Turning My Daily Commute into a Data Visualization Project

Most people see their daily commute as wasted time. I saw it as a dataset.

For months, I logged the details of my everyday journey to work — departure times, train delays, walking speed between stations, even how my mood shifted with the weather. What started as a way to distract myself during long rides turned into a data visualization project that revealed patterns I would have never noticed otherwise.

Collecting the Data

In the beginning, I kept it simple. I opened Google Sheets on my phone and manually entered:

• Departure & arrival times for each leg of my commute.
• Delay minutes (if any).
• Walking duration between home, station, and office.
• Noise level estimate inside the train (low, medium, high).
• Mood score — a quick 1–5 rating.

After a few weeks, the manual entry became too repetitive. So I leveled it up:

• Wrote a Python script that used GPS logging on my phone to track walking/ride times automatically.
• Pulled weather data from an open API to log rain, temperature, and snow.
• Used a smartwatch app to grab step counts + heart rate, which I synced into my dataset.

Suddenly, I wasn’t just collecting numbers — I was building a story of my commute.

Visualizing the Commute

With data in hand, I started exploring visualization tools:

• Matplotlib & Seaborn in Python gave me quick charts: average commute times, day-of-week trends, and mood vs. weather.
• Tableau let me create a dashboard showing how commute length shifted across weeks and seasons.
• D3.js gave me an interactive timeline where I could hover over a date and see all the conditions (time, mood, noise, weather).

The more I visualized, the more I realized: my commute wasn’t random chaos — it had rhythm.

What I Learned

Here are some surprising insights from my data experiment:

Mondays= Pain – My commute delays were 25% higher on Mondays than midweek.
Weather= Mood Killer – On rainy days, my mood score dropped by 40%, regardless of delays.
Leave Early, Save Time– Leaving just 7 minutes earlier reduced my average commute time by 15%.
Noise Patterns – The loudest rides weren’t at rush hour but on evenings when major sports events were happening — apparently, fans and train noise go hand-in-hand.

These weren’t just fun facts — I actually started leaving earlier and packing headphones when I knew a big game was on.

Why This Was Worth It

1. Practical Benefits – My mornings became less stressful once I knew the “sweet spots” to leave.
2. Skill Growth – I got hands-on practice in Python, APIs, and data visualization tools.
3. Storytelling Value – I now had a personal project I could show in interviews to demonstrate data storytelling.
4. Mindset Shift – Instead of seeing my commute as wasted time, I turned it into a learning experiment.

Takeaway

Not every data project has to start in a lab, a hackathon, or a work assignment.

Sometimes the best datasets are sitting in your daily routine. By tracking small details, you can uncover patterns that change the way you live and along the way, you sharpen your skills as a developer.

So next time you’re bored on your way to work, ask yourself : what could I measure here?