Is It Wise to Use Trivariate Map for Accommodating Three Information into one Visualization?

I was wondering if I could add one more variable to my previous bivariate map, and here they are: triangulating 1) Population Density (represented by point size); 2) Kilometers traveled per year by people who travel to work by Car or Van (gold color); and 3) Total carbon footprint per person in kgCO2e (blue color) into a trivariate map!

However, the visualization process was a bit tricky, considering MAUP and finding a balance between the precision and intuition of the map. That’s why I created two versions of the map with different mapping units: 1) using given mapping units (i.e., LSOAs) and 2) converting the LSOAs into Hexagonal Grids. The results are as seen in the figures.

Centroids by LSOAs

Centroids by Hexagonal Grids

Personally, it was quite challenging to interpret the map in a short period of time. It required careful consideration to understand what is happening with these three variables. Despite the complexity, I believe these maps are still interpretable enough to explain the correlation of higher dimensions of variables in one piece of visualization, which is a significant discovery for me personally.

While taking my time to create these maps, I wondered if there was any other way to visualize a trivariate map that is more intuitive. Additionally, is it possible to enhance a tri map into a quad map? I will leave these questions for another time.

Project code: https://github.com/ofitrahramadhan/trivariatemap_R Data source: https://www.carbon.place/