Metro Los Angeles has put together kind of a nice graphic to communicate the status of a tunnel construction project. It’s cartoonish, and yet contains a surprisingly large amount of scientific and engineering information.
I like this post on R bloggers proposing several alternatives to word clouds. I’ll list them below but really, you should look at the pictures because hey, this is about pictures.
- circle packing (basically this replaces the words with circles, dealing with the problem of bigger/longer words appearing to be more important in standard word clouds); there is a variation on this called the “horn of plenty” where the circles are arranged in order rather than randomly
- cartogram (in my ignorance, I have been calling this a “bubble map”. I have used these frequently to show engineering model results and find they work well for many people)
- chloropleth (these shade in geographic areas to convey data. I find these work well if the size of the geographic area is important information. If it is not, these tend to draw the viewer’s eye to larger areas, and in that case the bubbles are better. For example, per-person income of Luxembourg vs. China.)
- treemap (I’ve been calling these “packed rectangles” and I generally find them good for anything where conveying relative magnitudes of things to people is important)
- donuts (surpringly, the author concludes a donut is the best option for the data he is trying to show and I kind of agree, it gets the point across and leaves lots of room for labels)
The article has links to the specific packages and code used to create the graphics.
This R-bloggers post shows you how to recreate the famous Sankey diagram of Napolean’s invasion of Russia. And even how to improve it by overlaying it on a modern satellite image.
Here’s a wiki post about Edward Tufte’s data-ink ratio:
Tufte refers to data-ink as the non-erasable ink used for the presentation of data. If data-ink would be removed from the image, the graphic would lose the content. Non-Data-Ink is accordingly the ink that does not transport the information but it is used for scales, labels and edges. The data-ink ratio is the proportion of Ink that is used to present actual data compared to the total amount of ink (or pixels) used in the entire display. (Ratio of Data-Ink to non-Data-Ink).
Good graphics should include only data-Ink. Non-Data-Ink is to be deleted everywhere where possible. The reason for this is to avoid drawing the attention of viewers of the data presentation to irrelevant elements.
The goal is to design a display with the highest possible data-ink ratio (that is, as close to the total of 1.0), without eliminating something that is necessary for effective communication.
Before I offer an opinion, I should state the disclaimer that you should definitely listen to Edward Tufte, not me! So here’s my opinion: this idea is clearly absurd when taken to extremes because it would just mean a bunch of dots on a page that you have no way of interpreting. I can’t think of a way of making graphs without axes, scales, and a legend. Labels, arrows, and text boxes are an alternative which I find myself using often when giving projected slide presentations in fairly large rooms.
A reasonable interpretation of Tufte, I think, is to ask yourself whether each new thing you are adding to a graph provides useful information to the reader/viewer, increases the chances that the reader/viewer will draw the right conclusions, and makes the reader/viewer’s job easier or harder. The holy grail is to help your audience imbibe the point of the graph with very little effort. Unnecessary 3D effects and clip art aren’t going to do that. A splash of color and some nice big labels that middle aged people can read from the back of the room just might help.
Hans Rosling, who gave a famous TED talk on poverty and economic growth featuring animated bubble plots in 2006, past away on February 7. Here is Bill Gates remembering him.
Here is an interesting paper proposing design principles for flow maps, which “visualize movement using a static image and demonstrate not only which places have been affected by movement but also the direction and volume of movement.”
Design principles for origin-destination flow maps
Origin-destination flow maps are often difficult to read due to overlapping flows. Cartographers have developed design principles in manual cartography for origin-destination flow maps to reduce overlaps and increase readability. These design principles are identified and documented using a quantitative content analysis of 97 geographic origin-destination flow maps without branching or merging flows. The effectiveness of selected design principles is verified in a user study with 215 participants. Findings show that (a) curved flows are more effective than straight
flows, (b) arrows indicate direction more effectively than tapered line widths, and (c) flows between nodes are more effective than flows between areas. These findings, combined with results from user studies in graph drawing, conclude that effective and efficient origin-destination flow maps should be designed according to the following design principles: overlaps between flows are minimized; symmetric flows are preferred to asymmetric flows; longer flows are curved
more than shorter or peripheral flows; acute angles between crossing flows are avoided; sharp bends in flow lines are avoided; flows do not pass under unconnected nodes; flows are radially distributed around nodes; flow direction is indicated with arrowheads; and flow width is scaled with represented quantity.
Here’s an interesting article from the New York Times on all the ways it has tried to present election results visually over the years.
Canva has a helpful article with links to a large number of sources of free visuals – photos, videos, even Infographics. There is more than just Google Images and Youtube out there. There is even more here than it seems like at first because as you drill down some of the links are to additional lists…of lists…of…you get the idea.
Yes, you can make radar plots with R!
This week I discovered several websites that show you cool snapshots of current weather. My colleagues are laughing at me because apparently I am the last to know. I think this is one example of how a complex visualization can sometimes be much better than a simpler one. Compared to the typical “synoptic” maps of warm and cold fronts, which are confusing to most people, this is something I think even an elementary school student could begin to grasp.