A continuous description of discrete data points in informetrics : Using spline functions

Purpose – The paper aims to propose the use of spline functions for the description and visualization of discrete informetric data. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – The major disadvantage of the use of splines is that accurate data are essential. Practical implications – Spline functions can be used for illustrative as well as modelling purposes. Originality/value – Splines have hardly ever been used or studied in the information sciences.