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”
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”