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

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

Basic Info:

The Dataset represents the County Health Ranking of all states taking into account the various factors The County Health Rankings can be used to highlight regional variations in health, increase public understanding of the various factors that affect health, and inspire actions to improve community health. The Rankings capitalizes on our innate desire to compete by enabling comparisons across adjacent or comparable counties within states.

Dataset Information:

The CSV file contains the rankings and data details for the measures used in the 2022/23 County Health Rankings.
1) Outcomes and Factors Rankings --Ranks are all calculated and reported WITHIN states
2)**Outcomes and Factors SubRankings** --Ranks are all calculated and reported WITHIN states
3) Ranked Measure Data --The measures themselves are listed in bold.
4) Ranked Measure Sources & Years
5) Additional Measure Data --These are supplemental measures reported on the Rankings web site but not used in calculating the rankings.
6) Additional Measure Sources & Years

The Data Types of all Columns are automatically set to "Object" To change it just use data.apply(pd.to_numeric)

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.

Using four metrics—**Authority Score, Referring Domains, Citation Flow, and Trust Flow**—with an equal weight of 25%, SCImago constructs an overall indicator that reflects media websites’ digital reputation. The results define their relative position in the ranking and permit a comparison of digital development and leadership.

☢️❓The entire dataset is obtained from public and open-access data of SCImago Media Rankings

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

The dataset contains relevance scores for websites recommended to different users, and comprises of 30, 000 user-website pairs. For a user i and website j, the data contains a 136-dimensional feature vector uj i, which consists of user i’s attributes corresponding to website j, such as length of stay or number of clicks on the website. Furthermore, for each user-website pair, the dataset also contains a relevance score, i.e. how relevant the website was to the user.

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.

This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

The original dataset contains missing values. They have been simply replaced by zeros.

Top 25 Daily Page Views for the main website of Los Angeles

YouTube flows

Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"

Abstract of the article:

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

Moving away from plain-text DNS communications

The dataset contains traffic collected for 96 websites located in

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

The datasets are part of the study titled "A web-based Delphi multi-criteria group decision-making framework for renewable energy project development processes." The study aims to outline and implement the web-based Delphi Multi-criteria Group Decision Making (Delphi-MGDM) Framework, which is intended to facilitate top-level group decision-making for renewable energy project development and long-term strategic direction setting. The datasets include: (1) the weights of criteria obtained from judgments of the experts, (2) the summary of criteria scores, (3) the comparison table dataset, and (4) the full report of the Visual PROMETHEE. “Criteria Weighing Dataset” is obtained from the judgment of experts using the AHP-Online System created by Klaus D. Goepel (available at https://bpmsg.com/ahp/ahp.php). On a pairwise comparison basis, we asked the experts to make their opinion on four (4) criteria and then the sixteen (16) sub-criteria in three rounds. The group weights after the third round are considered the final weights of criteria and sub-criteria. To rank RES using MCDA, we used the data from the literature and the Philippines’ DOE for all ten quantitative sub-criteria. However, there are six qualitative sub-criteria, so we asked the opinion of experts on how solar, wind, biomass, and hydropower are performing in each criterion based on their knowledge and expertise. This time, we used a self-derived questionnaire and as a summary of this process, we produced the “Scoring of Options Dataset.” We got the average, minimum and maximum values of the scores to make data for the ranking in three cases (realistic, pessimistic, and optimistic). "Comparison table" dataset is composed of comparison tables for the three cases. Table A reflects the data for realistic case in which we use the averages of the qualitative inputs from experts, the averages of quantitative data obtained in ranges, and the actual value of data not given in ranges. Table B reflects the data for the optimistic case. For qualitative data, we used the minimum value of the sub-criteria to be minimized and maximum value for sub-criteria to maximized. For quantitative data in ranges, we used the minimum value of cost sub-criteria and maximum value of benefit sub-criteria. We estimated fictitious data for some quantitative data not given in ranges. Table C reflects the data for the pessimistic case. We used the same concept with Table B, but with opposite choices. For instance, we used the maximum value of cost sub-criteria and minimum value of benefit sub-criteria for quantitative data. Finally, we used Visual PROMETHEE (available at http://www.promethee-gaia.net/vpa.html) to rank renewable energy sources. The "Visual PROMETHEE Full Report" dataset is the actual report exported from the Visual PROMETHEE application – containing a partial and complete ranking of RES.

TRIMARC (Traffic Response and Incident Management Assisting the River City) camera locations in Louisville Metro Kentucky. This feature layer was created from a TRIMARC JSON files of camera locations. This item includes description, direction, and videos links and is used in the Louisville Metro Snow Map. The cameras are used to monitor the roadways and verify incidents to assist in freeway and incident management This feature is a static extract and will be reviewed before each snow season for updates. For more information on this feature layer and it's use please contact Louisville Metro GIS or LOJIC. To learn more about TRIMARC please visit the following website http://www.trimarc.org.