Pave the World
This project was a dissertation for my Master’s degree in Data Science. It was very challenging but I learnt a lot about working with spatial data. I especially enjoyed the occasions when I reached out to my supervisors and the authors of some papers I was reading. When completing projects in the future I will make an effort to do more of that as I think it makes them both more enjoyable and better.
Main results
We determined that paving over soil with concrete very likely reduces GHG emissions from the soil specifically. Soil being the key word here because our analysis does not consider the additional ecosystem services of soil. For instance:
- How did that forest that was on the car park before it was built affect GHG emissions?
- How has the flood risk changed now there is concrete preventing water from being soaked up by soil?
- How has the temperature in the area changed since the city was built due to the albedo effect?
- And the list goes on.
So whilst we did find out a lot about the change in soil GHG emissions from paving over soil, the story is not over yet.
Reflecting
I used the R packages terra, ggplot2 and the in-process database management system DuckDB to carry out the analysis. DuckDB was a relatively late addition to the project but was really great. Next time, I would have researched alternate methods to R like the extension to PostgreSQL, PostGIS, as I believe a system like that would have made running the pipeline significantly quicker - particularly if combined with some kind of cloud computing system.
On a non-technical note, next time I do a large project I am going to spend more time dedicated to thinking about parts of the problem with a pen and paper as this seemed to be when my work was at it’s best. With recent personal projects this has made all the difference - and made them more enjoyable too. I have always particularly enjoyed working on problems with a pen and paper.