Data viz challenge #1
Snow and Fire
Hello and Hi!
This is the first dataviz challenge for the Data Visualisation Stockholm (DVS) Meetup group. We’ve been discussing holding a challenge for at least a year and FINALLY we’ve finished the first one!
DVS was founded two years ago when the two original organisers started their own two dataviz Meetup groups just a few days apart in Stockholm, Sweden, but then decided that Stockholm was a one-dataviz-Meetup-group town and joined forces. We both wanted to create a space where we would gather enthusiastic designers, developers, artists, statisticians, journalists, and all kinds of amazing people interested in data to network and get to know each other. We also wanted our members to have the opportunity to grow as data designers.
We have discussed holding challenges for a while now, but it took an enthusiastic member asking us to stop procrastinating (Thanks!) and get on with it. But the discussions were not fruitless; they got us talking about the types of dataviz challenges we liked and the kind wanted to run. We determined that a fun and engaging challenge, one that we would want to participate in, had two equally important parts, an interesting theme and a design constraint. The theme would attract participants and the design constraint would challenge them to be creative.
This first challenge was all about Stockholm winters. The dataset had data on snow depth and temperature in Stockholm stretching from January 2021 all the way back to the beginning of the early 20th century.
I must say that I was quite impressed with the variety of submissions we got!
So in no particular order lets go through the charts and see all submissions. Descriptions were written by the respective authors.
Matthias Stahl. Freelance data visualisation designer with a history in bioinformatics, passionate about the combination of data, code, art and design to craft beautiful and impactful visuals.
Link to the interactive viz
I wanted to go for a spiral layout as this enables the year-wise comparison of this periodic data. I then transformed the temperature and snow data into binary variables — fitting to the overall black-and-white theme. Is there snow: yes/no; and is the temperature higher than 10 °C: yes/no. The best way to encode the temperature turned out to be the spiral line thickness. The circles for the snowy days are apparently inspired by snowflakes. In order to make it clearer that circles represent snowy days, I added a snowing animation.
Tools: Sketch on paper, then Svelte and D3
Alenka Gucek. Postdoc at Uppsala University, studying diabetes and cell biology. I’ve always enjoyed doing visualisations for research and now I’m looking for ways to change career to dataviz.
Originally I was planning for a gif with falling snow, but it looked like too much of chart junk. Then I switched to do something simple and informative, inspired by Evelina Judeikyte’s talk [at one of the Meetup events] on simplicity, annotations in The Economist and spark lines by Edward Tufte.
Tools: Excel, Origin and CorelDraw
Nina Lindell. Data visualisation engineer at Telia, building products based on mobility data.
Link to the code
I wanted to make the data look like piles of snow, and finally create a ridge-line plot (aka joy plot). I have recently started learning D3 and the challenge seemed like a good opportunity to try out my skills on a real dataset. I decided to keep the last 50 years of data and only look at the snow depth.
There are still some things I would like to improve upon — highlight interesting data points with annotations, mark the scale, and make winters continuous so that at least November — March are on the same row. That is something for the next iteration.
Tools: Tableau for prototyping, D3 for the final chart
Tyler Wolf. Data visualisation specialist at King Games.
I thought about the question “what does a month look like?” And then broke it into the two pieces of data we had in the dataset: temperature and snowfall. But then I wanted to give it context and look for patterns, specifically thinking of the question, are days getting hotter? — obviously thinking of climate change. So I ran a moving average on temperatures to break the temperature component into above or below average for that month. I did this because we can conclude that it’s cold in the winter and hot in the summer already, so a relative comparison gives more context. That made three pieces of data. Finally, I thought about how much I like it when it snows, so it would be nice to get an idea of how many days it was actually snowing in each month. So with those pieces of data and the limitation of black and white only, I experimented with rectangles and lines until I arrived at something that felt right and could condense the data, making each month look unique and allowing their elements to interact with each other. The aesthetic was inspired partially by some of giorgia lupi’s work.
Tools: D3 to draw the graphics, observablehq to get up and experimenting quickly and Figma for early sketching and to draw the legend and title.
Patrick Wojda. Data visualisation and front-end engineer at Aftonbladet, Swedens largest online newspaper.
That’s a wrap!
Stay tuned for more challenges, we will be announcing them on our GitHub repo (https://github.com/Dataviz-Stockholm/challenges).
Take care and stay safe.