The paper aims to propose the use of spline functions for the description and visualization of discrete informetric data.
Interpolating cubic splines: are interpolating functions (they pass through the given data points); are cubic, i.e. are polynomials of third degree; have first and second derivatives in the data points, implying that they connect data points in a smooth way; satisfy a best‐approximation property which tends to reduce curvature. These properties are illustrated in the paper using real citation data.
The paper reveals that calculating splines yields a differentiable function that still captures small but real changes. It offers a middle way between connecting discrete data by line segments and providing an overall best‐fitting curve.
The major disadvantage of the use of splines is that accurate data are essential.
Spline functions can be used for illustrative as well as modelling purposes.
Splines have hardly ever been used or studied in the information sciences.
