Matt N
TS Member
- Favourite Ride
- Shambhala (PortAventura Park)
Hi guys. I wasn't entirely sure where to put this, as it's not explicitly park-related, but I did something recently for one of my university modules that I thought people might be interested in seeing!
Basically, I did a module this semester on Data Visualisation, and one of the assignments was to create a data visualisation project. And for mine, I decided to create an interactive visualisation dashboard showcasing a comparison of average theme park major attraction queue times by location and operator! Now the work has been marked and I've received my grade, I feel at liberty to share this with you all!
Dataset-wise, I took data from the "average queue time" section from each theme park in queue-times.com's Parks index (https://queue-times.com/parks). I should point out that the data dates back to January 2025, and that is because it is actually reusing a dataset that I used for a previous analysis on TowersStreet about whether UK theme parks had the worst queue times in Europe, which also encompassed parks from other continents. The previous analysis regarding European queue times can be viewed here if you're interested: https://towersstreet.com/talk/threa...queue-times-in-europe.7443/page-6#post-498718
Some of the more specific aspects and methodology regarding my construction of the dataset can be read in the linked post above, if you're interested in scrutinising my processes.
Anyhow, I used this dataset to create a visualisation dashboard showcasing how major attraction queue times at theme parks vary by location and operator! For the technically minded, this was created using Python's Dash framework, with the figures themselves being created using Plotly.
A video tour of me executing my dashboard can be viewed here for full effect (the actual dashboard itself doesn't begin for a few seconds, as I start by running some of the necessary prerequisite code blocks in the notebook):
Or if you don't want to view the video (although I must admit that it does remove the effect of it being a cohesive interactive dashboard somewhat if I show you separated screenshots), here are a few sequential screenshots of what I created, working from the top down.
This is the introductory block of text and the introduction to the first plot on location:
This is the first plot area, showcasing the difference in major attraction queue times between location. In this area, you can break the data down by either country (with each country's continent highlighted by its bar colour being different) or continent:
And this is the second and final area, showcasing the differences between operators:
I should add that a particular feature I was aiming to show in these screenshots is that you can hover over each bar and showcase the exact average value. The speech bubbles aren't just in there statically; when you hover over a bar, it showcases the average major attraction queue time for that location or operator!
If you'd like to execute my dashboard for yourself, here is a Google Colab notebook link (to execute, just press play on each code block in the order in which they're inserted into the notebook): https://colab.research.google.com/drive/1ERovqBpb2HGGF1BRkxlnxyIevsA5UUWw?usp=sharing
I should also add that this has been adjusted slightly compared to the version I handed into university. Following my lecturer's feedback and the grade I received, I made the following adjustments:
So, that's something I created for university that I thought you all might like! As the intended audience of my visualisation was theme park enthusiasts (as I specified multiple times in the accompanying evaluation report!), I hope you guys like it! I'd really appreciate any feedback, good or bad; any feedback will allow me to adjust my approach to this sort of thing in the future. And seeing as I'm conscious that my theme park data analysis posts can sometimes have a bit of an... accessibility problem for the less technically minded, I'd be interested to know; would me creating a dashboard like this one when I do some data analysis make my work slightly more accessible and readable?
Basically, I did a module this semester on Data Visualisation, and one of the assignments was to create a data visualisation project. And for mine, I decided to create an interactive visualisation dashboard showcasing a comparison of average theme park major attraction queue times by location and operator! Now the work has been marked and I've received my grade, I feel at liberty to share this with you all!
Dataset-wise, I took data from the "average queue time" section from each theme park in queue-times.com's Parks index (https://queue-times.com/parks). I should point out that the data dates back to January 2025, and that is because it is actually reusing a dataset that I used for a previous analysis on TowersStreet about whether UK theme parks had the worst queue times in Europe, which also encompassed parks from other continents. The previous analysis regarding European queue times can be viewed here if you're interested: https://towersstreet.com/talk/threa...queue-times-in-europe.7443/page-6#post-498718
Some of the more specific aspects and methodology regarding my construction of the dataset can be read in the linked post above, if you're interested in scrutinising my processes.
Anyhow, I used this dataset to create a visualisation dashboard showcasing how major attraction queue times at theme parks vary by location and operator! For the technically minded, this was created using Python's Dash framework, with the figures themselves being created using Plotly.
A video tour of me executing my dashboard can be viewed here for full effect (the actual dashboard itself doesn't begin for a few seconds, as I start by running some of the necessary prerequisite code blocks in the notebook):
Or if you don't want to view the video (although I must admit that it does remove the effect of it being a cohesive interactive dashboard somewhat if I show you separated screenshots), here are a few sequential screenshots of what I created, working from the top down.
This is the introductory block of text and the introduction to the first plot on location:
This is the first plot area, showcasing the difference in major attraction queue times between location. In this area, you can break the data down by either country (with each country's continent highlighted by its bar colour being different) or continent:
And this is the second and final area, showcasing the differences between operators:
I should add that a particular feature I was aiming to show in these screenshots is that you can hover over each bar and showcase the exact average value. The speech bubbles aren't just in there statically; when you hover over a bar, it showcases the average major attraction queue time for that location or operator!
If you'd like to execute my dashboard for yourself, here is a Google Colab notebook link (to execute, just press play on each code block in the order in which they're inserted into the notebook): https://colab.research.google.com/drive/1ERovqBpb2HGGF1BRkxlnxyIevsA5UUWw?usp=sharing
I should also add that this has been adjusted slightly compared to the version I handed into university. Following my lecturer's feedback and the grade I received, I made the following adjustments:
- I added the differing colour palette according to continent on the country bar chart. Originally, all of the country bars were the same colour.
- I made the hover boxes more polished and user friendly (originally, I stuck to the Plotly defaults, which had "country=..." and "mean=..." in them).
- I altered some of the colours. Originally, some of my bar colours were green and red, which I was warned could impede accessibility for colour-blind individuals.
So, that's something I created for university that I thought you all might like! As the intended audience of my visualisation was theme park enthusiasts (as I specified multiple times in the accompanying evaluation report!), I hope you guys like it! I'd really appreciate any feedback, good or bad; any feedback will allow me to adjust my approach to this sort of thing in the future. And seeing as I'm conscious that my theme park data analysis posts can sometimes have a bit of an... accessibility problem for the less technically minded, I'd be interested to know; would me creating a dashboard like this one when I do some data analysis make my work slightly more accessible and readable?