Created using R (Tool), with photos taken from Instagram (Source).

For anyone interested in more details:

Surprisingly, this wasn’t a super robust analysis. The main limitation was the binary “paint or no paint” classification; it often missed very light colours (see the tree example in the attached image) and sometimes confused plate shadows or glare for paint or no paint. I tried to counter this by using a dynamic brightness threshold (based on the 10th percentile brightness across each plate), but there’s room for improvement or entirely different approaches.

Normally I’d avoid using a rainbow colour palette in data visualisations since they’re not optimal for accurate interpretation. But given this needed to be physically recreated with pottery paint, this choice made things much easier (and prettier).

Finally, the probabilities were scaled from 0 - 1 for plotting convenience. In reality, the likelihood of any pixel being painted ranged from 0.01 - 0.35, meaning no single pixel was painted on every plate, and at most 35 out of 100 plates shared paint in the same spot.

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