data visualisation | clues to the cloud

Photo: kiln.it

Kiln is a design studio specialising in data visualisation, digital storytelling, maps and animation. It was founded and is run by Duncan Clark and Robin Houston, creators behind such projects as Women’s Rights or In flight for the Guardian. In this short interview Duncan Clark talks about how they go about their projects.

How do you choose what subjects to cover in your visualisations?

It’s a mix. Sometimes we have an idea that we know we want to pursue; sometimes the Guardian or another client will approach us with an idea.

What is key for you in the process of designing information?

One golden rule is to let the information speak for itself. There’s no point making a pretty visualisation if it doesn’t make the data clearer to understand and easier to interrogate.

What is your favourite project that kiln.it worked on so far and why? What do you think makes it interesting for people to explore?

“In flight” is certainly the most ambitious thing we’ve done so far and possibly my favourite. I like that fact that almost everyone says “wow” at seeing the sheer number of planes that have flown through the air in the last 24 hours. But I also think it’s interesting as an experiment in combining different approaches to storytelling: it takes elements from documentary making, data visualisation, radio production, live mapping and tries to combine them into a coherent whole.

What’s your work process? How much leeway do you have in your work? Do you get precise instructions for your projects or do you only accept broadly defined commissions?

It varies. Sometimes the starting point of a commission is just a broad subject area; at other times a client might have a very specific visualisation technique in mind from the outset. Most commonly, though, we’re given a dataset and asked to work out how best to turn it into something compelling.

What advice would you give to a budding data journalist?

It depends what kind of data journalist you want to be. If you’re mainly interested in breaking stories, then getting acquainted with how to get unexplored data via Freedom of Information requests might be a good idea. If you’re more interested in interactives and visualisations then learning to code can’t hurt: access to good developers is always a bottleneck for journalists, so being able to do at least some of the coding yourself is a huge advantage. Try getting started with a free HTML, CSS and JavaScript course at Codecademy.

All the pretty things you see when browsing through data-driven news reports or visualisations are not that difficult to produce. In fact, they are two clicks away*. Mind the asterisk, though:

*IF AND ONLY IF you have a nice set of cleaned-up data easily readable by your software, like Tableau Public.

This was indeed the case during our class last week. Easy.

But while working on my first ever visualisation from the comfort of my room, I couldn’t escape the feeling that my time has been sneakily stolen. Not by tweaking colours, tooltips, filters and maps (the fun part), but by struggling to get something out of the numbers in Excel. I had to give up on a couple of more exciting ideas in favour of this breakdown of population in the largest city (% of urban population) as delivered by the World Bank. Here’s the result:

Population in largest city is the percentage of a country’s urban population living in that country’s largest metropolitan area.

After calculating the percentage change in the population dwelling in the largest city in every country between 1961 and 2009 (numbers for all countries were only available until that year), I created a symbol map which shows which largest cities grew most.

Excel’s vlookup function proved extremely useful to match countries and regions in order to show the growth regionally on a treemap, with Sub-Saharan African cities attracting population the quickest over that period of time and North American ones noting the slightest growth.

As an addition, I decided to include a bar graph showcasing the greatest magnet-cities of 2012.

The latter two serve as filters, thanks to which data might be manipulated either regionally (when a particular region is selected from the treemap) or by country (when a particular bar is selected).

The initial plan was to also include a map with a timeline presenting the city’s population change over years, but I still lack the skill to do it.

All in all, this first attempt at visualisation was great practice and allowed me to get more insight into the workings of Tableau. I’m looking forward to producing some more interesting pieces of work.