The ratios you mention are instances of standardized moments or $L$-moments.
Moments in signal processing are similar to moments for physics, and moments in statistics. In physics, the notion of moment is:
an expression involving the product of a distance and another physical
quantity, and in this way it accounts for how the physical quantity is
located or arranged
It can be seems as a generalization of the concept of the center of mass. The mean, the standard deviation, or skewness and kurtosis are derived notions, and they can be computed in any domain, like time or frequency. Basically, the $\alpha$-moment of a function $g$ over domain $D$, around value $c$, is defined in integral form by:
$$m_{g_D}(\alpha,c) = \int_D (t-c)^\alpha g(t)d t$$
or
$$m_{g_D}(\alpha,c) = \int_D [t-c|^\alpha g(t)d t$$
when needed. Classically, for real signals $x$, due to symmetry, in the Fourier domain with $X(f)$, spectral moments are defined with respect to the power normalized energy ($g(\cdot) = |X(\cdot)|^2$)
$$m_\alpha = \int_{f\ge 0} f^\alpha \frac{|X(f)|^2}{\int_{\nu\ge 0} |X(\nu)|^2d\nu}df$$
See for instance: Efficient computation of spectral moments for determination of random response statistics for spectral moments: "Spectral moments are calculated from the one-sided PSD".
Turned into ratios of $L$-moments, they can become scale-fre, unit-free indicators of functions behaviors, including extrema, zero-crossing or sparsity (with $m_1/m_2$ for instance).