Visual Analytics -shanzay -Journal

  I made a histogram with a normal curve on top of it using the built-in mtcars dataset for this project to run a deviation analysis. The histogram shows how the Miles Per Gallon (MPG) values for 32 cars are spread out. The blue normal curve shows the expected "bell-shaped" pattern that is typical of a normal distribution. This picture showed that the actual data didn't differ much from the expected curve. Most of the cars were between 15 and 20 MPG, while a few high-efficiency models made a little right skew. This image doesn't compare categories, but it does show how different data points are from the mean and how they differ from the mean. The design follows the ideas put out by Few (Chapter 9) and Yau (Chapter 7) by making sure that it is simple, has clear labels, and has a limited color palette to provide significance. The main issue I had was getting the normal curve to fit the histogram's frequency correctly. However, after I fixed it, the present...

  I used a dataset that is split into groups (T, S, R, Q, and Other) for this lesson so I could see how each part fits in with the whole. It was easy for me to choose because the numbers make it clear how the different groups are different. There are three different stories in the donut charts: The total numbers are shown in X.1. "Other" is much bigger than the others. In terms of rank, the average position shows that "Other" is way ahead of the others, with the others being close behind. Time shows how long each group takes, and we can see that one group takes most of the time. Visuals that show the relationship between parts and wholes are a quick way to show who has the biggest share. They look good on a blog and are easy to read. But it's harder to see the little changes between the slices, so a bar chart would be better some of the time. In general, I like how this chart makes the imbalance clear while also letting me know the boundaries when I...

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...

            https://www.ncei.noaa.gov A person made this graphic. The NOAA/NCEI hazard maps indicate where earthquakes, volcanic eruptions, and tsunamis have started throughout the years. Examples of meticulous cartographic design choices include accurate map projections, clear legends, symbols that are in proportion to each other, and color schemes that show the difference between depth and magnitude. The IOC, UNESCO, and NOAA emblems are further signs of reliable science and human control over the situation. AI-generated graphics frequently include problems like legends that don't line up, language that doesn't make sense, or terrain that isn't straight. None of these problems happen here. Instead, the maps are professional, consistent, and made with the goal of making it easier to send dangers. The maps are helpful and full of information. They provide you easy access to information on threats all across the globe. They are good for both studying an...