Fun site for map nerds or anyone trying to get a grasp on the differences between projections.

via Visualizing Data

I linked to a great post on FlowingData a few weeks ago about some basic rules for visualizations. Nathan then gave a more detailed treatment of his first rule:

I already covered the small handful of rules that pertain mostly to traditional statistical graphics. The first one–to always start your bar charts with a zero baseline–unexpectedly drew some disagreement, and I am unexpectedly compelled to go into more depth.

Great alternatives to a non-zero baseline.

There are a lot of suggestions for visualizations, but only a handful of actual rules.

Mike Bostock has created some really great visualizations of sampling, shuffling, sorting, and maze generation algorithms. He ends with a quick discussion of using vision to think, using his NYTimes interactive graphic “Is It Better to Rent or Buy?” as an example:

To fix this, we need to do more than output a single number. We need to show how the underlying system works. The new calculator therefore charts every variable and lets you quickly explore any variable’s effect by adjusting the associated slider.

via Flowing Data

Strava is a popular app for tracking your runs and rides. But the accumulated data that can be explored through the Strava Labs Global Heatmap is amazing! It is fun to look around and explore my area’s use for rides and runs. (But, I could have told you that 4th St. was bike highway for people leaving town.)

Looks like Strava is now selling accumulated data to cities to help plan better bike routes. Portland is in.

via FlowingData

Not sure where I would ever use it, but I would love to be able to produce these xkcd-style plots in IDL, currently available in Javascript with d3, R, and Python with matplotlib.

An academic paper has been written about this and even provides a Processing implementation. From the abstract:

Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive.

So it appears that a plot that looks like a sketch indicates to viewers that it is not a “final” product.

Datavisualization.ch is keeping a list of the tools that they “use to create interactive and dynamic data visualizations:”

That’s why we have put together a selection of tools that we use the most and that we enjoy working with. We called it selection.datavisualization.ch. It includes libraries for plotting data on maps, frameworks for creating charts, graphs and diagrams and tools to simplify the handling of data. Even if you’re not into programming, you’ll find applications that can be used without writing one single line of code. We will keep this list as a living repository and add / remove things as technology develops. We hope this will help you find the best tool for your next job.

I have mentioned many of these tools like ColorBrewer (many times), Processing, Processing.js, and Many Eyes.

Pathline is a visualization tool for genetic data created in collaboration between designers and geneticists:

We recently attended an interdisciplinary visualization workshop that was all about creating a dialogue between scientists, technologists and designers. It was interesting to discuss the different ways in which these groups think about visualization and how they use it for different purposes. Very bluntly put, each group lacks something another group knows and cares deeply about, be it an understanding of colour [we met in the UK] or an understanding of statistics.

The before and after images show a striking simplification that apparently helps even trained geneticists:

Two main advantages of the new tool were found. First, there was a massive gain in efficiency. The study of a heatmap took up to a half-hour before but can be done at a glance at the curvemap now. More importantly, though, the scientists made new discoveries of gene properties they didn’t know about before. What was hidden in the data before, is now very clear, even to an untrained eye.

I’ve been catching up on Brian Hayes’ great “Computing Science” column in American Scientist. The last issue’s “Pixels or Perish” discusses the move to interactive graphics from static print graphics:

Methods for producing scientific illustrations—and for reproducing them in publications—have been changing. Printing plates for figures were once engraved by hand, then made by a photographic process, and in recent years have been created by digital techniques. Now we are about to turn the page—if not close the book—on yet another chapter in publishing history. After centuries of reading and writing on paper, we seem to be headed for a world where most documents will be distributed online and viewed on a display screen of some kind. How will this transition to a new medium affect the practice of scientific illustration?

via FlowingData