This time, related to this post:

Probabilities for action and resistance in Blades in the Dark

This time i tried to link probabilities to 1/6 fractions, as these should be natural for dice users.

I tried to

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# Another Dice Probabilities Chart

# Visualizing probabilities for a board game

# Short Argument for Discretized Color Maps

# Few Links

# Why comparative efficacy figures are the most boring figures ever

# On visualization goals

# Visualizing Uncertainty in Dynamic Variables

# Beauty vs. Utility

# SWINE FLU EPIDEMIC IN FINLAND 2009-2011: results

# Swine flu epidemic in Finland 2009-2011: Data

This time, related to this post:

Probabilities for action and resistance in Blades in the Dark

This time i tried to link probabilities to 1/6 fractions, as these should be natural for dice users.

I tried to

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.

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:

Detail:

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:

Detail:

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?

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.

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

**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:

**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.

In method papers and conferences it is easy to encounter a picture like this:

Continue reading “Why comparative efficacy figures are the most boring figures ever”

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:

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”

In the spring 2009, a new influenza strain A(H1N1)pdm09 aka “swine flu” appeared, causing the world-wide pandemic. Luckily, swine flu happened to be a very mild infection, and the harm was insignificant. I have studied the Finnish branch of the global pandemic since 2010. I’ve made my master thesis about it, published a paper and going to submit another paper soon. This is a story about the data visualization in my upcoming paper. Next post will be devoted to the visualization of the results.

I will start with the data visualization I made 4 years ago for my Master’s thesis:

*(A) The number of new cases per week. The horizontal axis shows the week, from week 18 of 2009 to week 5 of 2010. The data consist of several layers: cases identified with A influenza, cases specifically identified with A(H1N1) influenza; cases identified with A(H1N1) influenza and assigned to hospital. **(B) The total number of cases by age group. **(C) The number of cases per 10000 individuals by age group. **(D) The total number of cases by region. **(E) The number of cases per 10000 individuals by region. *

I still think it was a nice visualization (especially I love the idea of showing absolute values with bars and relative values with ticks), but now I’m able to see a lot of mistakes.

Continue reading “Swine flu epidemic in Finland 2009-2011: Data”