Week 20 of #MakeoverMonday looked at a very sad topic - rhino poachng in South Africa. Here’s the original viz: What works well: Simple but effective colours choices Clear title The use of rhino imagery helps make it immediately clear what the chart is about What could be improved: I didn’t like the use of a unit chart as it didn’t quite make sense in terms of numbers of poached rhinos per year The use of rhino icons seemed a but too ‘fun’ for such a serious topic What I focused on: I wanted to simplify the orginal chart I wanted to annotate some of the key facts as well The interactive version can be found here

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Week 19 of #MakeoverMonday looked at the most efficient batters in Major League Baseball (MLB). Here’s the original viz: What works well: Simple title that says what it is about Nice colour choices There’s a lot of detail included, but without cluttering the viz What could be improved: I don’t follow baseball, I was a bit lost when trying to understand this I was confused by the highlighted stats.

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The focus of this month’s #SWDChallenge was on artisanal data. I found this challenge to be incredibly enjoyable. Why? Because I got to design a visualisation using data that I knew inside and out (since it was about my work history after all!). But hey, that was kinda the point, right? I want to take a step back here though. I want to address why I used data about my work history.

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Week 18 of #MakeoverMonday looked at spacewalks at the International Space Station. Here’s the original viz: What works well: The heading is a succinct summary of what the viz is showing Using the BANS (Big-ass numbers) for USA and Russia as a legend is a good idea The labels on the bars make the chart easier to read The colours are a good choice as they are very close to the NASA palette What could be improved: The years on the x-axis are on an angle, making them harder to read There are numerous images on the viz, making it very busy What I focused on: Changing the bar chart to a line chart Changing the orientation of the dates on the x-axis to be horizontal Adding the BANs into the description The interactive version can be found here

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Week 17 of #MakeoverMonday looked at Stephen Curry’s ranking of stadium popcorn. Here’s the original viz: What works well: A heat chart is perfect for this data. The labels and colours work well There’s a clear title The scoring is simple What could be improved: The colours aren’t great What I focused on: Although I do like the original chart, I decided to try something different.

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Week 16 of #MakeoverMonday was a collaboration with RJ Andrews, and looked at the most common words in his book Info We Trust. What works well: The most common word, data, stands out well People are used to seeing and interpreting word clouds What could be improved: Lack of a title or description Having words on different angles makes them hard to read Too many colours that don’t seem to serve a purpose What I focused on: I decided to show the top ten words per section in the book, which highlighted how common the word ‘data’ is.

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Week 15 of #MakeoverMonday looked at ranking US states by fiscal condition. Here’s the original visualisation: What works well: Tidy design Good choice of colours Clear question in the title The top/bottom states are clearly stated What could be improved: It isn’t clear if things are improving or getting worse. Is this any different to the previous year? More information could be added in the form of text to help explain what is being shown here What I focused on: I struggled with this one.

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Jared Braggins

Data Visualisation and Analysis