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

Abstract

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.

Citation

Liu, Y. and Rousseau, R. (2012), "A continuous description of discrete data points in informetrics: Using spline functions", Aslib Proceedings, Vol. 64 No. 2, pp. 193-200. https://doi.org/10.1108/00012531211215204