While looking for data for an individual project I came across this interesting project done by the students of UC Berkeley. I found this project quite interesting as we all know that certain group of American have different access to different set of resources but we don’t know how much is the inequality. The visualizations mentioned in the blog shows areas of unequal access and which gender and ethnicity are affected more. The data used in graphs is grouped by race, black/white and by sex, male/female.
Let me explain how the Radar/Spider chart works. Charts mentioned below demonstrate the degree to which one group is suffering in relation to the others. Larger the score in the circular chart, more is the suffering. Score 1 on the chart represents that the group has the largest barrier to success in this category, while a score of zero means that the group is least likely to suffer. For example, in the below graph, black men the as highest number of barriers to realize the American dream as the size of the polygon is large and “Bigger the polygon, more are the barriers”. Life expectancy and College Degree values are 1 which means they have a very low life expectancy, least likelihood of earning a college degree while suicide is around 0.32 which means they are less likely to commit suicide.
Things I like about visualization:
- Use of Radar Chart to show functionality: Radar Chart is a good way of comparing multiple quantitative variables, which makes it useful for seeing which variables have similar values or if there are any outliers amongst each variable. Radar chart provides functionality to the visualization and shows how different people suffering in different categories.
- Use of multiple variables making the graphs truthful: In this visualization, the author had considered multiple variables to support the claim that what are the barriers to the different people and how they are suffering in all the given category.
- Use of unique colors resulting in beautiful visualization: Use of unique colors which is not making them difficult to identify when we stacked them on top of each other in the combined graph and making visualization beautiful.
- Insightful and Enlightening: Based on the information given in combined visualization which is the degree to which one group is suffering compare to other groups and how much is the suffering in each category, we can take effective steps to prevent the suffering and minimize the barrier to the American dream.
Things I didn’t like about the Visualization:
- Comparison of positive and negative dimension in the same fashion/manner: In the radar map, many dimensions are getting displayed, for example, poverty and life expectancy. But poverty is a negative factor while life expectancy is a positive factor. If we compare both the factor on same radar and both has same value for e.g. 1, it interprets like it has very good life expectancy and high poverty but in facts, it shows very low life expectancy and high poverty.
- Misleading information: The displayed score is on the scale of 0 to 1, 1 means that the group has the largest barrier to success in this category, for example for White man, based on the graph we can say overdose and suicide are equal barriers. But is it true?
How the visualization can be improved:
- A broad range of values: Instead of showing 0 to 1 values in radar chart, we should show actual value or scaled value in range 1 to 100 to Cleary identify the difference in the value in each category.
- Additional chart to support the claim: Radar chart is not so good for comparing values across each variable so along with radar chart we should show bar graph which provides the comparison among each category.
Supporting chart for better insights - Renaming of the confusing categories: Renaming of some of the categories which are difficult to identify as the positive or negative effect on the barrier. For example, life expectancy can be rewritten as poor life expectancy.
Conclusion: Radar Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for comparing performance across multiple dimensions. But we should limit the number of polygon and variables in radar chart as having multiple polygons in one Radar Chart makes it hard to read, confusing and too cluttered. Many variables create many axes and also make the chart hard to read and complicated. So, it’s good practice to keep Radar Charts simple and limit the number of variables used and provide additional graphs along with radar chart.
References:
- Blog Reference: https://ikesmith.github.io/Priv_Git_Smith/index.html
- Good example of radar chart: https://www.tableau.com/about/blog/2015/7/use-radar-charts-compare-dimensions-over-several-metrics-41592
- Effective use of radar chart: http://www.msktc.org/lib/docs/KT_Toolkit/Charts_and_Graphs/Charts_and_Graphics_Radar_508c.pdf