My colleague, Chares Earl, pointed me to this interesting 2010 post that explores different ways to visualize categories of drastically different sizes.

The post author, Tom Hopper, experiments with different ways to deal with “Data Giraffes”. Some of his experiments are really interesting (such as splitting the graph area). In one experiment, Tom Hopper draws bar chart on a log scale. Doing so is considered as a bad practice. Bar charts value (Y) axis must include meaningful zero, which log scale can’t have by its definition.

Other than that, a good read Graphing Highly Skewed Data – Tom Hopper

Uncertainty is one of the most neglected aspects of number-based communication and one of the most important concepts in general numeracy. Comprehending uncertainty is hard. Visualizing it is, apparently, even harder.

Last week I read a paper called Value-Suppressing Uncertainty Palettes, by M.Correll, D. Moritz, and J. Heer from the Data visualization and interactive analysis research at the University of Washington. This paper describes an interesting approach to color-encoding uncertainty.

Value-Suppressing Uncertainty Palette

Uncertainty visualization is commonly done by reducing color saturation and opacity.  Cornell et al suggest combining saturation reduction with limiting the number of possible colors in a color palette. Unfortunately, there the authors used Javascript and not python for this paper, which means that in the future, I might try implementing it in python.

Two figures visualizing poll data over the USA map, using different approaches to visualize uncertainty

 

Visualizing uncertainty is one of the most challenging tasks in data visualization. Uncertain

 

via Value-Suppressing Uncertainty Palettes – UW Interactive Data Lab – Medium