Understanding relationships between global health indicators via visualisation and statistical analysis

Suresh Lodha, Prabath Gunawardane, Erin Middleton and Ben Crow
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Suresh Lodha: Department of Computer Science, University of California, Santa Cruz, CA, USA, Postal: Department of Computer Science, University of California, Santa Cruz, CA, USA
Prabath Gunawardane: Department of Computer Science, University of California, Santa Cruz, CA, USA, Postal: Department of Computer Science, University of California, Santa Cruz, CA, USA
Erin Middleton: Department of Sociology, University of California, Santa Cruz, CA, USA, Postal: Department of Sociology, University of California, Santa Cruz, CA, USA
Ben Crow: Department of Sociology, University of California, Santa Cruz, CA, USA, Postal: Department of Sociology, University of California, Santa Cruz, CA, USA

Journal of International Development, 2009, vol. 21, issue 8, 1152-1166

Abstract: Several agencies such as World Bank, United Nations and UNESCO are disseminating a large amount of socio-economic data at national level. Various websites such as UC Atlas, Gapminder, CIESIN and NationMaster are attempting to provide general users visualisation tools to display this data. Typical visualisation methods include line graphs, bar graphs, scatter plots, colour-coded glyphs (such as circles) and world maps. In addition to the general public, there is great interest in educational, research and public policy institutes to try to understand the relationships between these socio-economic indicators. In this paper, we juxtapose two techniques to investigate the relationships between global health indicators. The first approach employs sophisticated statistical techniques to develop a causality model between various global health indicators. The second approach, typically employed by the visualisation users of the various websites mentioned above, is to utilise a bivariate display between the health indicators in order to discover relationships between these variables. This visualisation approach is perhaps closest to a bivariate regression or correlation. Therefore, we employ these simple statistical techniques and associated visualisations as well. In this work, we analyse the two approaches using two specific examples related to health indicators. We find that the two approaches sometimes agree strengthening the conclusions or may provide different perspectives that require more careful analysis of the conclusions and need for further research. Copyright © 2009 John Wiley & Sons, Ltd.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jintdv:v:21:y:2009:i:8:p:1152-1166

DOI: 10.1002/jid.1652

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