Visual Analytics -shanzay -Journal

I choose a few things that connect safety outcomes to the city: Passenger Injuries, Total Injuries, People Waiting or Leaving Injuries, Primary UZA Population, Primary UZA LAC Code, and Organization Type. I chose them because they enable me compare not just the number of occurrences but also the size of the population and the kind of organization that reported them. This makes it easier for me to understand the facts. My illustration shows how the number of injuries fluctuates from month to month and how they are different for different types of companies. Public organizations tend to have more injuries than corporations or institutions, which shows where safety measures are most needed. The summer is when injuries happen the most, which might be because more people utilize public transit then. More people living in cities (higher UZA values) typically equals more events, although not always in a straight line. This might suggest that the rules for reporting or safety are d...

              I chose colors that make the map easy to read and interpret. I used high contrast between text and background so labels stand out clearly without straining the eyes. To create a clear hierarchy, I applied darker shades for headings and lighter tones for supporting details. I also selected a color-blind–friendly palette to ensure accessibility for all viewers. For insights, I grouped related elements using similar color families, which helps users quickly identify patterns. I used bold or bright colors sparingly to highlight key data points, while keeping the background neutral so the focus remains on important information. These choices make the map both visually appealing and informative. I added vector elements to make the map both informative and visually engaging. Icons like a person with a shield and masked figures symbolize protection and health awareness, helping viewers connect the data to real-world impacts. Virus icons in the ...

 Module 2—My Geographic Map of COVID-19 Case Rates For this week’s assignment, I was instructed to find a dataset and create a geographic map using Tableau. I selected a COVID-19 dataset from Data World, which is based on the Johns Hopkins University Coronavirus Case Tracker. The website summarizes the data as such: “This dataset combines Johns Hopkins CSSE coronavirus data with U.S. Census population data and urban/rural designations, providing case and death rates per 100,000 people.” At first, I had some difficulty finding a dataset that included everything I needed because many sources required a subscription. After a bit of research, I found this open dataset and thought it would be interesting to work with. Since this was my first time using Tableau, I also had to learn how to connect the dataset, geocode the fields, and experiment with different ways to visualize the data. County-Level COVID-19 Cases This map displays all U.S. counties from the dataset using the  C...