Note that the last update of the database was 2013. While the percentage of big producers might have not veered off too much, the volume of production would have certainly changed.
We can observe major producers were in Asia, followed by South America and Western Africa. China and India produced more than half of the rice globally.
The net production is often not what’s consumed in a country. One has to factor in import, export, crop variation, usage as animal feed, loss etc, and then it trickles down to food consumption.
What I learnt today is how to make voronoi tree in d3, based on Franck Lebeau’s plugin. This chart form interests me because it’s a combination of two layouts: voronoi and tree. While I also like treemap, a circular voronoi adds some unique advantage. My personal hypothesis is that because treemap is rectangular and sorted, it tends to orient viewers’ attention to the upper left direction; while this layout is circular, it scatters viewers’ attention, allowing people to capture not only the big categories, but also the smaller ones.
In order to make a voronoi tree, I first created a hierarchical data structure, then apply color gradient within each category with d3.interpolate, and added legend using d3.legend.
This is #day86 of my #100dayprojects on data science and visual storytelling. If you like it, please share it. Suggestions of new topics and feedbacks are always welcomed.