Data Visualisation: My Commute

Taking notes on my commute offered me more insight than I would have guessed before!

For my visualisation, I decided to focus on my morning and afternoon commute to and from school (my workplace), and therefore I labelled the time in between 10-16 with “my school day “. I used the 24 hour-notation for my time scale because it is common here in Switzerland, especially on train and bus timetables. The scale begins at 6 o’clock and ends at 19 o’clock because I always commuted within these times. 

In addition to the time I spent on trains and buses, I recorded whether it was a single or double vehicle, whether I was studying related tasks or on my phone ( I don’t do anything else on my commute) and whether I travelled alone or with a colleague. Furthermore, you can see if there was a delay in my commute and when I shopped groceries while changing trains. 

Legend to my visualisation below
Data visualisation of my commute (week 3 / Block 1)

This data visualisation shows that I tend to study on inter-city trains because they have tables for your notebook and you have enough space to get work, or in my case study related tasks, done. I am on my phone more on S-trains, because space is limited there. I don’t do any of those two activities on the bus, which is probably down to the fact that I tend to get motion sick on the bus and in the car.

You can tell that I went grocery shopping twice in the morning (to buy my lunch for the day) and twice in the afternoon (to take home groceries to cook dinner). 

Four of my trains were delayed this week, which is actually quite a lot and not representative of Switzerland’s train travels. Switzerland has the densest rail network in Europe, and the trains are around 90% on-time ( ). Counting the trains, I used this week, and how many were delayed, only 82% of my trains were on time. To me, it is very entertaining to see all this information by just tracking my commute. 

I would wish to visualise the exact times in a more precise way! I had to compromise between clear arrangement and the amount of information I wanted to get across: This is something you have to be aware of when collecting data. 

5 Replies to “Data Visualisation: My Commute”

  1. Your visualization confirmed for me that I really miss my commute! It reminded me of how I would use that alone time to study, read, and catch up on emails. It was also really clear and easy to understand, plus it even looks like a little collection of train carriages on the move!

  2. I think that your visualization is very efficient and aesthetic. It has the sense of movement and timetabling at the same time.

    For me, Switzerland is very much about trains because my husband works for Stadler. It was also interesting to know some people can still travel to work in Europe.

  3. What a good idea! I have come to miss my daily commute by train into Edinburgh over the last year (though I suspect delays are more routine here!). It could sometimes feel like the most productive part of the day. What’s really interesting about your commentary is that you have noticed just how much of your activity, and the environment you are located in, can be translated into data for analysis. Things we might otherwise not think about too much might be logged in the background, and used to create new data-centred understandings of daily lives. You’ve raised the critical issue about the selection criteria that determine which data are included/excluded for analysis too. Do you think tracking your daily routines in this way could help us understand anything about the collection and analysis of data for learning?

    1. Good question, Ben. What I noticed during that week is that my data collection is somehow limited. I can only visualise a certain amount of information, and in addition, all my information are self-reported! This means I am in control of what kind of data I collect and share on this blog. This awareness could influence my choice of data collection. In terms of learning, this could mean that we have to be mindful of which data we collect and which data contribute to our understanding of our students learning. In my profession, as a primary teacher, I support over 120 pupils a week. What I observe are 120 individual paths of learning. These observations come to my mind when I read about machine learning and big data because I often question how my students’ approaches to learning are represented within these concepts.

  4. Hi I really liked the visualization and how it is easy to read. I felt like it is really a journey!

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