Monthly Service & Safety Trends (Time Series)

 

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 back faster, and some modes (e.g., rail vs. bus) recover differently.

  • Sustained variability: Even after service resumes, monthly fluctuations in events and injuries remain, suggesting ongoing operational or seasonal effects.

Comments

Post a Comment

Popular posts from this blog

Student Loan Program Trends in the United States (2007–2025)

Image
Written Report      This project examines how federal student loan programs in the United States have changed over time, with a focus on borrowing amounts, the number of student recipients, and the growing cost burden on individual borrowers. Although student debt is often discussed as a single issue, most people are not aware that federal loans come from different programs, each with its own history and trends. The goal of this analysis is to understand which programs are expanding, which are disappearing, and whether students today are receiving more borrowed dollars per person than they did in the past.      The idea for this project was influenced by several existing visual resources, including the Federal Student Aid dashboard, DataUSA’s educational finance reports, and journalist-designed college affordability graphics. These examples revealed how powerful visual storytelling can be in making complex financial data easier to understand. However, man...
Read more

My Geographic Map of Florida COVID-19 Cases by ZIP Code

Image
     For this week’s assignment, I was instructed to find a dataset and create a visual using Tableau. I selected the Florida COVID-19 Cases by ZIP Code (April 28, 2021) dataset from the University of South Florida Libraries’ Digital Heritage and Humanities Collection, originally downloaded from the Florida Department of Health ArcGIS Data Portal. Using this data, I wanted to examine how COVID-19 cases were distributed across Florida ZIP codes during the s pring of 2021. After importing the dataset into Tableau, I generated this geographical map using a blue color scale , since it provided the clearest contrast.      On the map, ZIP codes shaded in darker blue represent higher case counts, while lighter areas represent lower counts. Unsurprisingly, South Florida ZIP codes (especially in Miami-Dade and Broward counties) had the highest reported numbers. Central Florida areas around Orlando also showed clusters of elevated case counts. What stood out most w...
Read more

Spotting Differences in R: Comparing Fuel Efficiency by Cylinder Count

Image
  For this assignment, I used RStudio and the built-in mtcars dataset to explore differences in average miles per gallon (MPG) across cars with different numbers of cylinders. Using ggplot2 , I created a simple bar chart that displays the mean MPG for 4-, 6-, and 8-cylinder cars. The chart clearly shows that 4-cylinder cars achieve the highest average fuel efficiency , followed by 6-cylinder, and then 8-cylinder vehicles with the lowest MPG. This visualization directly supports a spotting differences task, it makes the contrasts between groups immediately visible through bar height comparisons along a shared baseline. From a design perspective, I applied a minimal theme and used a clear title and axis labels to keep attention on the comparison itself. This aligns with Stephen Few’s  principle of simplifying visuals so that viewers can detect differences quickly and accurately. Likewise, Yau  emphasizes using straightforward encodings, like length along a common scale...
Read more