Recently I went snowboarding with friends in Livigno, Italy. Everything was perfetto! The variety of slopes and parks was impressive. The weather couldn’t be better with fresh snow every morning and sunny days. It was a full week of pure fun, sport and leisure. This was my first time in Italy, in the winter, and I must say that I love it as much as I love it in the summer.
One thing that I did almost the next day when I got back home, was to check the lift pass statistics as suggested on my pass at http://www.skipasslivigno.com/skicheck/
When I entered my pass number I was surprised that I received only one .csv file that if I open in Excel I will see raw data of few columns and many rows, representing all my lift entries. We also gathered with friends to compare and discuss when and where someone of us was riding. As you can imagine it was hard to compare anything using text editors.
And by talking with others I heard that it is common for other resorts also.
As a data visualization fan, I thought that it would be nice to have a way to visualize all of this raw data, in a user friendly way. I wanted to see which slopes/lifts I was riding the most, which my friends were riding, when and where we were together, when we were riding on opposite slopes and how our days were going. So I went over the stats and started experimenting with ideas, how such statistics will be nice to be shown.
So I started building a ski stats visualization library, having my own and my friends’ stats from Livigno. I can say that it is now ready for preview and I wanted to get feedback for it. You can see a demo of it here http://tgeorgiev.github.io/skistats/ . It is fully functional, so if you have visited Livigno and have your ski pass statistics, go ahead and provide them to see you visualizations. There is also a demo mode, that shows fake data, to have a better feeling of the project.
It is still in early development stages, so if you find any issues, have feature requests or want to see stats for other resorts, please open a new issue in the GitHub project page or comment your thought bellow this post. You can also check here how you can contribute.