Analyzing statistical relationships between global indicators through visualization

Abstract

There is a wealth of information collected about national level socio-economic indicators across all countries each year. These indicators are important in recognizing the level of development in certain aspects of a particular country, and are also essential in international policy making. However with past data spanning several decades and many hundreds of indicators evaluated, trying to get an intuitive sense of this data has in a way become more difficult. This is because simple indicatorwise visualization of data such as line/bar graphs or scatter plots does not do a very good job of analyzing the underlying associations or behavior. Therefore most of the socio-economic analysis regarding development tends to be focused on few main economic indicators. However, we believe that there are valuable insights to be gained from understanding how the multitude of social, economic, educational and health indicators relate to each other.The focus of our work is to provide an integration of statistical analysis with visualization to gain new socio-economic insights and knowledge. We compute correlation and linear regression between indicators using time-series data. We cluster countries based on indicator trends and analyze the results of the clustering to identify similarities and anomalies. The results are shown on a correlation or regression grid and can be visualized on a world map using a flexible interactive visualization system.This work provides a pathway to exploring deeper relationships between socio-economic indicators and countries in the hands of the user, and carries the potential for identifying important underpinnings of policy changes.

Understanding relationships between global health indicators via visualisation and statistical analysis

“…This example illustrates that the idea of a cluster analysis coupled with the causality model analysis is likely to be useful in grouping the countries on the basis of the level or dynamics of variables included in a database, such as GHND. A slightly more detailed application of cluster analysis for the grouping of the countries has been reported by the authors elsewhere (Gunawardane et al, 2009). …”

“…In contrast, correlation and bivariate regression coefficients use 2D data as the first step, for example, computing correlations between two variables along time for one country, and then extending this analysis to every country. The difference between these two approaches by taking different slices of the three-dimensional data-space, time and indicator-is described below and is further explored in greater detail by the authors elsewhere (Gunawardane et al, 2009).…”