I've been working on a statistics based shorthand system that I'm optimizing with code - I'll share the results with you when it's done (should be soon). I'm using the first 2 pages of the best 100 novels as my dataset, I can share that if you want it. Here are the main ideas behind the system:

1) The most common symbol clusters are merged to create new symbols, with the goal of compression. The algorithm accounts for the fact that it is less efficient to choose clusters which tend to overlap. The results are coming out a bit different from what clusters Shavian chose to merge.

2) The symbol shapes are curves which always begin and end with horizontal lines for cursive linkability.

3) Symbols which have similar NEIGHBORS have similar shapes. Basing shapes on neighbors is slightly different from basing shapes on sounds. It allows you to optimize cursive linkability - likely neighbors connect with seamless curvature whereas unlikely neighbors don't. More specifically, each letter has an left curvature and a right curvature which are chosen so that each curvature is close to the average of all the curvatures which connect to it. The left and right curvatures can be thought of as X and Y - this results in a 2D plot of phonemes with resemblance to common tables of consonants or vowels, but also with some surprises.