Week 17 was a collaboration with Global Footprint Network to celebrate Earth Day and bring attention to how Earth’s resources are being used around the world. Mikel Evan, Research Analyst at Global Footprint Network, has been following along all week including tuning in to Viz Review on Wednesday, and sent the following note for me to pass along.

First of all, we are very impressed by and incredibly thankful for all the work the participants in week 17 of Makeover Monday have submitted! There were so many different approaches people took with the data, it is hard to choose favorites so I created a couple of categories: overall aesthetics, interesting/novel stories, and carbon.

 

Aesthetics

Author: Daniel Caroli

Author: Craig Dewar

Interesting Stories

Carbon

Author: Simon Beaumont

Thanks for all your work. This has been great to be a part of!

 

LESSON 1: LINE CHART ASPECT RATIO

Consider this example from Anuj Mishra, which Eva and I reviewed during Viz Review, and pay particular attention to the height of the line chart vs. its width.

The chart has roughly a 5:1 aspect ratio, making the chart wide and short, thus flattening the view. When the data is presented in this way, it is more difficult than necessary to make sense of the data. If we change the chart to be tall and narrow, we get the opposite effect.

Now, the chart is so tall that the slope of the lines is over exaggerated. As a general rule, consider making line charts with a 3:2 ratio. I have adjusted the size of Anuj’s line chart to 3:2.

Now that the aspect ratio is corrected, it’s much easier to read the chart and understand the gaps between the lines.

 

LESSON 2: PACKED BUBBLES ARE INEFFECTIVE FOR TIME SERIES

A line chart is nearly always the most effective method for displaying time series data. Lines helps show the patterns by time. However, this week I saw something a haven’t seen before, a packed bubble time series.

In this view, each red circle represents a year of carbon emissions in China. The problem is there is no logical sorting to packed bubbles; an algorithm determines their placement. The only way for me to know the year each bubble represents is to hover of it, memorize the year and value, then hover over another and hope I pick the next year. I don’t know about you, but I would get lost very quickly.

If you have time series data, start with a line chart. You’ll rarely go wrong.

 

FAVORITES