Visualizing Conditional and Unconditional Distributions

  • Contact:

    Dr. Andreas Haupt 

  • Project Group:

    Jun. Prof. Dr.-Ing. Boris Neubert (Computer Graphics Group, KIT)
    Dr. Andreas Haupt

For a long time it is understood that communicating information is more effective by means of visual representations. The project aims offering new tools to communicate research results dealing with that particular problem. A very important and our first field of application is poverty research. Since the early 1990s Germany experienced an increase of its poverty rate from 10% to about 16% today. Poverty is commonly measured as 50% of the median equivalent income. Thus, the poverty rate is a result of the form of the overall income distribution. If the lower part moves to the middle over time, the poverty rate falls and vice versa. Current research suggests this to be a result of economic and demographic changes on different points of the income distribution. These changes refer to complex relations leading to potential misjudgement. While there are well established statistical methods to grasp such influences, there are less developed tools to communicate the results of these models. We claim that to be a piece of a much bigger picture: On the one hand, complex data analysis is going to be much more important within and outside the scientific community, but on the other hand data analytics do not have enough tools to appropriately communicate their results, especially when it comes to complex relations. The project is funded by the Young Investigator Network of the Karlsruhe Institute of Technology.