For week 30, we revisited a topic from 2018 week 15 - Arctic sea ice extent. During watch me viz, I focused on rebuilding:

This week's viz to makeover reminded me of a very similar (almost exactly the same) data set that we used for Makeover Monday in 2016. Here's the original viz:

Honestly, it's absolutely fantastic. It's one of the best examples of scrollytelling I've every seen. Check out the original here. 

Since I had explored this data set before, I know pretty quickly what I wanted to do. During Watch Me Viz, I went ahead through many iterations of working with time series data; they're all available in the workbook. Watch the video here (or below).

Here's my final viz. Each dot represents a month and the line represents at 10-year moving average. Each mark is compared to the 1951-1980 median. Click on the image to view the viz on Tableau Public.

I'm writing this having just finished a bike tour of Rome with my family in an absolute monsoon. Global warming is proven to cause unusual volatility in the weather, including hotter summers, extreme winter storms, and changing warm water patterns around the earth. This warming is most evident near the Arctic, where ice levels are at all time lows and the cycle of melting is accelerating year upon year.

So when I found this visualization by the National Snow & Ice Data Center, it seemed an appropriate topic for Makeover Monday. One of the most fun elements of this data set is that it includes only two columns: date and sea ice extent.

What works well?

What could be improved?

My Goals

This past week I saw this tweet from Elon Musk:

This led me to have a look at the data visualisations on NASA’s website, in particular, their viz of the global land-temperature index which reminded me a lot of all of the great work we saw for Makeover Monday week 20 - Global Warming is Spiraling Out of Control.

There’s so much to like about this visualisation. It has a great summary on the left with a massive number that is the centre piece of their story. Their intentional design of making the large number the focus make the line chart supplementary. The line chart is clear and simple, the legend is out of the way and the beacon on the end captures your attention.

The data is available right there below the viz so I downloaded it so that I could reproduce this in Tableau. I often attempt to recreate visualisations I like as a way to learn and practice. Because in the end, the only way to get better is to practice…A LOT!

I was able to reproduce everything bar the blinking dot on the end of the line. I also chose to fill in the circles on the grey line because I don’t care for the open circles. Lastly, I added a + to the beginning of the large callout number. I think that helps provide a quicker understanding of what the number means.

Click on the image to download and interact

In last week’s Makeover Monday about global warming I included a strip plot at the bottom of my final visualisation. You may hear these also called barcode charts or frequency charts, but whatever their “official” name, they are very useful for:

1. Seeing a lot of data at a glance
   
2. Understanding concentration of the data
   
3. Seasonal trends

In this week’s video tip, I walk you through five use cases for strip plots varying from global warming to the frequency of fires to distribution of sales to deprivation in Scotland.

There was a lot of chatter on Twitter last week about this terrific visualisation by Ed Hawkins:

The beauty of this visualisation is in the animation. However, without the animation, it kind of fails to tell the story. Let’s dig a bit deeper.

What works well?

• There is a clear title.
  
• The background circles provide helpful context.
• Including the month labels makes it easier to understand what you’re seeing.
• The year in the middle helps tell the story.
• The animation is compelling.
• It has a nice color scheme that works well on a black background.

What could be improved?

• While the title is clear, it could be more eye-catching, like a news headline.
  
• If you see this as a static image, you lose the sense of change.
• You can’t compare any time periods. All you know is 2016 is the warmest.
• There’s no explanation about what the numbers represent. Though I do see in the Twitter post a link to additional information.
• The color scale has nothing to do with the temperature change, which I assumed it did until I read hte additional information. The colors actually represent the years. That doesn’t add much value. I think coloring by the temperature change would be more impactful.

So, this data set is actually incredibly simple. All we have is one record per month, the temperature, and the confidence intervals.

The first thing I wanted to do was rebuild the radial chart. This wasn’t nearly as easy as I thought. This post by Jonathan Trajkovic was very helpful, but it wasn’t designed for months. I’ll record how I did made it for a future Tableau Tip Tuesday.

Click the image for the interactive version

This radial chart is basically the same as the original, however I can’t make it “play”on Tableau Public and I also changed the color to be the median temperature difference. Really, I only built this to see if I could. It’s not any more useful than the original.

Next, I took the radial chart and flattened it out.

Click the image for the interactive version

This doesn’t make the understanding all that much easier because I can’t tell which years are which. Maybe I should switch the color legend back to years?

Click the image for the interactive version

Oh wow! What a difference! Now I can easily see the distinction between the older and more recent years. I think this is much, much better than the original, especially in static format. I wanted to keep iterating though.

Whenever I’m working with time-based data, I like to build either calendar heatmaps or heatmaps by year and month. Here’s what this data set looks like as a heatmap:

Click the image for the interactive version

The heatmap makes the series of lines even easier to understand. It’s super easy to see the gradual temperature change over time. This is pretty compelling, yet I wanted to keep going. Was there a better way to tell the story?

Next I looked at the 10-year average, that is, a 120 month moving average of the median temperature change. I then overlaid the confidence intervals.

Click the image for the interactive version

Lastly, I took the 10-year moving average view and replaced the monthly confidence intervals for the monthly values while keeping the overall 10-year average. This is my submission for Makeover Monday. In this view, I like how I can see the drastic monthly fluctuations but still have the overall context. Including a reference line at zero helps emphasize the dramatic change since about 1984.

I also included a strip plot under the graph that shows the average median temperature difference for the entire year. This brings back a bit of the heatmap view above.

In the end, another fun week with a simple data set that provides lots and lots of options. Which one do you like best?

UPDATE: This week has been a fascinating exercise in iterating. That’s the beauty of Tableau. I can get another idea and build it quickly. After seeing some of the submission for this week, I thought a jitter plot might work well. Thoughts?

Click the image for the interactive version