I quickly uttered my surprise at the presence of such a poorly designed chart — esp. in such a high profile online publication — in a snarky Twitter comment, and soon after, Robert Kosara posted a whole blog post defending the graphic, and calling for “a bit more subtlety in our criticism”.

Well, I am not sure if Twitter was optimized for subtlety, yet, I guess I should clarify a bit the background of my judgement (especially since Robert’s speculative assumptions about my train of thought is not accurate in all points).

The chart in question shows the amount of a certain soccer player’s presence in different areas of the field. The field is divided in cells, and in each cell, a little “tower” indicates by height and color the amount of the player’s presence in that cell. Essentially, this makes it a hybrid of a heatmap and a 3D bar chart overlaid over a soccer field.

The redundant encoding (i.e., in this case, using height and color to encode the same value) is nothing bad per se, and in this case quite justified, as both the color encoding as well as the 3D bars height alone would be too weak visual variables for the data.

The 3D-y-ness of the chart? I am not fond of it. I find it a very clear case of “Hmm, this looks a bit bland. Maybe we should tilt it a little? Ooh look, how awesome.” Frankly, to me this is just childish. Let me put it this way: Bacon is a legitimate ingredient to many dishes, and can be quite tasty, when used right. But if your cooking style is to start with cooking something bland, and then add bacon to make it less bland, then, trust me, you are not a great cook. A great cook makes a feast out of a simple egg, they say, and I think this is what we should aspire to.

The arrow? Well, it serves its purpose, but it is quite loud, isn’t it. The missing legend, title, and description of the data and its transformations? Why bother? We have a 3D chart!

Anyways, all of that is not that grave, maybe even nit-picking, but the one thing that is unforgiveable about the chart is the color palette. If you do a heatmap, there is basically only one thing you need to get right, and this is the color palette. Yet, this one has been given very little love.

Generally, using a green, yellow and red gradient could be justified when we have benefits from a “traffic lights” reading. But I cannot see how this would apply in our case. This leaves us with the screaming dissonance of the complimentary primary colors used in full saturation, lacking any difference whatsoever in value or saturation. I hope we don’t need to discuss the aesthetic shortcomings of this approach.

Conceptually, things fall apart more, if we look closely, as there is a huge gap between no (zero) and quite little presence (1 in the supposed scale above) in a cell: Of course, I understand that this is due to the green being the color of the playing field, but why not work with that self-imposed constraint, instead of just ignoring it?

Lastly, here is how the gradient looks desaturated:

You might say, this is not an issue, as the color hue carries the information, but be reminded that a good proportion of our population is in fact red-green blind, and also for the others, key to establishing contour and depth in an image is to work with brightness contrast.

Update: Mike reminds us in the comments that red-green blindness is quite different from just not seeing the respective color hues, which is correct. I did indeed run a test on the image on vischeck.com, and here is the result:

Well, to end on a more positive note — how could we fix this?

Starting with the colors, here is the lowdown: the recommended approach for encoding “little to high amount” in a color palette is to use small variation in color hue and combine it with a higher variation in brightness (see, e.g. Stephen Few’s color primer). In our case, we might want to stick with the green of the playing field, but rather go into a darker, blue-isher direction for the higher intensities of the data, achieving a harmonious palette. Second, we will group the data into a smaller number of bins, to increase separability and emphasize the fact that the exact numerical measurement is not the point of the chart, but the overall patterns. This could result in a palette like this:

Moving to the heatmap itself, I found the 3D blocks emphasize the flaws of the measurement process over the information we want to measure. There is nothing blocky, or square about the soccer player’s movement, it is just an artefact of the data gathering and representation chosen. In a perfect world, we could measure the player’s position to the inch, each single second, [edit] resulting in which we could use to model [end edit, thanks Mike, for spotting my inaccuracy here] a smooth 3D manifold instead of the blocks. One way to approximate this could be to smooth the data, and separate it with isolines into regions with a similar intensity. This allows us to focus on the resulting (estimated) topology, instead of the measurement process:

(Note: This is just a mock-up, as I did not have access to realistic data.)

As a bonus, this image works in very, very small, too, as well as in black and white (these two tests are quite effective, in my opinion):

I am not claiming that this is the perfect solution, there is a myriad ways to work with this data. It is just a quick sketch. But at least, I can justify the design choices I made quite well, and I hope I could demonstrate that if our only goal is merely to “do no harm”, and not to try and make the best choices possible, we are missing out. And remember: don’t eat too much bacon, dear people. Thanks for your attention.

Palin sentence diagrams

Two projects from Pitch Interactive

Job arcs

Donations

Neoformixhas a couple of interesting visualizations, mostly relating to speech analysis. I like this one best, as it really tells a compact story:

Stream graph: Obama’s one week speech

The NY Times info design department does not cease to amaze me:

The measure of a president

Can a president tame the business cycle?

Naming names

More lexical analysis and the windbag index from Martin Krzywinski:

Lexical Analysis of 2008 US Presidential and Vice-Presidential Debates — who’s the Windbag?

Every moment now

Great discussion on proper plotting of the candidates’ tax plans (via Nature where I picked up the great term “couture visualization”)

The Visual Display of Political Information

Another vivid discussion on displaying swing state dynamics at eager eyes:

Swing states

And now, my dear American readers – get out and vote! May the youngest and best-looking win :)

About his latest project, Running the numbers, he writes:

Running the Numbers looks at contemporary American culture through the austere lens of statistics. Each image portrays a specific quantity of something: fifteen million sheets of office paper (five minutes of paper use); 106,000 aluminum cans (thirty seconds of can consumption) and so on. My hope is that images representing these quantities might have a different effect than the raw numbers alone, such as we find daily in articles and books. Statistics can feel abstract and anesthetizing, making it difficult to connect with and make meaning of 3.6 million SUV sales in one year, for example, or 2.3 million Americans in prison, or 32,000 breast augmentation surgeries in the U.S. every month. This project visually examines these vast and bizarre measures of our society, in large intricately detailed prints assembled from thousands of smaller photographs. Employing themes such as the near versus the far, and the one versus the many, I hope to raise some questions about the role of the individual in a society that is increasingly enormous, incomprehensible, and overwhelming.

David Huynh has recently joined the freebase team, after having worked on Exhibit and other SIMILE tools at MIT. His new project Parallax is obviously based on Exhibit (which followed mostly a faceted filtering paradigm) but demonstrates a really interesting “sidewards browsing technique” for navigating related sets of different types of entities.

As an example, you could start with a set of architects, then filter down to all modern architects, plot them on a map, a timeline etc. – quite nice already, but traditional facet browsing in principle. The catch however, is that you can explore related collections, like the buildings they designed, their birth places etc. in the same manner. Very interesting principle and nicely executed, yet a bit hard to explain.

In this screencast, David explains it himself:
Freebase Parallax: A new way to browse and explore data from David Huynh on Vimeo.

As a side remark: academically, I think the Humboldt paper by Georgi Kobilarov first presented this principle (but they also refer to an earlier prototype of David’s work). Unfortunately it was introduced under the name of pivot browsing, which is sort of reserved already for the quite related, but not identical principle introduced in dogear.

Any ideas for a good name? Sidewards browsing? Entity shift? Or just stick with parallax?

Essentially, the waveforms of a musical piece by Frans de Waard were rendered as a sculpture with a CNC Milling Machine.

This project sort of follows a week in the life, another physical visualization, where a week of location data of the author is mapped in a wooden cartogram.

I just finished reading Bill Buxton’s Sketching User Experiences and it had quite an impression on me. It starts with a general, broad argument on the role of design thinking in business and product development, illustrating how design, design thinking and design artifacts are not yet well enough integrated and understood in technology business. A great introduction also for non-designers, including a fantastic discussion of the iPod design and business story. For design practitioners, the main part of the book is concerned with the activities of sketching and prototyping. His main argument is that these two concepts are often used interchangeably, however serve two very different purposes: Sketches are for getting the right design , developing the basic idea, the punchline of the design project. Sketches are quick, disposable, diverging, and abundant. Prototypes (as well as usability testing, mock-ups, etc.) are for getting the design right — narrowing down the possibilities, making decisions, just doing what it takes to get from an idea to a really well designed thing. To experienced designers, this might sound quite obvious, but I have to admit myself I did not reflect properly on how I use these words, and how I use these design tools. Buxton did a great job of introducing subtle distinctions in this area, and gives you lots of different angles and examples to get it. Great stuff. Obligatory reading for designers, and highly recommended for anyone who has designers around them – I promise you will understand them much better afterwards :)

The “Tagging” book by Gene Smith is out. I am still awaiting a paper copy, but had a chance to look at the online version already. It looks really comprehensive, concise and covering all important tagging concepts. Which is not easy for such a moving target topic.

Besides, my elastic tag maps visualization is featured on p.102f. Nice!

A real wow-project has gone into version 2: Exhibit. It is part of SIMILE, focussing on “Semantic Interoperability of Metadata and Information in unLike Environments”, which provides a whole toolbox of pragmatic semantic web applications.

Exhibit itself presents a “a three-tier web application framework written in Javascript, which you can include like you would include Google Maps.” An exhibit application typically consists of a content presentation area and several widgets for filtering, sorting and grouping the content presentation, following the faceted browsing paradigm also used in my elastic lists. Currently, the available widgets cover plain checkbox lists, maps, and timelines, and a live search.

From my first experiments with the tool, I can say it is really ridiculously easy to create custom views on existing data sets: Data can be imported “from a Google Spreadsheet, Excel spreadsheet, EditGrid spreadsheet, BibTex files or any JSONP data source” without much effort.

Applications are built by loading the exhibit script and then assigning “Exhibit roles” to layers in your java-script: For instance,

<div ex:role="facet" ex:expression=".discipline"
ex:facetLabel="Discipline" />

will create a filtering widget looking for values of the “discipline” property in your data set and display them as a list to be used for filtering. It couldn’t possibly be easier to build client-side facet browsing applications.

Other nifty are the integrated browser history for filtering steps and the ability to export filtered views as e.g. HTML, but also tab-separated text files, RDF, etc.

Check out the Getting started page to get an impression.

The down side, of course is, that all data has to be loaded on the client before the application can start. So we are rather speaking about hundreds than thousands of data items. Also, I would of course be interested in building custom widgets, however, this looks a wee bit more complicated from first looks into the code. And of course UI-wise, I would have some suggestions as well, but anyways, I find the project quite impressive already as it is!

Kudos to David Huynh and the rest of the team at MIT. More info can also be found in David Huynh’s PhD thesis.

Thanks to Till for the tip!