A little sideproduct from the selfiecity project:
Shared tag space — a comparative visualization of the keywords people in five cities use to describe their selfies. The visualization displays a network of tags, cities and photos. The photos are used as bridges between tags and cities. Only tags that have been used at least twice are displayed with a text label. Bigger tags are used more often. The tags in the center constitute shared vocabulary across the cities, while the ones on the outside are more specific to one (or sometimes two) cities. The visualization was created as part of the selfiecity project, an interdisciplinary investigation of the selfie phenomenon.
The Data Cuisine Workshop Barcelona was fantastic, we had a really great time.
Big thanks to my collaborators Dr. Susanne Jaschko and Sebastian Velilla, thanks to Jose Luis de Vicente and Olga Subiros for bringing us over, and last but not least for our great participants for the crazy dish ideas they came up with!
The workshop is split in two days, with day one discussing the concept and design aspects of data visualization, while day two goes into the nitty gritty of building a data-based graphic from scratch.
I teach only occasionally, so this is a rare opportunity. I can only recommend to register as soon as possible :)
Also, a good opportunity to talk a bit about the last workshop Dominikus and I gave at resonate. It was a great group, and I was really impressed what the participants came up with in very short time. We worked with a dataset about movies and TV series from IMDb and after a day of basic introduction, the participants went from initial questions and concept over data explorations to visual refinement and annotation in what was basically a day of workshop time.
Tableau and RAW proved to be extremely valuable tools. It is amazing what you can achieve in really short time with these applications. The workshop was a big success, and a really nice experience, which also showed in the evaluation results.
At the end, I tried to make a more general point, which is of big importance to me, and which I would like to expand on in the following:
You will often hear these days, that data visualization is great for “telling stories”, to “make the complex simple” or to “make boring facts exciting”.
While this all true to some degree, I think it misses the greatest quality of data visualization today: to provide us with new kinds of “glasses” to see the world.
I am happy to announce that I just published my first information graphic for Scientific American on a topic that is very close to my heart — bees :)
Together with my passion for communicating complex science, Jen Christiansen correctly realized this package would be an offer I could hardly resist (despite my packed schedule), and I am really glad how the project and collaboration turned out.
Personally, I am always keen on hearing the inside stories behind projects — so, here is me returning the favor, with a brief overview of how I approached the project:
We reworked the OECD Better Life index. Besides a general facelift and interface overhaul (top work by Raureif, as usual), more languages (Spanish and Russian), the biggest change has been to replace the Flash-based interactive visualization with an HTML5 based component. Big congratulations to Dominikus Baur for the feat of accomplishing something very close to the Flash original (and in some parts even better)!
Dominikus has a super-useful in depth write-up of all the many little tricks that went into optimizing the graphics over on his blog, so I’ll shut up now and let the dirty rectangles do the talking.
My speech from the summit. No big surprises for long-time followers, but a good summary overall, I guess. Also, I wear a suit!
Stadtbilder (“city images”) is a new little side project of mine — an attempt to map the digital shape of cities. I am increasingly fascinated by the idea of mapping the “real world” — life and culture as opposed to just physical infrastructure — and when I learned about the really deep datasets Georgi from Uberblic had been collecting, I just had to work with the data.
The maps show an overlay of all the digitally marked “hotspots” in a city, such as restaurant, hotels, clubs, etc. collected from different service like yelp, or foursquare. What they don’t show are the streets, the railroads, the buildings. I wanted to to portray the living parts of the cities as opposed to the technical/physical infrastructure you usually see on maps.The only exception are the rivers and lakes, because I felt they help a lot in orienting on these fairly abstract maps.
While the designs are meant to be printed, as a digital companion, the website helps you decipher the posters by providing a little map overlay on click. If you are interested in a print, please sign up to be notified when prints are available — I still need to figure out the precise logistics. (Let me know if you know of a high-quality poster printing and shipping service a la imagekind which can also ship from EU/worldwide..)
For now I settled on the three main German cities, because they have very different characteristics, and I know them very well. But I might be convinced to do other city editions as well :)
Here are some process shots!
It took me a while to figure out how to overlay these four different heatmaps on top of each other. I experimented with 3D manifolds, different dot patterns, small multiples, etc. etc. In the end, I am really happy with the solution I came up with, as combining always two of them in one stroke direction allows to decipher all of the dimensions, and just by looking at stroke width and brightness (if the two overlap), no need to do “color mixing reverse engineering” in your head, which is pretty much impossible anyways. The downside of this approach is obviously a low spatial resolution.
How does it work technically?
I first query the Uberblic API (not public yet, sorry ;) for the scores in the different categories by marching through a hexagonal grid.
From the values, I first draw fairly blurry heatmaps in processing:
I then walk over these heatmaps pixel by pixel in diagonal lines, and draw on a new canvas a line whose stroke corresponds to the brightness of the pixel on each step:
and then I merge and simplify these images in Illustrator. That’s it!
In other news, the Berlin version made it to the cover of WEAVE magazine already (but it should be noted that for this use case, the background was tweaked more into a blueberry tone, the original is way more violet):
If you find this project interesting, Flowingcity hosts a collection many more projects in this direction.
Anyways, let me know what you think of it!
Update: Prints are now available!
Nathan Yau: With client work, you must hear “innovative”, “compelling”, and “engaging” often. Personally, I get a lot of this, and potential clients often don’t have data to work with. What makes a project interesting to you and worth your time?
Moritz Stefaner: In hindsight, the most satisfying projects for me were the ones, where the client or project partners were able to follow me in exploring, questioning, and extending the original data basis and project, sometimes going far away from the original project idea. Some ideas sound really good in the beginning, but turn out bland or much too complicated once executed with real data. Other ideas sound boring beforehand, but become exciting in a specific context. And other ideas you just develop by getting inspired from the data material itself. So, I realize this approach is a lot about trust and also flexibility on side of the client, but is the only way to get your visualization on point, ultimately.
NY: For people like us, the data is interesting as a CSV file, but most don’t get that luxury. What role does aesthetics play in making data interesting to a wider audience? What about truth? (You are after all, the truth and beauty operator :).
MS: I honestly hope, and sometimes think, that the playfulness and organic nature of some of my visualizations does actually get people excited about a topic they would not have been interested in beforehand. I do like to dig in data and be a little “data detective” myself, and if my work can contribute to getting more people excited about querying and questioning data, and, most importantly, come up with new questions to ask, than I am happy :) Aesthetics and appeal are of course a big part of the equation here, besides, of course, relevance of the information presented and truthfulness of the representation. But to me, aesthetics is very little about “decoration” — in my world, a clearly pronounced, rigorous, characteristic, “on point” representation of data lets the inner beauty of a compelling data set shine, instead of trying to “sex up” bland lists of facts.
NY: When getting started with visualization, everyone seems to want to know the “rules” of turning data into geometry. Are there any rules?
MS: There are no hard rules, but, as in any skill-based profession, there are some things that work better than others, and as for any cultural activity, you are not acting in the void, but in an established set of conventions, expectations and common knowledge. In my experience, great works are not strictly adhering to “the rules”, but, at the same time, are very sensitive to these two types of contexts, and are able to refer to, and sometimes even play with them.
NY: What’s the first thing you do when you have your data and get started on a project?
MS: Get a coffee, put on a DJ set, fire up Tableau and or Gephi, and Sublime Text for Python and data massaging and plot the sh*t out of the data. Then inspect, take a day to do something else, and follow up on the interesting perspectives. To continue, I might transform the existing data, get some new data, or take one of the interesting, but still very generic views and start to tailor it more to the specific issue at hand.
Looking forward to having a copy of the book in my hands! And thanks, Nathan!
Btw, a few weeks ago, iCharts interviewed me, too. Good times!
So, I made this thing on how many women speakers we have on stage at the type of conferences I like to go to. Personally, I think our field is probably already quite grown-up in respect of diversity and balance, but I think we could still do much better. I hope my small data collection and visualization helps a bit for organizers to reflect where they would like to land on my x-axis :) Relatedly, if somebody has good intentions, but trouble finding good female speakers, drop me a line, I know plenty.