by monthly visits [Dataset]. https://www.statista.com/statistics/1201880/most-visited-websites-worldwide/
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    Dataset updated
    Mar 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 2024
    Area covered
    Worldwide
    Description

    In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

  7. India: leading websites 2024, by total visits

    • statista.com
    • ai-chatbox.pro
    Updated Jun 26, 2025
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    Statista (2025). India: leading websites 2024, by total visits [Dataset]. https://www.statista.com/statistics/1108779/india-websites-ranking-by-traffic/
    Explore at:
    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 2024
    Area covered
    India
    Description

    In November 2024, Google.com held the top spot in India's website rankings, averaging over **** billion monthly visits. YouTube ranked second, with traffic of **** billion visits, while social platforms Instagram.com and Facebook.com followed with *** million and *** million monthly visits each. Internet penetration In the past decade, India has witnessed a remarkable transformation in its digital landscape. This substantial expansion has resulted in extensive digital connectivity, with more than **** of India's *** billion citizens now enjoying internet access. India ranked **** on the Digital Quality of Life Index in 2023, which revealed electronic infrastructure as one of the country’s strengths. YouTube in India As of 2025, India had the world’s largest YouTube user base, figuring over *** million users. The video platform caters to the nation’s tech-savvy denizens as an educational resource and a source of entertainment. Moreover, YouTube has evolved into a dynamic space for digital marketing, especially harnessing the consumer base segment aged below 32 years.

  8. Total global visitor traffic to amazon.com 2024

    • statista.com
    Updated Feb 18, 2025
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    Statista (2025). Total global visitor traffic to amazon.com 2024 [Dataset]. https://www.statista.com/statistics/623566/web-visits-to-amazoncom/
    Explore at:
    Dataset updated
    Feb 18, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2023 - Mar 2024
    Area covered
    Worldwide
    Description

    In March 2024, Amazon.com had approximately 2.2 billion combined web visits, up from 2.1 billion visits in February. In the fourth quarter of 2024, Amazon’s net income amounted to approximately 20 billion U.S. dollars. Online retail in the United States Online retail in the United States is constantly growing. In the third quarter of 2023, e-commerce sales accounted for 15.6 percent of retail sales in the United States. During that quarter, U.S. retail e-commerce sales amounted to over 284 billion U.S. dollars. Amazon is the leading online store in the country, in terms of e-commerce net sales. Amazon.com generated around 130 billion U.S. dollars in online sales in 2022. Walmart ranked as the second-biggest online store, with revenues of 52 billion U.S. dollars. The king of Black Friday In 2023, Amazon ranked as U.S. shoppers' favorite place to go shopping during Black Friday, even surpassing in-store purchasing. Nearly six out of ten consumers chose Amazon as the number one place to go find the best Black Friday deals. Similar findings can be observed in the United Kingdom (UK), where Amazon is also ranked as the preferred Black Friday destination.

  9. d

    Traffic Volumes from SCATS Traffic Management System Jul-Dec 2020 DCC

    • datasalsa.com
    zip
    Updated Jun 19, 2025
    + more versions
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    Dublin City Council (2025). Traffic Volumes from SCATS Traffic Management System Jul-Dec 2020 DCC [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=dcc-scats-detector-volume-jul-dec-2020
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    Dublin City Council
    Time period covered
    Jun 19, 2025
    Description

    Traffic Volumes from SCATS Traffic Management System Jul-Dec 2020 DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road.

    3 resources are provided:

    SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows.

    Information provided:

    End Time: time that one hour count period finishes.

    Region: location of the detector site (e.g. North City, West City, etc).

    Site: this can be matched with the SCATS Sites file to show location

    Detector: the detectors/ sensors at each site are numbered

    Sum volume: total traffic volumes in preceding hour

    Avg volume: average traffic volumes per 5 minute interval in preceding hour

    All Dates Traffic Volumes Data

    This file contains daily totals of traffic flow at each site location.

    SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following;

    Site id – This is a unique identifier for each junction on SCATS

    Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets

    Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets

    Region – The area of the city, adjoining local authority, region that the site is located

    LAT/LONG – Coordinates

    Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends....

  10. D

    Website Accessibility Plugins Software Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 12, 2024
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    Dataintelo (2024). Website Accessibility Plugins Software Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-website-accessibility-plugins-software-market
    Explore at:
    pptx, csv, pdfAvailable download formats
    Dataset updated
    Sep 12, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Website Accessibility Plugins Software Market Outlook



    The global market size for Website Accessibility Plugins Software was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This significant growth is driven by increasing awareness and regulatory requirements for web accessibility, along with a rising emphasis on user experience across digital platforms.



    One of the prominent growth factors for the Website Accessibility Plugins Software market is the increasing regulatory landscape aimed at promoting web accessibility. Governments across various regions are introducing stringent laws and guidelines, such as the Americans with Disabilities Act (ADA) in the United States and the Web Content Accessibility Guidelines (WCAG) globally, compelling businesses to comply with accessibility standards. Non-compliance can result in legal repercussions, driving enterprises to adopt accessibility solutions proactively. This regulatory push is particularly critical for public sector organizations and large enterprises with extensive digital properties.



    Another critical driver is the growing recognition of the economic benefits of accessible websites. Accessible websites ensure that businesses do not exclude a significant portion of the population, particularly people with disabilities, thereby expanding their potential customer base. Enhanced accessibility can lead to improved user satisfaction, increased website traffic, and higher conversion rates, making it a prudent business investment. Moreover, search engines prioritize accessible websites, contributing to better search engine optimization (SEO) results and driving organic traffic.



    The advent of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also propelling the market forward. These technologies are being integrated into accessibility plugins to provide automated solutions that can identify and rectify accessibility issues in real-time. AI-powered tools can analyze vast amounts of web content and offer precise recommendations for making websites compliant with accessibility standards. This automation reduces the manual effort and expertise required, making it easier for even small and medium enterprises to adopt these solutions.



    Regionally, North America holds the largest share of the Website Accessibility Plugins Software market, driven by strong regulatory frameworks and high digital adoption rates. Europe is also a significant market, with the General Data Protection Regulation (GDPR) indirectly influencing web accessibility standards. The Asia Pacific region is anticipated to witness the highest growth rate, owing to increasing internet penetration and rising awareness about web accessibility among businesses. Governments in countries like Japan and Australia are also taking initiatives to promote accessibility, further boosting the market in this region.



    Component Analysis



    The Website Accessibility Plugins Software market can be segmented into software and services. The software segment encompasses a variety of tools and plugins designed to enhance web accessibility. These tools include automated testing tools, screen readers, and voice recognition software. The software segment is expected to dominate the market due to its critical role in identifying and fixing accessibility issues on websites. Many software solutions offer real-time monitoring and automated compliance checks, ensuring that websites adhere to the latest accessibility standards, such as WCAG.



    On the other hand, the services segment includes consultation, implementation, training, and support services. These services are crucial for organizations that lack the in-house expertise to manage web accessibility. Consultants provide valuable insights and customized solutions tailored to the unique needs of a business, ensuring effective implementation of accessibility standards. Training services are also essential as they educate staff on best practices for maintaining accessible websites, thus fostering a culture of inclusivity within the organization.



    The integration of AI and ML in software solutions is particularly noteworthy. These technologies enhance the functionality of accessibility plugins by providing automated solutions. For instance, AI can analyze website content to identify accessibility issues and suggest or implement fixes automatically. This reduces the need for manual intervention and allows for continuous monitoring and updat

  11. SEO Software Market By Application (Social Media Marketing, Email Marketing,...

    • verifiedmarketresearch.com
    Updated Nov 23, 2024
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    VERIFIED MARKET RESEARCH (2024). SEO Software Market By Application (Social Media Marketing, Email Marketing, SEO Marketing, Pay Per Click Marketing, Display Marketing, Video Marketing, Content Marketing), Deployment (On-Premises, Cloud), Enterprise Size (Small & Medium Enterprises (SMEs), Large Enterprises), & Region for 2024-2031 [Dataset]. https://www.verifiedmarketresearch.com/product/seo-software-market/
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    Dataset updated
    Nov 23, 2024
    Dataset provided by
    Verified Market Researchhttps://www.verifiedmarketresearch.com/
    Authors
    VERIFIED MARKET RESEARCH
    License

    https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

    Time period covered
    2024 - 2031
    Area covered
    Global
    Description

    SEO Software Market size was valued at USD 274.95 Million in 2024 and is projected to reach USD 790.95 Million by 2031, growing at a CAGR of 14.12% from 2024 to 2031.

    Global SEO Software Market Drivers

    Growing Importance of Online Presence: As more and more people use the internet and become more digitally literate, companies from all sectors are realizing how critical it is to have a strong online presence. SEO software helps companies become more visible on search engines, increasing brand awareness and bringing in organic traffic to their websites.

    Updates to Search Engine Algorithms: In order to provide consumers with more relevant and superior search results, search engines such as Google regularly improve their algorithms. The need for SEO software, which enables companies to modify their tactics to satisfy the most recent search engine standards and preserve or raise their search ranks, is being driven by these algorithm changes.

    Increasing Rivalry in Digital Marketing: As more companies engage in digital marketing, there is growing rivalry for online exposure and search engine results. In order to stay ahead of the competition, firms can use the tools and analytics provided by SEO software to analyze their rivals, spot possibilities, and improve their SEO tactics.

    Concentrate on material Marketing: Since relevant, high-quality material is necessary to draw in and hold the attention of readers, content marketing is an important component of SEO. In order to help organizations generate and optimize content that appeals to their target audience, SEO software frequently includes capabilities for keyword research, content optimization, and content performance tracking.

    Mobile Search Optimization: As more people browse the internet on mobile devices, businesses are placing a premium on mobile search optimization. In order to guarantee a flawless user experience and higher search ranks on mobile search results pages, SEO software provides tools and insights to optimize websites for mobile devices.

    Data-driven Decision Making: SEO software gives organizations access to insightful statistics and data that help them decide on the best SEO tactics. SEO software helps organizations to assess success, spot trends, and improve their SEO strategies for greater outcomes. It does this through keyword analysis, backlink monitoring, and performance tracking, among other features.

    Concentrate on Local SEO: Local SEO is crucial for drawing clients in certain regions for companies that serve local markets or have a physical presence. To assist businesses become more visible in local search results, SEO software frequently includes capabilities for local keyword research, citation management, and local business listing optimization.

  12. D

    Website Builder Tools Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Website Builder Tools Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-website-builder-tools-market
    Explore at:
    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Website Builder Tools Market Outlook



    The global website builder tools market size was valued at approximately USD 2.3 billion in 2023 and is projected to reach an impressive USD 6.5 billion by 2032, representing a robust CAGR of 12.5% over the forecast period. This substantial growth can be attributed to the increasing need for businesses and individuals to establish an online presence, the surge in e-commerce activities, and the ongoing digital transformation across various industries.



    One of the primary growth factors for the website builder tools market is the escalating demand for user-friendly, cost-effective, and quick solutions for website creation. The proliferation of small and medium enterprises (SMEs) seeking to enhance their digital footprint has significantly spurred the uptake of website builder tools. These tools cater to non-technical users by offering drag-and-drop functionality, pre-designed templates, and comprehensive features that simplify the web development process, thereby reducing dependency on professional developers and cutting down costs.



    Another major driver is the rapid growth in the e-commerce sector. As consumers increasingly prefer online shopping, businesses are compelled to establish an online presence to capitalize on this trend. Website builder tools enable even small businesses to create professional-looking e-commerce websites with integrated payment gateways, inventory management, and other essential features. This democratization of e-commerce capabilities has leveled the playing field, allowing smaller players to compete with larger corporations.



    The rise of mobile internet usage has also significantly influenced the website builder tools market. Given that a substantial portion of web traffic now comes from mobile devices, website builders are focusing on offering mobile-responsive templates and features. This ensures that websites built using these tools provide a seamless user experience across various devices, thereby enhancing customer engagement and retention. The convenience and flexibility offered by mobile-compatible website builders are crucial in attracting a broader user base.



    In the realm of specialized website creation, Photography Website Builders have emerged as a crucial tool for photographers looking to establish a compelling online presence. These builders offer tailored features such as high-resolution image galleries, client proofing, and portfolio management, which are essential for showcasing photographic work in the best light. With the rise of digital photography and social media platforms, photographers are increasingly turning to these builders to create visually stunning websites that can attract potential clients and collaborators. The ease of use and customization options provided by Photography Website Builders enable photographers to focus on their art while ensuring their online portfolio is both professional and aesthetically pleasing.



    From a regional perspective, North America dominated the website builder tools market in 2023, owing to the high adoption rate of digital technologies, a large number of SMEs, and a well-established e-commerce sector. However, regions such as Asia Pacific are expected to exhibit the highest growth rates during the forecast period. This can be attributed to the rapid digitalization initiatives by governments, increasing internet penetration, and the burgeoning number of startups and SMEs in countries like China and India. Europe, Latin America, and the Middle East & Africa are also projected to experience significant growth due to similar digital transformation trends.



    Type Analysis



    The website builder tools market is segmented by type into online website builders and offline website builders. The segment of online website builders is gaining considerable traction owing to its ease of access and functionality. Online builders, such as Wix, Weebly, and Squarespace, offer cloud-based platforms that users can access from any device with an internet connection. This convenience is a significant factor driving their adoption, particularly among individual users and small businesses that require affordable and accessible website creation solutions without the need for advanced technical skills.



    In contrast, offline website builders like Adobe Dreamweaver and Mobirise require software installation on a local machine. These tools provide users with more control over the design and functionality o

  13. Illinois Gateway Traffic Cameras

    • gis-idot.opendata.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Jul 25, 2018
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    Illinois Department of Transportation (2018). Illinois Gateway Traffic Cameras [Dataset]. https://gis-idot.opendata.arcgis.com/datasets/illinois-gateway-traffic-cameras
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    Dataset updated
    Jul 25, 2018
    Dataset authored and provided by
    Illinois Department of Transportationhttp://www.dot.il.gov/
    License

    Attribution-ShareAlike 2.0 (CC BY-SA 2.0)https://creativecommons.org/licenses/by-sa/2.0/
    License information was derived automatically

    Area covered
    Description

    IL Coverage of the Gateway camera snapshots. The Gateway provides camera snapshot images throughout its coverage area in the form of camera icons on its maps and images in its camera report. With a free subscription, users can also access the Gateway ftp server which contains the most up to date versions of the images available.ImgPath - this is a link to the travelmidwest.com/lmiga/showCamera.jsp popup window that allows the user to select another direction, if availableCameraLocation - a text description of where the camera is locatedCameraDirection - the direction the camera is facing (NONE, N, E, S, W, NE, NW, SE, or SW)y - latitude in decimal degreesx - longitude in decimal degreesSnapShot - public URL of camera's image file that is suitable for placement in a tag, for instanceWarningAge - "true" if the camera is more than 10 minutes old, false otherwiseTooOld - "true" if more than 30 minutes old, "false" otherwiseAgeInMinutes - integer age of camera image in minutes

  14. f

    Overview of website traffic sources.

    • plos.figshare.com
    xls
    Updated Jan 8, 2024
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    Kyobin Hwang; Surabhi Sivaratnam; Rita Azeredo; Elham Hashemi; Lindsay A. Jibb (2024). Overview of website traffic sources. [Dataset]. http://doi.org/10.1371/journal.pdig.0000181.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 8, 2024
    Dataset provided by
    PLOS Digital Health
    Authors
    Kyobin Hwang; Surabhi Sivaratnam; Rita Azeredo; Elham Hashemi; Lindsay A. Jibb
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Social media is increasingly used to engage persons with lived experience and healthcare professionals in research, however, there remains sparse guidance on how to effectively use social media to engage these groups in research agenda-setting. Here we report our process and experience utilizing a social media campaign to engage Canadians within the pediatric cancer community in a research priority-setting exercise. Following the James Lind Alliance method, we launched a priority-setting partnership (PSP) to develop a child with cancer-, survivor-, family member-, and healthcare professional-based Canadian pediatric cancer research agenda. Social media-based strategies were implemented to recruit participants for two PSP surveys, including preparatory activities, developing a website, launching graphics and advertisements, and engaging internal and external networks. Descriptive statistics of our data and analytics provided by the platforms are used presently to report our process. The framework we implemented involved preparing for social media use, identifying a target audience, developing campaign content, conducting the campaign, refining the campaign as needed, and evaluating its success. Our process resulted in a substantial social media-based reach, good survey completion rates, and a successfully developed pediatric cancer community-specified research agenda. Social media may represent a useful approach to engage persons with lived experience and healthcare professionals in research agenda development. Based on our experience, we present strategies to increase social media campaign engagement that may be useful to those seeking to conduct health research priority-setting exercises.

  15. Daily domestic transport use by mode

    • gov.uk
    Updated Jul 9, 2025
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    Department for Transport (2025). Daily domestic transport use by mode [Dataset]. https://www.gov.uk/government/statistics/transport-use-during-the-coronavirus-covid-19-pandemic
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    Dataset updated
    Jul 9, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Transport
    Description

    Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. You are welcome to contact us directly by emailing transport.statistics@dft.gov.uk with any comments about how we meet these standards.

    These statistics on transport use are published monthly.

    For each day, the Department for Transport (DfT) produces statistics on domestic transport:

    • road traffic in Great Britain
    • rail passenger journeys in Great Britain
    • Transport for London (TfL) tube and bus routes
    • bus travel in Great Britain (excluding London)

    The associated methodology notes set out information on the data sources and methodology used to generate these headline measures.

    From September 2023, these statistics include a second rail usage time series which excludes Elizabeth Line service (and other relevant services that have been replaced by the Elizabeth line) from both the travel week and its equivalent baseline week in 2019. This allows for a more meaningful like-for-like comparison of rail demand across the period because the effects of the Elizabeth Line on rail demand are removed. More information can be found in the methodology document.

    The table below provides the reference of regular statistics collections published by DfT on these topics, with their last and upcoming publication dates.

    ModePublication and linkLatest period covered and next publication
    Road trafficRoad traffic statisticsFull annual data up to December 2024 was published in June 2025.

    Quarterly data up to March 2025 was published June 2025.
    Rail usageThe Office of Rail and Road (ORR) publishes a range of statistics including passenger and freight rail performance and usage. Statistics are available at the https://dataportal.orr.gov.uk/" class="govuk-link">ORR website.

    Statistics for rail passenger numbers and crowding on weekdays in major cities in England and Wales are published by DfT.
    ORR’s latest quarterly rail usage statistics, covering January to March 2025, was published in June 2025.

    DfT’s most recent annual passenger numbers and crowding statistics for 2023 were published in September 2024.
    Bus usageBus statisticsThe most recent annual publication covered the year ending March 2024.

    The most recent quarterly publication covered January to March 2025.
    TfL tube and bus usageData on buses is covered by the section above. https://tfl.gov.uk/status-updates/busiest-times-to-travel" class="govuk-link">Station level business data is available.
    Cycling usageWalking and cycling statistics, England2023 calendar year published in August 2024.
    Cross Modal and journey by purposeNational Travel Survey2023 calendar year data published in August 2024.

  16. C

    Construction sites on Hamburg's main roads and trunk roads

    • ckan.mobidatalab.eu
    html, wfs, wms, xml +1
    Updated May 15, 2023
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    HMDKLGV (2023). Construction sites on Hamburg's main roads and trunk roads [Dataset]. https://ckan.mobidatalab.eu/cs_CZ/dataset/construction-sites-on-main-traffic-and-main-roads-hamburg1
    Explore at:
    wfs(31066), xml(240543), xsd(2939), html(1396), wms(15503), html(108065)Available download formats
    Dataset updated
    May 15, 2023
    Dataset provided by
    HMDKLGV
    License

    Data licence Germany – Attribution – Version 2.0https://www.govdata.de/dl-de/by-2-0
    License information was derived automatically

    Area covered
    Hamburg
    Description

    Construction site coordination in Hamburg Maintaining the infrastructure is of fundamental importance for the development of Hamburg. That's why construction sites on the street are part of the normal picture - to the chagrin of residents and road users. In many cases, however, it is not work on the road itself that leads to obstructions, but the many supply and disposal lines in the road structure or private construction projects. Approximately 25,000 jobs per year in the Hamburg road network, of which more than 3,700 are in the important main thoroughfares, therefore require careful coordination in order to reduce disruptions to the flow of traffic to a minimum. This is the task of the Traffic Flow Improvement department in the State Office for Roads, Bridges and Waterways. The incoming information from all road construction departments, management companies and private builders is collected and evaluated here. The information for the most important construction sites is published with a 7-day preview on the Internet at www.hamburg.de/baustellen. When coordinating construction sites, the aim is to coordinate construction sites at the same time, e.g. B. on important parallel roads, so that traffic has alternative routes available without disruption. However, no amount of coordination, no matter how good, cannot completely prevent traffic jams. The Hamburg road network is sometimes heavily used and sometimes overloaded during the morning and evening rush hour. We therefore recommend that every road user find out about the current traffic situation before setting off and only then choose a suitable means of transport and route. If you have any questions about construction sites in Hamburg, please contact the construction site hotline on 040 428 28 2020 or send a letter to the Free and Hanseatic City of Hamburg State Office for Roads, Bridges and Waters Sachsenfeld 3-5 20097 Hamburg

  17. Global online retail website visits and orders 2025, by device

    • statista.com
    Updated May 28, 2025
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    Statista (2025). Global online retail website visits and orders 2025, by device [Dataset]. https://www.statista.com/statistics/568684/e-commerce-website-visit-and-orders-by-device/
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    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Mobile phones dominate global digital commerce website visits and contribute to the largest share of online orders. As of the first quarter of 2025, smartphones constituted around ** percent of retail site traffic globally, responsible for generating ** percent of online shopping orders. Marketplace momentum Retail e-commerce has significantly increased globally over the past few years. Currently, the leading countries in retail e-commerce growth, such as the Philippines, have seen an increase of up to ** percent. In 2024, the majority of online purchases worldwide were made on online marketplaces, incurring around a ** percent share of consumer purchases. The top four retail websites for consumers to visit globally were all marketplaces, with the leading website being Amazon.com. Converting clicks When shopping online, website visits often do not end in purchases. This can be due to having second thoughts when online shopping, or simply due to consumers using the platforms to search for products. In 2025, the conversion rate of online shoppers globally was under * percent, with beauty & skincare incurring the highest conversion rate from online purchases. Across the globe, almost ** percent of all retail sales were conducted online. This figure is forecast to increase to at least ** percent by 2027.

  18. Most popular travel and tourism websites worldwide 2025

    • statista.com
    • ai-chatbox.pro
    Updated Jul 21, 2025
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    Statista (2025). Most popular travel and tourism websites worldwide 2025 [Dataset]. https://www.statista.com/statistics/1215457/most-visited-travel-and-tourism-websites-worldwide/
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    Dataset updated
    Jul 21, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 2025
    Area covered
    Worldwide
    Description

    In June 2025, booking.com was the most visited travel and tourism website worldwide. That month, Booking’s web page recorded around *** million visits. Tripadvisor.com and airbnb.com followed in the ranking, with roughly *** million and ** million visits, respectively. Popular online travel agencies in the U.S. Online travel agencies (OTAs), such as Booking.com and Expedia, offer a wide variety of services, including online hotel bookings, flight reservations, and car rentals. According to the Statista Consumer Insights Global survey, when looking at flight search engine online bookings by brand in the United States, Booking.com and Expedia were the most popular options when it came to making online flight reservations in 2025. When focusing on hotel and private accommodation online bookings in the U.S., Booking.com was again the most popular brand, followed by Airbnb, Expedia, and Hotels.com. Booking Holdings vs. Expedia Group Booking.com is one of the most popular sites of the online travel group Booking Holdings, the leading online travel agency worldwide based on revenue, that also owns brands like Priceline, Kayak, and Agoda. In 2024, Booking Holdings' revenue amounted to almost ** billion U.S. dollars, the highest figure reported by the company to date. Meanwhile, global revenue of Expedia Group, which manages brands like Expedia, Hotels.com, and Vrbo, reached nearly ** billion U.S. dollars that year.

  19. Attitudes towards the internet in Japan 2025

    • statista.com
    Updated Apr 11, 2025
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    Umair Bashir (2025). Attitudes towards the internet in Japan 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
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    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

  20. Attitudes towards the internet in Mexico 2025

    • statista.com
    Updated Apr 11, 2025
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    Umair Bashir (2025). Attitudes towards the internet in Mexico 2025 [Dataset]. https://www.statista.com/topics/1145/internet-usage-worldwide/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Umair Bashir
    Description

    When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

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Homan, Sophia (2024). Network Traffic Analysis: Data and Code [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11479410

Network Traffic Analysis: Data and Code

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Dataset updated
Jun 12, 2024
Dataset provided by
Soni, Shreena
Ferrell, Nathan
Chan-Tin, Eric
Moran, Madeline
Honig, Joshua
Homan, Sophia
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

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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