The Friday project – week 11

This week’s lesson: use the right data!

Before I began the Friday project at the start of the year I asked Andy Kirk and Andy Cotgreave’s advice on where to start. Andy C’s suggestion was to participate in Makeover Monday and Andy K’s was to be disciplined, to think like a Data Journalist, and to add constraints.

As my Tableau skills have improved I have sought to add constraints by completing Makeover Monday within an hour on Sunday, leaving Fridays to work on thinking like a Data journalist: finding an interesting story, sourcing appropriate data, and creating a visualisation.

This week it was this article that caught my eye: Man found guilty of killing one of Britain’s rarest butterflies.

large blue

Now butterflies are something I know almost nothing about but the article triggered a few questions for me, many Large Blues are there in the UK, how have numbers changed over the years, and how do they compare to the other 25 protected species?

And here is the kicker: I couldn’t find the information I wanted. The best data I could find was these Occurrence (Distribution) and Abundance (Population) Trends for 1976-2014 and 2015-2014, for 60 species of UK butterflies, including the 25 protected ones (top 5 only shown):

Butterfly data

For the Large Blue there was insufficient data for Occurrence change for both periods, a 1,440% increase in Abundance change for 1976-2014, and a 20% decrease for 2005-2014. So why the fuss if numbers had increased so much since 1976?

Without the underlying numbers behind the increase, I had no coherent story. But instead of looking for another topic and wasting a morning, I pressed ahead and wasted the day, creating, then deleting, a dashboard which was devoid of interest. I had less to show from the day than I had from spending an hour on my Makeover Monday viz:

Joy of sex

So what were those key lessons?

  1. Add time constraints to data gathering as well as to data visualisation
  2. Be wary of the sunk cost fallacy: know when to move on
  3. Be thankful for the work done to create those Makeover Monday datasets – it is harder than it looks!

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