Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization
IEEE Transactions on Visualization and Computer Graphics
Harnessing Natural Textures for Multivariate Visualization
IEEE Computer Graphics and Applications
Data Mining with C4.5 and Interactive Cartographic Visualization
UIDIS '99 Proceedings of the 1999 User Interfaces to Data Intensive Systems
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Two-Tone Pseudo Coloring: Compact Visualization for One-Dimensional Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Temporal Visualization of Planning Polygons for Efficient Partitioning of Geo-Spatial Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Interactive linked micromap plots and dynamically conditioned choropleth maps
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Visualization of Geo-spatial Point Sets via Global Shape Transformation and Local Pixel Placement
IEEE Transactions on Visualization and Computer Graphics
Worldmapper: The World as You've Never Seen it Before
IEEE Transactions on Visualization and Computer Graphics
Geographically Weighted Visualization: Interactive Graphics for Scale-Varying Exploratory Analysis
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
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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.