Alright folks, let me tell you about my little side project: visualizing the Euros 2024 in Shenzhen. Sounds random? Maybe! But it was a fun way to play with data and maps, and I learned a ton along the way.
It all started with me being a football (soccer, whatever!) fanatic. I was hyped for the Euros, and I was also itching to try out some new data visualization techniques. I thought, “Why not combine the two?” So, the idea of mapping out where people were watching the games in Shenzhen popped into my head.
First thing I did was figure out how to get the data. I wasn’t about to go door-to-door asking people about their viewing habits! So, I turned to the usual suspects: social media, online forums, and even some local restaurant review sites. Basically, I scraped (yes, I scraped!) any public data I could find that mentioned “Euros 2024” and “Shenzhen” along with location info. It was messy work, cleaning up typos and trying to standardize addresses, but hey, that’s data science for ya!
Next up: mapping. I decided to use a * because it was relatively easy to use and free! I spent a good chunk of time wrestling with the API, trying to get the map to center on Shenzhen and to properly display the data points I’d collected. There were some hiccups with coordinate systems and data formats, but Stack Overflow and a whole lotta coffee pulled me through.
Then came the fun part: making it look good! I played around with different map markers, color schemes, and pop-up windows to display the location names and some basic info (like the number of people who said they were watching there). I even tried to add some heatmaps to show the most popular viewing spots. This was where I really got to flex my (admittedly limited) design skills.
The result? A simple but interactive map showing where people in Shenzhen were supposedly catching the Euros games. It wasn’t perfect. The data was probably biased towards online users, and the locations weren’t always precise. But it was a cool proof-of-concept and a great way to learn more about data visualization. More importantly it was fun!
- Data Collection: Web scraping, manual data entry from forums.
- Mapping Library: *
- Data Cleaning: Regular expressions, manual correction
- Visualization: Map markers, heatmaps, pop-up windows
What did I learn? Data is messy, maps are tricky, and even a small side project can be a great learning experience. Plus, I got to watch the Euros while working on it, so that was a win-win!
