Module 3: Refined Map with Color

 


 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 downtown cluster look like one area. A shared region, with the legend in a rounded box, and the quiet base under bright points, clarifies importance. The path guides the eye toward the main cluster.

Comments

Post a Comment

Popular posts from this blog

Module # 6 Visual Differences & Deviation Analysis via R programming

Image
  For this exercise, I created a bar chart in R using the supplied mtcars dataset. The chart compared average miles per gallon for cars with different cylinder numbers (4, 6, and 8). I plotted the differences with barplot () function, along with color coding & axis labels. Visualization showed clear differences between groups - cars with 4 cylinders got much better fuel efficiency than 6 or 8 cylinders. This allowed me to quickly see how engine size relates to MPG which is not so obvious from the raw data table. The chart was not meant to represent deviation from a benchmark but implied that it showed how 8-cylinder cars lag behind the overall average MPG, which is a negative deviation from the norm. Regarding Stephen Few's ideas from Chapter 9 in Now You See It, my chart is in line with his ideas about deviation and comparison analysis. Few points out that effective visuals should show patterns, differences and exceptions without being overly decorated. The simplicity of a bar...
Read more

Final Project

Image
  Problem Description: I wanted to explore a dataset that was rich enough to support several different visual analysis types yet simple enough to interpret. And so I selected the diamonds dataset built into R, which has over 53,000 observations and ten variables describing carat weight / cut / color / clarity / price. My research question in general was: How do physical and quality attributes of a diamond affect its price - and what patterns emerge when they are considered individually and together? From that question I sliced the analysis into several connected ideas. I wanted to know how the distribution of diamond prices and carat size varies across cut categories. I also wondered which cut categories command higher prices and if there were significant deviations from the average price across the dataset. Last but not least, I wanted to test whether price was strongly associated with carat weight and how multiple attributes like cut and color affect average price. Such small que...
Read more

Tampa Metro Area Unemployment Rate

Image
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.
Read more