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We caught up with Berlin-based data journalist Tin Fischer, co-creator of the project, to learn more. He's been working with Instagram data for some time, "usually creating infographics which answer unimportant questions like what the most #rainbow-rich season is".
Tin explains that the idea of combining Instagram geo-data with subway maps helped to solve a visualisation problem: "It's tricky because the data is very abstract. We did heat maps of cities for example, but you'd have to be very familiar with a city and a bit of a geo-geek to properly 'read' them. What we needed was a grid that everybody has in mind when thinking about a city and then we could stick the data onto it. This was, of course, the subway map. A street map would have been far too detailed."
When it came to the choice of maps to use, the team leapt at the chance to employ Jug Cerovic's clever redesigns of prominent metro maps. For one matter, the official maps cannot always be legally reproduced by third parties. But there were several other good reasons, says Tin: "They are universal, like hashtags. They are colourful and have a square format, like Instagram. And Jug immediately liked the idea, too!"
The tags shown at each station on the networks are statistically derived from the frequency of their use within 300 meters of that station and also their uniqueness: "the largest deviation from average frequency of each respective hashtag across all stations". But there's a little editorial intervention, too. If the most frequently used tag was simply the name of the station or the neighbourhood, for example, then the team moved down to the next on the list. Equally, if the statistically-derived top tag was an event that doesn't reoccur annually at the same place, then it was skipped for a more useful choice.
Once mapped in this unique way, every city yields surprises: as Tin says, "You start 'seeing' a city when looking at these maps."
The maps illuminate cities, but through their own interesting lens: "Of course, it's a map of what Instagrammers post in a city – not the general population. So, it's only representative of a particular group and their photography habits. But it's a bit like when a friend shows you his or her photographs from a city trip – only that now, all Instagrammers collectively are your friend. You get to see a certain angle, but you also get a pretty good picture of what's going on where."
Tin reveals that there are more secrets hidden in the Instagram data that form a further map layer not yet visualised. "They have the tendency to gather all in the same few places. There are always a couple of blockbuster hashtags and then a lot of small ones. Like in real life."