An interview with myself looking back at the recent IronViz Feeder for Health and Well-Being. Below is my final visualisation, click for the interactive version.
Perhaps I can start by asking you what you thought about this Iron Viz theme, did it get you excited – did you immediately have themes that sprang to mind?
To be honest I have a love / hate relationship with these feeders, on the one hand I love the open-endedness of the theme, yes it’s a guide but you can go almost anywhere with it, but for me it still feels too open to mean my imagination struggles to settle on one particular idea.
I was already doing some work for a Data Beats article on Parkrun and their accessibility and I initially wanted to cover this but what I had in mind didn’t fit nicely into a single visualisation or set of dashboards. I also had in mind some work on Agony Aunts – comparing the Sun’s Dear Deidre to The Guardian’s Mariella Fostrop based on the words they used – but the analysis started taking too long….
So that’s an interesting point – how do you balance out the various aspects of visualisation when choosing a subject? Do you choose subjects that require little data preparation so you can maximise data visualisation time or look for more complex subjects?
When choosing a subject I’m primarily interested in choosing a subject that interests me. If the subject do that then it isn’t going to make me want to stay up til 3am working on it, or dedicate hours outside work. Let’s face it, I’m not in IronViz to win the thing, although I’d love to, there’s just too much talent and competition out there for me to compete, and so I’d rather just have fun doing a visualisation.
That said, I also don’t want to pick a subject that feel’s too easy – I like to work at my data and perform some analysis, I want to be able to say “This is what I found” rather than “This is what the dataset I found said”. The difference is subtle but I see this as a direction my public visualisation path is taking more and more lately. So I want to build and define my own analysis and say something with it – I do take inspiration from other sources, after all very little is new or novel today, but for me the analysis is as important as the visualisation itself.
This is where too the “data journalism” aspects of data visualisation are important, in the IronViz scoring criteria this is labelled as “Storytelling”. However you label it I interpret it as not just showing the numbers. Anyone can show numbers visually, they can show the highest and the lowest and the relationship between them. They can design a dashboard and they can publish it. That isn’t data storytelling though, it’s data presentation. I want to convey why someone should be interested in the numbers, what do the numbers tell us and why is it important, and what should we do because the numbers show what they do.
So you mean adding commentary?
Well yes, but that’s only part of what I’m talking about. What I’m getting at is that this storytelling goes right back to the data choice, the subject choice and the analysis. And it’s not about presenting numbers back that people should care about either; it’s about doing some meaningful analysis and telling a story that is different, not the same old numbers presented in a different way.
It sounds like you feel the way you approach IronViz now is perhaps different to the way you’ve approached it in the past. What’s prompted it do you think?
Certainly it’s been a journey to get to this point, probably starting with my Springs got Speed visualisation in last years Iron Viz. As to what has prompted a shift towards this more analytics direction, well I suppose it’s the same things that prompted Rob and I to start Data Beats. Sometimes you look at the Iron Viz entries and you feel like you’re in a game where everyone is kicking the ball when suddenly someone comes in, picks it up and starts running with it. Over the last year or so the norms in the Tableau community certainly seem to have shifted; what was considered good a few years ago is now very, very average and people are pushing boundaries left right and centre.
When people start pushing boundaries you really are left with two choices; you can either find your own boundaries to push or settle down and try to do the basics really, really well. So while perhaps in the past I was happy to push boundaries, there are now others who do much wilder stuff than I ever could – and so I really need to hunker down and do the basics as well as I can.
So tell us about your Iron Viz. Where did the idea come from and how did you choose to approach it?
I decided to look at how deprivation is linked to the number of takeaways in an area, looking back I think like any good idea it didn’t come from any one source, instead it came from several seeds over time. Certainly walking around my own town, which is in a relatively deprived area I see a lot of takeaways, we get new takeaway leaflets every day and, where once the town centre was made up of lots of different stores now I see about twelve takeaways (contrast this to just two as I was growing up – in perhaps 500m of shops). There’s been some similar research on these links already, this article sticking in my mind recently.
Having explored other ideas and failed I knew I could get this one off the ground quickly – I’ve played with the Food Standards Agency’s Ratings data before and knew I could download that to get a classification of takeaways, while deprivation is easy calculated from the Index of Multiple Deprivation. So the problem seemed relatively simple given my limited time.
Speaking of time, how long did your visualisation take?
I didn’t have long enough, the world cup, TC Europe and several camping holidays meant that I really dedicated just the last day of the time allowed to this. I started about 3pm and, with several breaks to see to the family and eat, I was working til 3am.
I wouldn’t recommend this approach, it meant I had very little time to iterate on the visualisation, I had no time to get feedback from any peers and very little time to step back and consider what I was doing.
What took the longest time?
I settled on the data and story fairly quickly, using Alteryx to pull the data together. However the design was something that I hadn’t worked out before starting, and well over half the time was spent on trying to come up with ideas.
I started off the idea to put the article on a newspaper, partially covered by fish and chips (that’s how we eat them from takeaways traditionally in the UK); there were however several difficulties. First and foremost, I need any design to use images I have created or that are free to use. Finding an image of fish and chips at the right angle and with a transparent background was hard with no copyright was hard, also I wanted to have the article crease like it was folded which would have been quite a bit of work.
I quickly returned to the drawing board with very few ideas as to how to approach my visualisation design. I’d wasted 2 or 3 hours looking for images and I needed something quick. In desperation I googled “Takeaway” to look for related images and that’s where the takeaway menu hit me – and the idea was born.
The design looks quick complex – what software do you use?
I actually have a licence of photoshop I use for photography but I’m not a very good user, I can understand layers and some elements so I used that to piece together the design.
Wait, Photoshop? and you use Alteryx? Other people don’t have those advantages?
No, but let’s be clear I only use them because I have licences and have put effort in to learn them. All the work I did in Photoshop I could have done in Gimp, SnagIt Editor or even Paint. Likewise the data prep work could have been done in Tableau Prep (aside from joining on the spatial files which could have been done in Tableau) or I could have used other free software like EasyMorph.
So back to the design, where did the actual Takeaway design come from?
copied took inspiration from this design from a menu designer, I then played around with the sizes and colours until I was happy.
What blind alleys did you go down in producing your Iron Viz?
Lots! Isn’t that what Iron Viz is all about. I really wanted to add an extra geographic element to the visualisation, and look at the relationship at perhaps 1km grid level. I did the analysis but the relationship I wanted to see just wasn’t there due to geographic anomalies i.e. town centres have a lot of shops but not many people. I tried extending the analysis out to 3km or 3 miles but there was too much noise in the data, remote areas were completely distorting the story and there were no patterns I could. In the end I settled for the simple analysis.
What was the hardest part did you find?
Having done so many Data Beats projects lately, I found it incredibly hard to limit myself to a single dashboard. I’ve got so used to using words to tell my story and explaining it over several paragraphs with visualisations to help me a long the way then this was incredibly frustrating – I had so much to say but not enough space to say it.
You said in the last Feeder you were too intimidated by the competition to enter. What changed your mind to enter?
I regret not entering the first feeder. My thought process came from my competitiveness – I really want to win this thing and I feel the competition is such that I might not be able to. Coupled with the fact the time has increased to a full month I really struggled to create enough time to compete with some of the stronger entries. Before a few hours was enough to compete, now it’s not even close.
But my thought process was wrong, trying to win Iron Viz is like trying to win the London Marathon – it takes hours and hours of practice and training in the build-up to get even close. Does that mean it’s not worth it? No. The fun is in the taking part – I’d encourage everyone to take part and just give it a go. It’s a fun project and something that only comes around 3 times a year.
What about the other entries, any favourites?
For analysis I have to choose my good friend Rob Radburn’s: Who Care’s. Rob has an instantly recognisable style and his commentary and analysis really shine in this piece.
For Story-Telling I’d say Mike Cisneros’: Last Words is just beautiful. Mike pulls together visualisations that might be just “show me the numbers” but binds them with stories of last letters home which just break the heart.
For Design Curtis Harris’: If I was Kevin Durant wins the day for me, it’s just a beautiful piece of work, not over reliant on imagery – just all about the data.
There are lots more I could pick out but these are some of my favourites.
Those are all amazing pieces of work, thanks for sharing Chris and good luck with Iron Viz.