One reason is that the implementations are much more difficult. In k-d trees you have to deal with the distance from hyperrectangles. It is not something you would write in 30 minutes.
Also k-d trees don't scale much in higher dimensions (https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/...) so in large dimensional problems (the article says 20 is already large) the naive linear search tends to be actually faster. Voronoi is even worse in dimensions larger than 2.
Also k-d trees don't scale much in higher dimensions (https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/...) so in large dimensional problems (the article says 20 is already large) the naive linear search tends to be actually faster. Voronoi is even worse in dimensions larger than 2.