Visualizing probabilities for a board game

Here is a visualization I did few year ago. The task was to visualize probabilities of successful dice rolls for a board game. For example, wheat would be the probability to get 10+ on two dices, if it is possible to reroll one dice? Or what is the chance to get 8+ by rolling three dices and ignoring the smallest one? These probabilities are not hard to could with the computer. The goals is to present them in a readable format for quick decision making during the game.

Here is how the table looks like. Each number is a probability, expressed in percents.

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So the first thing I did was to show the probabilities with the color, so that the portion of the colored background would correspond to the probability:

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Detail:

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Looks much more readable! But can we do better?

It is not immediately easy to tell whatever a certain probability is larger or lower then half. One have to apply one’s attention to read these values. So I made a second version:

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Detail:

small_stats6

I split the boxes in halves, and fill these halves sequentially. The portion of the colored background still correspond to the probability, but the small datails are more visible now. I like it better!

But what if we go even further? What if instead of splitting boxes into two parts, we split them into six instead?

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Now this may be a step too far. Picture now looks too noisy to me, printed numbers overlap with color bars. I submitter the previous version.

 

 

Visualizing probabilities for a board game

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

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