Saeed’s Data Visualization Blog

  I used the Module 5 dataset with two variables: Position and Time Average. Using this dataset I showed Ranking and Part-to-Whole visualizations and Average Position over time at simple intervals. I made a bar chart for my Ranking visualization that orders time intervals highest to lowest Average Position. So it is easy to compare values directly - we know which time intervals had the most significant positions. Sorting the bars in descending order shows which points dominate the dataset and which trend is overall. For Part-to-Whole visualization, I made a pie chart of how each time interval contributes to total Average Position. Smaller slices were assigned to an "Other" category. So we can easily find out how each segment breaks the data up against the total number of segments. The Ranking chart helped me see which intervals performed best and which groups contributed most. Together they give a very detailed and nebulous picture of the dataset. In terms of strengths of the...

  Link to My Time Series Visualization:  https://public.tableau.com/shared/9XBX6FSYC?:display_count=n&:origin=viz_share_link I selected six variables; they give a balanced view of transit performance. The variables combine ridership trends, operational activity along with safety metrics. From the visualization, I noticed that ridership and vehicle operations remained steady for several years, but there was a decline after 2018. The decline could indicate service reductions or changing travel patterns. This drop shows a significant shift.

   In this assignment i enhanced my previous map with a colored legend and a pirate-styled theme to show historical sites of Tampa. Color Choices: The map uses separate colors for each site, which are safe for persons who cannot see all colors. Point sizes stay the same, so color shows the meaning. The legend shows those colors for a simpler understanding. A quiet map with markers that are 70 - 80 % opaque increases the difference, so the eye moves to landmarks and the downtown area looks clear at once. Vector Elements: Over each site, I put circle points that look the same. A simple legend panel matches those colors to names for quick finding. I also put a compass, a light path with an “X”, a little ship along with a clear title. Each addition supports the theme without hiding the data. Gestalt Principles: Because each point has the same shape and size, and the map and legend use the same color, viewers readily group sites. The closeness of points makes the dow...

  Link to dataset: https://hub.arcgis.com/datasets/tampa::historic-landmark-national/explore?location=27.917374%2C-82.415147%2C11.61 Finding the dataset was very straightforward and simple however i faced some challenges using Tableau as it was my first time using it. I had to dabble around the app and figure out how to adjust size, change map, colors, etc.  About point size, I vary it to show recency or importance. A large point means new or notable. I also use a color-blind - safe palette for type. The palette has about 60 - 70 % opacity, so overlaps are clear. Text labels or tooltips : I tried to keep the labels to a few important sites. Everything else provides details on hover. A tooltip would show the name, address along with listed date or decade. The format is clean and consistent. Proximity or similarity : When points come together, I call out those areas with a short note or a small boundary. This highlights the pattern. Using colors that are the same for the sa...

Source Url: https://fred.stlouisfed.org/series/TAMP312UR The visualization, from FRED (Federal Reserve Bank of St. Louis), shows the unemployment rate for the Tampa - St. Petersburg - Clearwater metro area. A human designer created it, not AI. The chart follows a very standardized template for FRED visualizations which includes consistent format, clear axis labels, neutral color choices, and proper sourcing from the Bureau of Labor Statistics. All these elements tells us that it was designed by a human, which makes it easier to read. AI usually produces extra styles or random designs to make it flashy which is not the case in this graph.