Visual Analytics Journal

Why I Chose This Dataset For this assignment I worked with the provided dataset containing Average Position over Time . I chose it because it’s simple but perfect for demonstrating two design frameworks we studied,  Ranking and Part-to-Whole . The data tracks performance over time, and I wanted to see not just which periods performed best , but also how the results break down overall .   The Story My Visualizations Tell Ranking Chart (Bar Chart) The ranking chart sorts each time period by  Average Position (with lower being better). This makes it easy to see which times had the strongest performance and how each period compares to the others. The rank labels on each bar highlight the exact order. From this chart, it’s immediately clear that certain periods consistently ranked at the top, while others lagged behind. Part-to-Whole Chart (Donut Chart) The donut chart divides all observations into four quartile groups (Q1–Q4). This view shows the distribution of perf...

  Why I chose these 6 variables I used two service metrics,  Vehicle Revenue Miles and Vehicle Revenue Hours , to capture how much service agencies are actually running each month. I paired them with four safety/outcome metrics— Total Events , Total Security Events , Passenger Injuries , and Total Injuries —so I can see how operational levels relate to incidents and injuries over time. Together, these six variables balance operations and outcomes and make it possible to spot relationships and changes across months and years. What the visualization reveals Clear 2020 disruption: Both service metrics show a sharp decline around 2020, followed by a gradual recovery in the years after. Safety tracks service volume: When service decreases, event and injury counts generally dip; as service returns, those counts rise again—though the timing and magnitude vary. Different recovery patterns by agency/mode: Using the Agency and Mode filters shows that some systems bounce ba...

    For this assignment, I refined the Florida COVID-19 Cases by ZIP Code map I created in Module 2 by bringing it into Adobe Illustrator. The dataset, originally from the Florida Department of Health and archived by the USF Libraries Digital Heritage and Humanities Collection, shows case counts as of April 28, 2021. After exporting the map from Tableau, I vectorized it in Illustrator so I could edit individual ZIP shapes. I then applied a 6-class sequential blue color scale to better show variation in the data. The ranges I used were: 0–499 500–999 1,000–1,999 2,000–2,999 3,000–3,999 4,000–5,000+ This made the map much clearer than the default gradient, which was skewed by extreme outliers (ZIPs with up to 16,000 cases). By capping the scale at 5,000, I reduced the influence of those outliers and allowed the mid-range ZIPs (1,000–2,000 cases) to show meaningful differences. To improve readability, I added a legend , a title with the date , and labels for majo...