Moritz Stefaner came to the field of data visualisation with a background in cognitive science and interface design. His works are intriguing in their effort to create a comprehensible visual vocabulary for mapping complex data.

In his latest project “Stadtbilder”, the German designer visualised geolocation data for three German cities, with the aim to show “where and in which form the city is alive”. This past week Moritz and I sat down to talk about the project in detail.

Moritz, you wrote that the data set provided by Uberblic was the inspiration for you to start your project “Stadtbilder”. What fascinated you about this data?

I always wanted to map the real life of cities, to show what is happening instead of mapping a physical landscape. So, of course, I have always been following the projects done, for instance, at MIT SENSEable City Lab or by Eric Fischer. I had done some smaller experiments in that field before, such as mapping tagged images from flickr or other services, but of course these things had been done before. So the idea lay dormant for a while.

But then, Georgi Kobilarov from Uberblic told me at the beginning of this year that he had gathered a data set for several German cities, which combines data from social media services such as Foursquare, Yelp, and others. So I suddenly had access to really well compiled geographical information about shops, restaurants, nightclubs etc. I then just had to go work with this data and see what it reveals about each of these cities.

The three maps are focussing on social urban structures and skip the actual topography almost completely. What was the most interesting part for you in designing these maps?

First of all – maps are really difficult in terms of data visualisation, because already the basic map layout takes up a lot of the two-dimensional space you have to work with. So it is not easy to create a really effective map with a multi-layered data set. When I worked on the “Stadtbilder” project, I experimented quite a bit with options for visualising these multiple layers, using 3D landscapes, colored dots and structured lines.


The hatching I finally came up with provided a very nice way to combine four categories of data on the map. I assigned a colour to each type of space: Food places are red, while night clubs are yellow etc. Two layers of lines are directed top left towards bottom right, while the two remaining layers take the opposite direction. Line thickness corresponds to the density of this type of places in the area.

This way even in dense areas, the eye can still differentiate the data values in each of the lines. So you get to see clusters within a city, such as that music venues are usually in places that are not otherwise very dense, because you need a lot of space and it can be loud, so there shouldn’t be too many neighbours.

You hold a Bachelor degree in cognitive science. What were your main insights about the perception and processing of information? How do you reflect that in your work?

Cognitive science draws from many different disciplines such as mathematics, computer science and linguistics. So you really jump into various methods of researching how the brain works. Yet, I haven’t derived any simple guidelines that I follow when creating a visualisation. However, this experience did shape my work in various ways, and I think it was a large influence in guiding me into the field of data visualisation.

For instance, the experience I gained in linguistics made me think about design in terms of a visual vocabulary. How can you shape the single elements of a visual design such that it is easily comprehensible? What is the syntax, what is the vocabulary of interactive visuals?

And, of course, when studying cognitive science, you will learn to process and analyse data sets of all kinds. When you analyse a brain scan for instance, you must deal with large sets of numeric data. And I learned a lot about optimising algorithms, data structures, knowledge representation etc. That helped me to develop the technical and conceptual abilities that I use for working with data today.

How do you see the state of digital cartography these days, what are the big challenges at the moment?

To be honest, I find it very challenging to make a great map, so I had avoided to work with traditional maps for quite a while and preferred visualisations of virtual spaces, such as e.g. the twitter community around the resonate festival. Cartography is an old craft, and it does take quite some experience to learn the craft. Yet, with regards to digital cartography, we currently have an incredible flow of new technologies, we suddenly have access to satellite imagery, tools like OpenStreetMaps – none of this was available ten years ago.

There are still some technological issues to be dealt with, such as how to extract details from large data dumps, or to work with map projections other than the omnipresent Mercator. But apart from these technological questions – I don’t really see a major conceptual challenge, but more an incredible pool of opportunities.

And last but not least – will you set up more maps like these three? I hear there have been requests for maps of other cities in other countries?

At first, the Uberblic data were available for German cities only, and I live in Germany, so that’s how the first choice came about. However, I am working on getting access to data for American cities – so if time permits, I would definitely love to do some more cities!

All images © Moritz Stefaner. For more information see the project website or Moritz’ project description. Prints are available at Redbubble. Data source: Uberblic. Thanks, Moritz!

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