Short Argument for Discretized Color Maps

Continuous color map:
Checker-shadow-illusion

Discrete color map:
Checker-shadow-illusion3

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

Advertisements
Short Argument for Discretized Color Maps

Few Links

General Sources:

Lecture notes on Information Visualization by Professor Andreas Butz.

Visualization and Visual Communication by Robert Kosara.

UPD:  perceptualedge.com by Stephen Few.

Particular Issues:

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:

data-ink

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.

Few Links

Visualizing Uncertainty in Dynamic Variables

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’:
vis0

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”

Visualizing Uncertainty in Dynamic Variables

SWINE FLU EPIDEMIC IN FINLAND 2009-2011: results

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: 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”

SWINE FLU EPIDEMIC IN FINLAND 2009-2011: results