Dataset

This dataset was created by Merve Afranur ARTAR

Contents

Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

The global Big Data Analysis Software market is experiencing robust growth, driven by the increasing volume of data generated across various sectors and the rising need for extracting actionable insights. The market size in 2025 is estimated at $50 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This significant expansion is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, the emergence of advanced analytics techniques, such as machine learning and artificial intelligence, enhances the ability to derive meaningful predictions and improve decision-making. Industry verticals like banking, manufacturing, and government are leading the adoption, leveraging big data analytics for risk management, process optimization, and improved customer service. However, challenges such as data security concerns, the need for skilled professionals, and the complexity of integrating diverse data sources are acting as restraints. The market segmentation reveals strong growth in cloud-based solutions, reflecting the shift towards flexible and readily available software infrastructure. Significant regional variations exist, with North America and Europe currently holding the largest market shares, though Asia-Pacific is projected to witness accelerated growth due to increasing digitalization and technological advancements. The competitive landscape is characterized by a mix of established players like IBM, Google, and Amazon Web Services, alongside specialized software providers such as Qlucore and Atlas.ti. These companies are continuously innovating to provide comprehensive solutions that cater to the evolving needs of businesses. The future of the Big Data Analysis Software market hinges on advancements in data visualization, enhanced integration capabilities, and the development of user-friendly interfaces. The market is likely to see further consolidation as companies strive to offer end-to-end analytics solutions, including data ingestion, processing, analysis, and visualization. The continued focus on addressing data security and privacy concerns will also play a critical role in shaping the market trajectory. The forecast suggests that by 2033, the market will surpass $150 billion, showcasing the transformative potential of big data analytics across various sectors globally.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

The Web Analytics Market in Retail and CPG is experiencing robust growth, projected to reach $1.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing need for data-driven decision-making within retail and CPG companies is paramount. Businesses are leveraging web analytics to gain deeper insights into customer behavior, optimize marketing campaigns, and personalize the shopping experience. The rise of e-commerce and omnichannel strategies further intensifies the demand for sophisticated web analytics solutions. Specifically, the ability to track customer journeys across multiple touchpoints, analyze real-time data, and measure the effectiveness of online marketing initiatives are crucial factors driving market growth. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more predictive analytics, empowering businesses to anticipate customer needs and proactively address potential challenges. Competitive pressures are also pushing companies to adopt advanced web analytics technologies to gain a competitive edge and improve operational efficiency. Segmentation reveals a strong demand across both SMEs and large enterprises, with significant application in search engine optimization (SEO), online marketing automation, customer profiling, application performance management, and social media management. Major players like Google, IBM, Meta, and Salesforce are strategically positioned to capitalize on this expanding market. The market's growth trajectory is expected to be consistent throughout the forecast period, driven by continued digital transformation within the retail and CPG sectors. While challenges such as data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive. The North American market is anticipated to hold a significant share, given the region's advanced digital infrastructure and high adoption of web analytics technologies. However, other regions, particularly Asia Pacific, are expected to show significant growth due to the rapid expansion of e-commerce and increasing internet penetration. The market's future success hinges on the continued development of innovative analytics solutions that address the specific needs of retail and CPG companies, providing actionable insights that drive revenue growth, customer loyalty, and operational efficiency. Recent developments include: April 2024 - IBM Consulting and Microsoft have unveiled the opening of the IBM-Microsoft Experience Zone in Bangalore, India. The Experience Zone is designed as an exclusive venue where clients can delve into the potential of generative AI, hybrid cloud solutions, and other advanced Microsoft offerings. The goal is to expedite their business transformations and secure a competitive edge., January 2024 - Microsoft Corp. announced a suite of generative AI and data solutions tailored for retailers. These solutions cover every touchpoint of the retail shopper journey, from crafting personalized shopping experiences and empowering store associates to harness and consolidating retail data, ultimately aiding brands in better connecting with their target audiences. Microsoft's initiatives include introducing copilot templates on Azure OpenAI Service, enhancing retailers' ability to craft personalized shopping experiences, and streamlining store operations. Microsoft Fabric hosts advanced retail data solutions, while Microsoft Dynamics 365 Customer Insights boasts new copilot features. Microsoft also rolled out the Retail Media Creative Studio within the Microsoft Retail Media Platform. These advancements collectively bolster Microsoft Cloud for Retail, providing retailers with diverse tools to integrate copilot experiences across the entire shopper journey seamlessly.. Key drivers for this market are: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Potential restraints include: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Notable trends are: Search Engine Optimization and Ranking Sector Significantly Driving the Market Growth.

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

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, integra

The Web Analytics Market Report is Segmented by Application ( Mobile Analytics, Content Marketing, Social Media Management, and More), Offering (Solutions, and Services), Deployment Model (Cloud-Based, and On-Premises), Organization Size (Large Enterprises, and Small and Medium Enterprises), End-User Vertical (Retail and E-Commerce, Manufacturing, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Information about pages on the City's website including their age and their Google Analytics data (everything from "PageViews" and to the right). If the Google Analytics fields are empty, the page hasn't been visited recently at all.

A site analytics story page discussing data freshness on the Maryland Open Data Portal with links to the State's Data Freshness Homepage.

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

The global web analytics market, valued at $5529.7 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid advancements in digital technologies and the increasing reliance on data-driven decision-making across industries, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers: the rising adoption of cloud-based analytics solutions, the increasing demand for real-time data insights, and the growing need for personalized customer experiences. Furthermore, the expansion of e-commerce and the proliferation of mobile devices are significantly contributing to the market's expansion. Emerging trends such as artificial intelligence (AI) and machine learning (ML) integration within web analytics platforms are further enhancing analytical capabilities and driving market growth. While challenges like data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive, suggesting a significant increase in market value by 2033. The competitive landscape is dynamic, with a mix of established players like Adobe, Google, and IBM alongside agile startups like Heap and Mouseflow. These companies offer a range of solutions catering to different business sizes and needs, from basic website traffic analysis to sophisticated predictive analytics. The market is witnessing a shift towards more user-friendly and visually appealing dashboards, making web analytics accessible to a broader range of users beyond dedicated data scientists. This democratization of data, coupled with ongoing technological advancements, promises to further accelerate market growth and consolidate the position of web analytics as a critical component of successful digital strategies across all sectors.

A RESTful web service for querying data and metadata components from data sets, including instruments, observatories, and inventory. This interface calls the services of the SPDF CDAWeb data browsing system. The Space Physics Data Facility (SPDF) is the archive of non-solar data for the Heliospheric Science Division (HSD) at NASA's Goddard Space Flight Center.

Considering dataset description

The user wants an improved description of the "online_shoppers_intention" dataset. I'll base the rewrite on metadata like 12,330 sessions, collected over a year, and incorporate details about the dataset’s purpose, data components, and possible uses. I could consider writing it as a cohesive paragraph or bullet points. I'll need to include citations too—let's search for further details on OpenML. Once I find that, I'll craft a clearer, more thorough description.

Thought for 5 seconds

Overview The Online Shoppers Purchasing Intention dataset captures 12,330 distinct web‐session records collected over a one‐year span from an e-commerce site, with each session belonging to a different visitor to prevent user‐ or campaign-specific biases. Originally published in 2017 and licensed under CC BY 4.0, it was curated by Sakar et al. for benchmarking classifiers on independent and identically distributed tabular data.

Features

• Numerical (10):

  • Administrative, Informational, ProductRelated (counts of pages visited) and their corresponding _Duration fields (total time in seconds spent on those pages).
  • BounceRates, ExitRates (average session‐level bounce and exit rates) and PageValues (average monetary value of pages preceding a purchase).
  • SpecialDay (normalized [0 – 1] indicator of how close the visit was to major shopping holidays, e.g. Valentine’s Day).

• Categorical (7):

  • Month (Aug – Sep), OperatingSystems (8 codes), Browser (13 codes), Region (9 codes), TrafficType (20 codes), VisitorType (“New_Visitor,” “Returning_Visitor,” “Other”), and Weekend (True/False).

Target and Class Distribution

• Revenue (False/True) denotes whether the session ended in a purchase.
• Of the 12,330 sessions, 84.5 % (10,422) did not result in revenue, while 15.5 % (1,908) did.

Intended Use This dataset is ideal for developing and comparing binary classification models—ranging from multilayer perceptrons and LSTM networks to tree-based methods—to predict online purchasing intention in a controlled, time-invariant setting.

The global Enterprise Website Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to understand their online presence and optimize their digital strategies. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of mobile devices and diverse digital channels requiring sophisticated analytics, and a growing focus on data-driven decision-making across all departments. Large enterprises are leading the adoption, leveraging these tools for detailed customer journey mapping, performance optimization, and enhanced ROI on marketing investments. However, the market faces challenges such as the complexity of integrating various analytics platforms and the need for specialized expertise to effectively interpret and utilize the vast amounts of data generated. The segment showing the fastest growth is likely cloud-based solutions due to their flexibility and accessibility. We estimate the 2025 market size to be around $15 billion, based on observable growth trends in related software markets and considering the increasing adoption of analytics solutions across various industries. A Compound Annual Growth Rate (CAGR) of 12% is projected for the forecast period (2025-2033), indicating substantial market expansion over the coming years. The competitive landscape is highly dynamic, with both established tech giants (Google, IBM) and specialized analytics providers (Adobe, SEMrush, Mixpanel) vying for market share. The ongoing trend towards mergers and acquisitions further shapes the industry. Companies are continually innovating to offer more comprehensive solutions, incorporating features like artificial intelligence (AI) for predictive analytics, real-time data visualization, and seamless integration with CRM systems. Geographic growth will vary, with North America and Europe expected to maintain significant market share due to high technological adoption rates. However, Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and economic expansion. The market's future trajectory hinges on continuous innovation within analytics capabilities, addressing the challenges of data privacy and security, and fostering greater user-friendliness within these sophisticated platforms.

The Big Data User Behavior Analysis Platform market is experiencing robust growth, driven by the increasing need for businesses to understand user interactions and optimize digital experiences. The market, estimated at $15 billion in 2025, is projected to expand significantly over the next decade, fueled by a Compound Annual Growth Rate (CAGR) of 15%. This growth is propelled by several key factors: the proliferation of digital channels, the rise of personalized marketing strategies, the increasing adoption of cloud-based analytics solutions, and the growing demand for real-time data insights. Key market segments, including e-commerce and website analysis platforms, are witnessing particularly strong growth, as businesses leverage these platforms to improve conversion rates, customer retention, and overall business performance. The competitive landscape is marked by a mix of established players like Google and Adobe, alongside specialized analytics vendors such as Mixpanel and Amplitude. These companies are continuously innovating, incorporating advanced technologies like AI and machine learning to enhance their offerings and cater to evolving business needs. The geographic distribution of the market is diverse, with North America and Europe currently holding the largest market shares. However, rapid growth is anticipated in Asia-Pacific regions like India and China, fueled by increasing internet penetration and digital adoption. While the market faces certain restraints, such as data privacy concerns and the complexity of implementing big data analytics solutions, these challenges are being mitigated by advancements in data security technologies and user-friendly analytics platforms. The ongoing trend towards real-time analytics and predictive modeling will further drive market expansion, empowering businesses to make data-driven decisions with greater speed and accuracy. The forecast period of 2025-2033 promises substantial growth opportunities for both established players and emerging startups in this dynamic sector.

Web Analytics Platform Market Outlook

According to our latest research, the global web analytics platform market size in 2024 is valued at USD 7.21 billion, with robust growth trends driven by the increasing digitalization of businesses and the need for data-driven decision-making. The market is exhibiting a promising CAGR of 15.8% from 2025 to 2033, and is expected to reach USD 26.62 billion by 2033. The primary growth factor fueling this expansion is the escalating demand for actionable insights from online user behavior, which is critical for optimizing marketing strategies and enhancing customer experiences.

One of the key growth drivers for the web analytics platform market is the exponential increase in internet penetration and digital transformation across industries. As organizations shift their operations online, the volume of data generated from web interactions has surged, necessitating advanced analytics solutions to derive meaningful insights. Businesses are leveraging web analytics to monitor website performance, track user engagement, and personalize customer journeys, which in turn boosts conversion rates and revenue generation. The proliferation of e-commerce platforms and the growing importance of omnichannel marketing have further accelerated the adoption of sophisticated analytics tools, enabling enterprises to stay competitive in an increasingly digital ecosystem.

Another significant factor propelling market growth is the integration of artificial intelligence (AI) and machine learning (ML) technologies into web analytics platforms. These advancements empower organizations to automate data collection, enhance predictive analytics, and uncover deep behavioral patterns that were previously inaccessible through traditional analytics methods. The ability to process vast datasets in real-time and generate actionable insights has transformed how businesses approach digital marketing and customer engagement. Moreover, AI-powered analytics platforms are increasingly being used for targeting and behavioral analysis, multichannel campaign optimization, and real-time decision-making, which are vital in todayÂ’s fast-paced digital landscape.

The surge in demand for personalized customer experiences and data privacy compliance is also shaping the future of the web analytics platform market. With consumers expecting tailored interactions and regulatory bodies enforcing stricter data protection laws, businesses are investing in analytics platforms that offer robust privacy features and transparency. This dual focus on personalization and compliance is driving innovation, with vendors developing solutions that provide granular insights while ensuring data security. The growing adoption of cloud-based analytics platforms, which offer scalability, flexibility, and cost-efficiency, is further amplifying market growth, especially among small and medium enterprises (SMEs) seeking to leverage enterprise-grade analytics without significant upfront investments.

From a regional perspective, North America continues to dominate the web analytics platform market, accounting for the largest market share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is attributed to the presence of major technology providers, early adoption of digital marketing strategies, and a mature e-commerce ecosystem. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing internet and smartphone penetration, and a burgeoning e-commerce sector. The Middle East & Africa and Latin America are also experiencing steady growth, fueled by rising investments in digital infrastructure and a growing emphasis on data-driven business strategies. As organizations across regions recognize the strategic value of web analytics in achieving business objectives, the market is poised for sustained expansion through 2033.

Component Analysis

The web analytics platform market is segmented by component into software and services, each playing a pivotal role in the adoption and effectiveness of analytics solutions. Software solu

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

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.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.