Continuous color map:
Discrete color map:
i.e. the human eye observes the difference between neighboring colors, not the absolute value of the color. It is possible to identify colors and compare them across one or more figures only when the number of colors used is small.
Image take from https://en.wikipedia.org/wiki/Checker_shadow_illusion
Lecture notes on Information Visualization by Professor Andreas Butz.
Visualization and Visual Communication by Robert Kosara.
UPD: perceptualedge.com by Stephen Few.
Color maps for Science by Kenneth Moreland. Examples, advices and more links.
Making Tufle-Style plots in R. Practical advices.
Junk Charts. Bad visualization and how to improve it.
Hive Plots: new approach to visualize networks. Don’t know if it is effective, but it is definitely interesting.
Blogs devoted to data and visualization. A big list. Some may be interesting: Statistical Graphics and More, chart porn, juice analytics blog
Picture illustrating the information-driven approach:
References in Russian:
Советы по дизайну от Бюро Артёма Горбунова. Советы относятся к дизайну вообще, но некоторые темы будут полезны и для научной визуализации, например: Убрать Все Лишнее, Расписание, Тафти.
According to the single axiom of visualization, a figure should be judged according to its goals. Lets discuss it a little bit more. There are three consequences:
1. A figure may work amazingly in some circumstances but fail miserably in another. Take a look at this wonderful barplot:
Continue reading “On visualization goals”
How to visualize an uncertainty in a time-dependent variable according to the principles of uncertainty visualization?
We have a trajectory is a time-space, but we don’t know exactly where it is. One of the simplest way way to visualize such data is a ‘spaghetti-plot’:
Here each line in Figure is one possible trajectory. This Figure is already very efficient, but it may be influenced by the choice of visualized possibilities. For example:
Continue reading “Visualizing Uncertainty in Dynamic Variables”
This is a navigation sign from a cinema in Helsinki. It means than rooms 6-8 are located on the left, and rooms 9-10 are downstairs.
The original sign was made of fancy neon tubes. But something went wrong and simple A4 papers had to be placed on top of it.
I have described the swine flu data in the previous post, now I’m going to cover the presentation of the results in my paper. The results are a set of posterior distributions. The application details do not matter here. Here is the old picture from my master’s thesis: Posterior distributions were visualized with histograms. The bins in the histogram were not predefined: they were shifted so that the mode of the original distribution would be exactly in the middle of the bin. I used different colors to emphasize that the figure show three parameter families. Now I would sort the rows according to some meaningful attribute and add a grid. Continue reading “SWINE FLU EPIDEMIC IN FINLAND 2009-2011: results”