I recently entered The Data Lab - Innovation Centre 2025 Data Visualisation Competition. I am happy to share that my project was shortlisted from nearly 100 entries, which gave me the opportunity to present my work to an expert judging panel.

Beyond the competition itself, this was a great reason to upskill in Google BigQuery (a cloud data warehouse) and Looker (a BI tool) for the first time.

The Project: My entry explored the “EV Rebound” effect. While electric vehicles are a cornerstone of Net Zero, lower driving costs could inadvertently put more cars on the road. My analysis looked at how these shifting habits might impact congestion in Scottish cities and local neighbourhoods between now and 2050.

Technical Build:
SQL: I managed the data engineering in BigQuery, writing scripts to handle multi-table joins and cleaning complex datasets to ensure the data was formatted correctly for the final visualisation.
Python: I utilised Python to process geospatial shapefiles and run linear regressions. This statistical grounding allowed me to test my “Urban Penalty” theory, confirming the link between local population density and projected congestion growth.

The Outcome:
Whilst I did not take home the top prize, the experience of presenting to the panel was excellent and I was really encouraged by the written feedback I received:

💬 “Analysis all seems very competent and varied… the work demonstrates a solid understanding of the long-term negative costs associated with traffic congestion.”
💬 “The language used throughout was clear which is great on such a complex topic. The annotations were also helpful and the story was impactful from the start.”
💬 “I like how there is a ‘story’ running through the charts… the first chart is very clear and does a good job of setting the scene, and the second chart does a great job of attracting attention.”
💬 “The approach of linking charts to a map is nice… and it has identified a really interesting issue that I wasn’t aware of.”

A big thank you to the DataLab and all judging panel for the opportunity.