Makeover Monday 2021 Week 11, Crops

Makeover Monday 2021 Week 11, Crops

You can see my entry here.

Lessons Learnt

● I spent far too long on this! I’ll try to take a much simpler approach next time.

● It is difficult to balance background images that cover the whole dashboard with visual clarity. Not sure of the best solution here. To be practised. Or maybe avoided.

● More to come after feedback!

Makeover Monday

Whilst I deeply love playing with mathematics and geometry in Tableau, I’ve felt a bit weak on the design front. I decided that the only solution was practice and hopefully get a little feedback. Sounds very much like Makeover Monday! This is an excellent project that has been going for years and is run by Eva Murray and Andy Kriebel.

A published data viz is presented for comment along with a data set with which you can make your own viz. This week’s viz was all about global crop production.

Feedback on original viz

  • The top graph is OK, presenting lots of data. Interesting to note that the figures are in km2, not the hectares in the original data. I wondered about doing this in my viz as it felt more meaningful to me, but decided in the end that it wasn’t important as I wanted to focus on trends.
  • The colours are pleasant and work together.
  • The vertical parts of the grid disturb me a bit as it makes it feel more like a unit graph.
  • The authors have made a decision to sort based on area planted, which is fine, but as I’m used to interactivity with Tableau, it would have been nice to offer the reader a chance to sort on the other criteria.
  • The second graph does not fit with the second. It’s doing a different job. Pick one or the other! Having said that…
  • The circles are fine as exact comparisons are not needed here. They are just making a tabloidy point. Which is also valid, depending on the audience and purpose.

The analysis

Before creating my viz, some analysis of the data is needed to see what is interesting. There’s a LOT of data here. I spotted Mark Daynes’s entry and my first thought was that quinoa production looked like it was influenced by quinoa trend over the last few years. I added some table calculations to production figures of all the crops to see if other trends popped out, but I didn’t spot anything interesting.

Then the sheer mass of production struck me and I wondered how much of our planet’s habitable space are we using for these crops? Whilst trying to find out how much habitable land we have, I came across this analysis, also based on FAO data:

This gave me a figure for the total habitable land of 104 Million km2. The total area covered in the data provided for this exercise didn’t fit with that in the Agricultural land data above. I’m sure that this is down to definitions of crops, e.g. animal feed is not included in the graph above under crops. I started going down the rabbit hole of trying to work out the reason for the differences and in the end (and with great regret) gave up to focus on the trends.

Adding further data

The thing that struck me the most was the comparison of agricultural land and forests due to our ever-decreasing natural resources, largely thanks to population growth. So that was the topic I focused on. For this I needed two further data sets, world population (from the World Bank) and deforestation (from FAO).

I started combining these using classical blending techniques, but started having problems with calculations across data sources. Then I remembered: Relationships! Dead easy, elegant:

The relationships were simply based on year. Easy peasy!

The only thing I’m not sure about it whether adding further data to the original data set is within the spirit of Makeover Monday. I hope I’ll find out during the review.

What to show?

After experimenting with various combinations of charts, I ended up with the following three main concepts:

How much is being produced and how much space does this need for each human on the planet. An obvious omision here is the massive diff

Design tools: Figma and heikai

I again used Figma for the layout, using the grid function:

I originally started with rows, but found that this was not sufficient for my needs. The great thing about Figma is that once I changed this and adjusted the blocks, I could simply read off the locations and size figures and apply them to Tableau. A much quicker process than doing the equivalent in Tableau.

As the theme was deforestation for crops, I wanted a background that transitioned from rainforest to deforestation to planted crops. I found all the images simply via google image search with the creative commons filter set. Partly as an excuse to try it out, I used the free haikei online app to create three areas split via organic lines (setting the image size to fit my dashboard and using the stacked wave in vertical format). The idea being to place each of the images into one of the three areas created via the shape. I only just about get by with graphics programs, but by using the image tracer plugin for Figma and experimenting with its mask and boolean features, I eventually managed to get the effect I was after. See the mess below:

Tip for using the mask feature: the mask needs to go behind the image you’re trying to cut a piece out of!

Design decisions

Colour. I used a simplified palette: green (because, you know, plants!), black and white. Partly for simplification and partly because I see the issue as pretty stark, i.e. black and white, even though the issues and solutions are far from simple.

Axes. I decided that detailed axes were a distraction as I wanted to focus on trends. I added tooltips and annotations to provide the actual data. The timeline was important, however, and also acted as a kind of base for the graphs to sit on.

Deforestation graph. I’m not 100% happy with this. I was undecided as to whether is should be a negative graph, going down from the axis to show loss and imply negativity or go up from the axis as the topic was deforestation. Positive deforestation = loss of forests (negative concept). I’m still not sure if I made the correct choice here. I also wanted to have the area graph filled with an image of recently cut trees instead of colour, but decided that I didn’t have the time for that. Additionally, it would make the whole visual impact too busy. In the end, I simply filled it with green to represent the forests.

Critique and feedback

From me:

Whilst it was important for me to show include the deforestation images, I feel that it makes the whole thing a bit busy and difficult to read.

From my wife:

She felt that the deforestation graph implied a positive message because (a) its green and (b) it’s going up at the end, i.e. positive

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