Analyzing statistical relationships between global indicators through visualization

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 indicator-wise 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.