Composite indicators (CIs) are common measurements and benchmarking tools used to measure multidimensional concepts such as well-being, education and more. Indicators and sub-indicators are selected and combined to reflect a measured phenomenon. Measurement iterations produce a series of time-oriented data, which stakeholders, as well as the general public, might be interested in interpreting. Visualization of a CI is highly recommended, in order to facilitate interpretation and enhance understanding of indicator components and their evolution over time. In recent years, a variety of CI visualizations have been published including various visualization techniques. Indeed, visualizing a CI is a complex and challenging issue, involving many design choices. However, there is a lack of guidelines and methodological approaches for CI visualization design. We suggest a framework that provides a systematic way of thinking of CI visualizations. The framework is intended for two uses: as a design tool when constructing a new CI visualization, and as an analytic tool for systematically describing, comparing and evaluating CI visualizations. The suggested framework is the outcome of both a top-down process, based on CI construction and information visualization literature, and a bottom-up process, in which 35 existing visualization applications of popular CIs were analyzed. We use Munzner’s visualization analysis and design framework (Munzner in Visualization analysis and design, CRC Press, Boca Raton, 2014) in an adaptive way, considering the specific challenges and characteristics of CI visualizations, in order to develop and discuss a systematic view of the data, tasks and methods for visualizing CIs. We demonstrate the use of the framework with a case study analyzing the popular OECD Better Life Index visualization tool.
Bibliographical notePublisher Copyright:
© 2017, Springer Science+Business Media B.V., part of Springer Nature.
- Composite indicator