Analysis of ‘Top 50 Pages By Pageviews on Austintexas.gov -’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3c834400-d28d-4a2f-b8ba-fc2b6d9f2490 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This data, exported from Google Analytics displays the most popular 50 pages on Austintexas.gov based on the following: Pageviews: The total number of times the page was viewed. Repeated views of a single page are counted. Unique Pageviews: The number of visits during which the specified page was viewed at least once. A unique pageview is counted for each page URL + page Title combination. Average Time on Page: The average amount of time users spent viewing a specified page or screen, or set of pages or screens. Entrances: The number of times visitors entered your site through a specified page or set of pages. Bounce Rate: The percentage of single-page visits (i.e. visits in which the person left your site from the entrance page without interacting with the page). Percent Exit: (number of exits) / (number of pageviews) for the page or set of pages. It indicates how often users exit from that page or set of pages when they view the page(s). This demonstrates the top 50 pages for a three-month period.

--- Original source retains full ownership of the source dataset ---

Abstract (our paper) The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends. Data personal-name.txt.gz: The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014. personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz: The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view. Publication This data set was created for our study. If you make use of this data set, please cite: Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015. http://dx.doi.org/10.1145/2786451.2786495 http://arxiv.org/abs/1509.02218 (author-created version) Note The raw data of Wikipedia page views is available in the following page. http://dumps.wikimedia.org/other/pagecounts-raw/ {"references": ["Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015.", "Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Analysis for Search Trend Prediction. Proceedings of the Annual Conference of Japanese Society for Artificial Intelligence (in Japanese). vol.29, no.2I1-1, pp.1-4, 2015."]}

Abstract (our paper)

This paper investigates the page view and interlanguage link at Wikipedia for Japanese comic analysis. This paper is based on a preliminary investigation, and obtained three results, but the analysis is insufficient to use the results for a market research immediately. I am looking for research collaborators in order to conduct a more detailed analysis.

Data

Publication

This data set was created for our study. If you make use of this data set, please cite: Mitsuo Yoshida. Preliminary Investigation for Japanese Comic Analysis using Wikipedia. Proceedings of the Fifth Asian Conference on Information Systems (ACIS 2016). pp.229-230, 2016.

Analysis of ‘Top syndicated pages from CDC.gov by weekly page views’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f60a8a0a-01c4-42b1-a899-8d6b8e14ba4a on 11 February 2022.

--- Dataset description provided by original source is as follows ---

The CDC Content Syndication site at https://tools.cdc.gov/syndication/ allows you to import content from CDC websites directly into your own website or application. These services are provided free of charge from CDC. The data shown in this table represent the weekly top page views from CDC.gov offered by syndication.

--- Original source retains full ownership of the source dataset ---

Web analytics for mesaaz.gov and mesanow.org

Context

• Time Series: Time series is a set of observations recorded over regular interval of time, Time series can be beneficial in many fields like stock market prediction, weather forecasting. - Accounts for the fact that data points taken over time may have an internal structure (such as auto correlation, trend or seasonal variation) that should be accounted for.

• Web traffic: Amount of data sent and received by visitors to a website. - Sites monitor the incoming and outgoing traffic to see which parts or pages of their site are popular and if there are any apparent trends, such as one specific page being viewed mostly by people in a particular country

Content

Contains Page Views for 60k Wikipedia articles in 8 different languages taken on a daily basis for 2 years.

https://i.ibb.co/h1JCgpY/DSLC.png" alt="DSLC">

A Data Science Life Cycle can be used to create a project. Forecasting can be done for any interval provided sufficient dataset is available. Refer the Github link in the tasks to view the forecast done using ARIMA and Prophet. Further feel free to contribute. Several other models can be used including a neural network to improve the results by many folds.

Acknowledgements

Credits :
1. Wikipedia 2. Google

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

Raising public awareness of sepsis, a potentially life-threatening dysregulated host response to infection, to hasten its recognition has become a major focus of physicians, investigators, and both non-governmental and governmental agencies. While the internet is a common means by which to seek out healthcare information, little is understood about patterns and drivers of these behaviors. We sought to examine traffic to Wikipedia, a popular and publicly available online encyclopedia, to better understand how, when, and why users access information about sepsis. Utilizing pageview traffic data for all available language localizations of the sepsis and septic shock pages between July 1, 2015 and June 30, 2018, significantly outlying daily pageview totals were identified using a seasonal hybrid extreme studentized deviate approach. Consecutive outlying days were aggregated, and a qualitative analysis was undertaken of print and online news media coverage to identify potential correlates. Traffic patterns were further characterized using paired referrer to resource (i.e. clickstream) data, which were available for a temporal subset of the pageviews. Of the 20,557,055 pageviews across 65 linguistic localizations, 47 of the 1,096 total daily pageview counts were identified as upward outliers. After aggregating sequential outlying days, 25 epochs were examined. Qualitative analysis identified at least one major news media correlate for each, which were typically related to high-profile deaths from sepsis and, less commonly, awareness promotion efforts. Clickstream analysis suggests that most sepsis and septic shock Wikipedia pageviews originate from external referrals, namely search engines. Owing to its granular and publicly available traffic data, Wikipedia holds promise as a means by which to better understand global drivers of online sepsis information seeking. Further characterization of user engagement with this information may help to elucidate means by which to optimize the visibility, content, and delivery of awareness promotion efforts.

Dataset contains the total number of page views for the dates 1/1/2014 through 12/31/2016. Data obtained through Google Analytics.

This dataset is a direct export from DC government's Google Analytics report of daily page views on the https://www.dc.gov web portal. This shows daily page views, per year, on DC.gov from 2008 to March 2020. It is identified by the part of the URL after the dc.gov domain path where users have visited.

Snapshot img

Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting,

This dataset displays the number of page views each day in 2018 for mississauga.ca. This data is compiled by Google Analytics and is updated annually.

This data, exported from Google Analytics displays the most popular 50 pages on Austintexas.gov based on the following: Views: The total number of times the page was viewed. Repeated views of a single page are counted. Bounce Rate: The percentage of single-page visits (i.e. visits in which the person left your site from the entrance page without interacting with the page). *Note: On July 1, 2023, standard Universal Analytics properties will stop processing data.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including page views and unique page views.

Data dictionary: Page_Title: Title of webpage used for pages of the website www.cityofrochester.gov Pageviews: Total number of pages viewed over the course of the calendar year listed in the year column. Repeated views of a single page are counted. Unique_Pageviews: Unique Pageviews - The number of sessions during which a specified page was viewed at least once. A unique pageview is counted for each URL and page title combination. Avg_Time: Average amount of time users spent looking at a specified page or screen. Entrances: The number of times visitors entered the website through a specified page.Bounce_Rate: " A bounce is a single-page session on your site. In Google Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Google Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Google Analytics server during that session. Bounce rate is single-page sessions on a page divided by all sessions that started with that page, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Google Analytics server. These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Google Analytics calculate the length of the session. "Exit_Rate: The number of exits from a page divided by the number of pageviews for the page. This is inclusive of sessions that started on different pages, as well as “bounce” sessions that start and end on the same page. For all pageviews to the page, Exit Rate is the percentage that were the last in the session. Year: Calendar year over which the data was collected. Data reflects the counts for each metric from January 1st through December 31st.

Analysis of ‘Austin's data portal activity metrics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/data-portal-activity-metricse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Austin's open data portal provides lots of public data about the City of Austin. It also provides portal administrators with behind-the-scenes information about how the portal is used... but that data is mysterious, hard to handle in a spreadsheet, and not located all in one place.

Until now! Authorized city staff used admin credentials to grab this usage data and share it the public. The City of Austin wants to use this data to inform the development of its open data initiative and manage the open data portal more effectively.

This project contains related datasets for anyone to explore. These include site-level metrics, dataset-level metrics, and department information for context. A detailed detailed description of how the files were prepared (along with code) can be found on github here.

Example questions to answer about the data portal

1. What parts of the open data portal do people seem to value most?
2. What can we tell about who our users are?
3. How are our data publishers doing?
4. How much data is published programmatically vs manually?
5. How data is super fresh? Super stale?
6. Whatever you think we should know...

About the files

all_views_20161003.csv

There is a resource available to portal administrators called "Dataset of datasets". This is the export of that resource, and it was captured on Oct 3, 2016. It contains a summary of the assets available on the data portal. While this file contains over 1400 resources (such as views, charts, and binary files), only 363 are actual tabular datasets.

table_metrics_ytd.csv

This file contains information about the 363 tabular datasets on the portal. Activity metrics for an individual dataset can be accessed by calling Socrata's views/metrics API and passing along the dataset's unique ID, a time frame, and admin credentials. The process of obtaining the 363 identifiers, calling the API, and staging the information can be reviewed in the python notebook here.

site_metrics.csv

This file is the export of site-level stats that Socrata generates using a given time frame and grouping preference. This file contains records about site usage each month from Nov 2011 through Sept 2016. By the way, it contains 285 columns... and we don't know what many of them mean. But we are determined to find out!! For a preliminary exploration of the columns and what portal-related business processes to which they might relate, check out the notes in this python notebook here

city_departments_in_current_budget.csv

This file contains a list of all City of Austin departments according to how they're identified in the most recently approved budget documents. Could be helpful for getting to know more about who the publishers are.

crosswalk_to_budget_dept.csv

The City is in the process of standardizing how departments identify themselves on the data portal. In the meantime, here's a crosswalk from the department values observed in all_views_20161003.csv to the department names that appear in the City's budget

This dataset was created by Hailey Pate and contains around 100 samples along with Di Sync Success, Browser Firefox 19, technical information and other features such as: - Browser Firefox 33 - Di Sync Failed - and more.

How to use this dataset

• Analyze Sf Query Error User in relation to Js Page View Admin
• Study the influence of Browser Firefox 37 on Datasets Created
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Hailey Pate

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This dataset shows the number of page views each day of 2016 for data.edmonton.ca. This data is pulled from our Google Analytics and updated monthly.

This dataset contains trace data describing user interactions with the Inter-university Consortium for Political and Social Research website (ICPSR). We gathered site usage data from Google Analytics. We focused our analysis on user sessions, which are groups of interactions with resources (e.g., website pages) and events initiated by users. ICPSR tracks a subset of user interactions (i.e., other than page views) through event triggers. We analyzed sequences of interactions with resources, including the ICPSR data catalog, variable index, data citations collected in the ICPSR Bibliography of Data-related Literature, and topical information about project archives. As part of our analysis, we calculated the total number of unique sessions and page views in the study period. Data in our study period fell between September 1, 2012, and 2016. ICPSR's website was updated and relaunched in September 2012 with new search functionality, including a Social Science Variables Database (SSVD) tool. ICPSR then reorganized its website and changed its analytics collection procedures in 2016, marking this as the cutoff date for our analysis. Data are relevant for two reasons. First, updates to the ICPSR website during the study period focused only on front-end design rather than the website's search functionality. Second, the core features of the website over the period we examined (e.g., faceted and variable search, standardized metadata, the use of controlled vocabularies, and restricted data applications) are shared with other major data archives, making it likely that the trends in user behavior we report are generalizable.

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Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

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Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

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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...

This dataset displays the number of page views each day in 2016 for mississauga.ca. This data is compiled by Google Analytics and is updated annually.