Visual Analytics LIS 4317

The dataset can be seen below: Ranking Chart using an ordered bar chart: Part-to-Whole Chart using a donut chart: Why you chose this dataset. I chose this dataset because it has two straightforward but meaningful variables: Time on the x-axis and Average Position on the y-axis. This setup is applicable in many real-world contexts. For example, “Time” could represent days, months, or trials in an experiment, while “Average Position” could represent a performance ranking, a score, or some measure of quality. Many real datasets look like this: something is being tracked over time to see whether it improves or declines. Practicing with this data can assist in getting used to analyzing patterns that are very relevant in fields like business (tracking customer satisfaction), sports (tracking performance stats), or technology (tracking algorithm accuracy).  The story or insights your visualizations tell. The ordered bar chart revealed that as Time increased, the Average Position steadily ...

Because the end goal is to create a time-series, I decided to keep the variables that would show a change over time. And so I decided to keep the following variables: Year, Month, Service Area Population, Vehicle Revenue Miles, Vehicle Revenue Hours, Total Events, and Total Injuries. From these variables, the following time series visual was created.  (Please click image for better clarity) In this time series visualization of transit operations and safety from 2014–2025, several clear patterns emerge. Vehicle Revenue Miles and Hours remain steady until 2020, when they drop sharply due to COVID-19, before partially recovering in later years. Total Injuries follow a similar trend but rise again after 2022, suggesting that safety risks grew as service levels came back. Also could be due to an influx in people coming outside and using public transit. Interestingly, Total Security Events remain flat until 2022, then spike sharply, raising questions about reporting changes or new s...

 Enhancing Visualization with Color and Vector Graphics (Please select image for better quality) In this assignment, I refined my Module 2 map of Volusia County schools by applying thoughtful color, adding vector elements, and using Gestalt design principles to improve clarity and readability. One of the most important improvements I made was in the color scheme. Instead of relying on default or potentially confusing colors, I selected a color-blind–safe qualitative palette. This means that Charter schools are shown in blue, High schools in orange, Middle schools in purple, and Private schools in teal. These choices improve readability by making each school type visually distinct while avoiding the common red-green problem, which can be difficult for people with color vision deficiency. The colors also provide enough contrast against the muted basemap so that the data points remain the primary focus of the map. Because my Module 2 map was exported as a PNG, I recreated the school s...

(Please click the image for better clarity) The link to the data set: https://docs.google.com/spreadsheets/d/1JVH7sUdoyhdIds7FE16uhZyU0vv-7rbC2fZlCgr-wug/edit?usp=sharing.  This data set is about Volusia County Public (high and middle), Private and Charter School Locations. I haven’t used Tableau in many years, so getting used to the newer features and user interface was slightly challenging. Some of the menus and options were in different places than I remembered, and I had to relearn how to assign geographic roles, adjust marks, and customize legends. It also took some trial and error to figure out why my data was aggregating into a single point instead of showing all schools, and how to clean up the map so it displayed clearly. Once I re-familiarized myself with the workflow, it became easier to build and customize the visualization.  When thinking about visual grammar, I see opportunities to apply Gestalt principles to make the map more intuitive. For example, somethi...