We introduce novel information-entropic variables -- a Point Divergence Gain (${ \Omega}^{ (l \rightarrow m)}_\alpha$), a Point Divergence Gain Entropy ($I_\alpha$), and a Point Divergence Gain Entropy Density ($P_\alpha$) -- which are derived from the R\'{ e}nyi entropy and describe spatio-temporal changes between two consecutive discrete multidimensional distributions. The behavior of ${ \Omega}^{ (l \rightarrow m)}_\alpha$ is simulated for typical distributions and, together with $I_\alpha$ and $P_\alpha$, applied in analysis and characterization of series of multidimensional datasets of computer-based and real images.
Categories: physics.data-an