The dB scale is very common, useful, and easy to understand over a large range of amplitudes for very linear systems. Resonance plots are an example where data is unreadable without and easily readable with log plotting. Wide ranges of delta% (from unity=100%) seem like another good choice.
What this means in practice is that human senses are a logarithm of the input. Because hearing is a logarithm of the input, we deal with the perception of sound in exponential quantities of sound.
That's not very relevant to this university chart, though. For a simpler example, stock price charts are sometimes logarithmic. On such a chart, if I buy a constant dollar amount and then sell it, I'll make the same dollar gain or loss for any buy and sell points the same vertical distance apart. I believe this chart's creator was thinking of an analogous property; the labels are placed in pairs geometrically equidistant from 0%, since (1 + 0.25)*(1 - 0.2) = (1 + 0.5)*(1 - 0.333...) = 1. I believe the rounding to 34% and not 33% is a mistake, but that the scale is otherwise fine.
log-log graphs are not the same as a log graph. A log-log graph is useful for turning monomials into lines, which is exactly what a Bode plot is. But this is a good point. Maybe "exponential function" is the wrong terminology. "Function with an exponent" is more apt.
> stock price charts are sometimes logarithmic.
Stock price charts are logarithmic when the movement of a stock is exponential over the time frame being looked at. Stock prices, in general, are kind of related to perception as well. A $4 stock that moves +/- $2 is perceived very differently than a $100 stock that moves +/- $2. The perception of a price delta is relative to the current price, not the absolute dollar amount of the movement. This is why stock prices exhibit compound returns, which is of course an exponential function.
I generally prefer log units to linear percentages for anything like a scale factor or a ratio. A lot of stuff comes out cleaner and more symmetric, for example because (1 + 0.10)*(1 - 0.10) isn't equal to one exactly, but exp(0.1)*exp(-0.1) is. The case for that in the university chart seems slightly pedantic but fine to me.
What's more natural: looking at a change relative to 0, or looking at a change relative to all the other points? Personally, I think looking at a change relative to 0 makes much more sense. Trying to figure out the meaning of the points by looking at relative values is garbage, because there is no underlying exponent to justify the use of the logarithm.
What if it were reversed: what if the number of students dropped by 20% one year, and then when up by 25% the following year. How many students are now enrolled in my course compared to the beginning in this scenario?
That is, I compute the new size after adjusting by Y% first, and then take Z% of that. To me, this is still treating values as magnitudes from 0 and the Y% change is totally independent from the Z% change. I guess you can multiply Y and Z first, but that’s not how I think about it.
If the original author's had included a brief explanation of the unexpected looking axis label and its advantages, the whole problem would have been avoided. And I think the argument that "log scales are standard" is not a valid defense here, as this whole thread demonstrates.