Makeover Monday: U.S. National Debt

This week’s #MakeoverMonday features a really simple dataset: the size of the U.S. national debt ($19.5 trillion) compared to that of the rest of the world combined ($39 trillion).

I aimed to maintain simplicity in my visualization, representing the debt in blocks of $500 billion:

Makeover Monday: Clinton vs. Trump U.S. Election Viz

This week’s #MakeoverMonday viz takes a look at polling results in each state leading up to the U.S. Presidential election. The original viz is this interactive “Votamatic” tool at Daily Kos.

Daily Kos polling results tracker

This is honestly an awesome visualization. The filter allows you to view the results for each state individually. And the interactive chart lets you hover and view the polling numbers at any point during the race.

So, I decided to look at the data in a slightly different way.

Here’s my Tableau #ElectionViz:

Makeover Monday: Public Transit Satisfaction Survey

Here’s my first attempt at a Makeover Monday viz.

This week’s featured graphic is from the Financial Times. It displays the results of a public transit satisfaction survey conducted across Europe. Respondents were asked to rate their satisfaction with public transit in their city.

Satisfaction with public transport - Financial Times

So how can we improve this viz? Let’s find out.

Here’s my version:

What improvements did I make?

  • Taking a hint from Zen Master Andy Kriebel, I centred the bars around a central axis, showing positive sentiment to the right and negative to the left. This makes is easier to judge the overall response.
  • I used an orange-green colour palette, with positive responses in green and negative responses in orange.
  • Instead of a traditional colour legend, I used colour-coded text labels along the top of the chart. I also aligned the city labels immediately beside the data bars. And I added data labels on the bars, instead of using a horizonal axis. The intent of these changes is to make the chart easier to read by more directly labeling the data.