A Conceptual Framework for Visualizing Composite Indicators
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.
KeywordsFramework Visualization Composite indicator
This research was done as part of the National Israel ICT project by The Center of Internet Research (http://infosoc.haifa.ac.il) supported by the Israel Internet Association-ISOC-IK; the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation (1716/12); and the Samuel Neaman Institute for National Policy Studies.
- Albo, Y., Lanir, J., Bak, P., & Rafaeli, S. (2016b). Static vs. dynamic time mapping in radial composite indicator visualization. In Proceedings of the International Working Conference on Advanced Visual Interfaces (pp. 264–271). ACM.Google Scholar
- Andrienko, N., & Andrienko, G. (2006). Exploratory analysis of spatial and temporal data: A systematic approach. Berlin: Springer Science & Business Media.Google Scholar
- Bandura, R. (2008). A survey of composite indices measuring country performance: Update. Office of Development Studies, New York: United Nations Development Programme.Google Scholar
- Barzilai-Nahon, K., Rafaeli, S., & Ahituv, N. (2004). Measuring gaps in cyberspace: Constructing a comprehensive digital divide index. In Workshop on Measuring the Information Society, the conference of Internet Research (Vol. 5).Google Scholar
- Bertini, E. (2011). Review: OECD’s Better Life Index. Retrieved September 23, 2015 from http://fellinlovewithdata.com/reviews/review-better-life-index.
- Burch, M., & Weiskopf, D. (2014). On the benefits and drawbacks of radial diagrams. In W. Huang (Ed.), Handbook of human centric visualization (pp. 429–451). New York: Springer.Google Scholar
- Colecchia, A., Pattinson, B., & Atrostic, B. (2000). Defining and measuring electronic commerce. Document de discussion de la DSTI/OCDE.Google Scholar
- Dutta, S, & Bilbao-Osorio, B. (2012). The Global information technology report 2012: Living in a hyperconnected world. World Economic Forum.Google Scholar
- Few, S. (2011). Data blooms in beauty and truth. Retrieved September 23, 2015 from http://www.perceptualedge.com/blog/?p=1044.
- Fuchs, J., et al. (2013). Evaluation of alternative glyph designs for time series data in a small multiple setting. In Proceedings of the SIGCHI conference on human factors in computing systems.Google Scholar
- Google Public Data Explorer. Retrieved January 15, 2016: https://support.google.com/publicdata/answer/1100640?hl=en.
- Gnaldi, M., & Ranalli, M. G. (2015). Measuring University performance by means of composite indicators: A robustness analysis of the composite measure used for the benchmark of Italian Universities. Social Indicators Research, 129(2), 1–17.Google Scholar
- Index, L. P. (2014). The 2014 Legatum Prosperity Index.Google Scholar
- ITU. ICT-Eye. Retrieved September 23, 2015 from http://www.itu.int/net4/itu-d/icteye/.
- Joint Research Centre-European Commission. (2008). Handbook on constructing composite indicators: Methodology and User guide. OECD publishing. http://www.keepeek.com/Digital-Asset-Management/oecd/economics/handbook-on-constructing-composite-indicators-methodology-and-user-guide_9789264043466-en#page42.
- Keim, D. A., Schneidewind, J., & Sips, M. (2004). CircleView: A new approach for visualizing time-related multidimensional data sets. In Proceedings of the working conference on advanced visual interfaces (pp. 179–182). ACM.Google Scholar
- Kelvin, W. T. (1883). Electrical units of measurement. Popular Lectures and Addresses (1889), 1, 80–81.Google Scholar
- Nardo, M., Saisana, M., Saltelli, A., & Tarantola, S. (2005a). Tools for composite indicators building. European Commission Joint Research Centre. Institute for the Protection and the Security of the Citizen, Econometrics and Statistical Support to Antifraud Unit, I-21020 Ispra (VA) Italy, Report number: EUR, 21682.Google Scholar
- Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005b). Handbook on constructing composite indicators. Berlin: Springer.Google Scholar
- Perin, C., Vuillemot, R., & Fekete, J. D. (2014). A table!: Improving temporal navigation in soccer ranking tables. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 887–896). ACM.Google Scholar
- Plaisant, C. (2005). Information visualization and the challenge of universal usability. In J. Dykes, A. MacEachren & M. J. Kraak (Eds.), Exploring geovisualization (pp. 53–82) Oxford: Elsevier.Google Scholar
- OECD Better Life Index. Retrieved April 29, 2016 from http://www.oecdbetterlifeindex.org/.
- Saisana, M. & Tarantola, S., (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Citeseer.Google Scholar
- Sciadas, G. (2004). International benchmarking for the information society. In ITU-KADO digital bridges symposium.Google Scholar
- Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In IEEE symposium on visual languages, 1996. Proceedings (pp. 336–343). IEEE.Google Scholar
- UNDP (2015). Human Development Index (HDI). Google Public Data Explorer. Retrieved September 23, 2015 from http://hdr.undp.org/en/data-explorer.
- Wehrend, S., & Lewis, C. (1990). A problem-oriented classification of visualization techniques. In Proceedings of the 1st conference on Visualization’90 (pp. 139–143). IEEE Computer Society Press.Google Scholar
- World Wide Web Foundation. Web Index. 2014. Retrieved September 23, 2015, from http://thewebindex.org/data/?indicator=INDEX&country=ALL.