Startle wayfinding

Axel Peemoeller’s wayfinding system for the Melbourne Eureka Tower Carpark has been making the internet rounds. Props to him, it’s a novel and eyecatching design. (See below for one example from his site.) But something about it makes me think it’s disorienting (and possibly dangerous) for drivers. Let me try and articulate my amateur cognitive science/interaction design theory to explain.

While driving, your brain’s 3D systems are in high gear. (Pardon the pun.) Your mind is tuned to look for positioning cues such as occlusion, parallax, and especially size changes. This last is most important, as your visual system is on the lookout for anything that suddenly grows larger than the things around it, which would be a clear sign that you’re about to hit something. It’s called the startle response, and it happens within about 80 milliseconds, far too fast for any rational processing to counteract it.

So now, think of yourself in the Eureka Tower Carpark. Turning a corner, you’re a little confounded by the strange and lovely colored shapes on the wall. What’s going on here? All of a sudden, your visual system puts all these shapes together in a way that could only make sense if there was something (in this case, typography) jumping out right in front of you. Your gut reaction should be to slam on the brakes, even if your logical brain can decipher the thing a few milliseconds later. Hopefully the driver behind you left enough room.

So I haven’t been there, and I don’t know if this conjecture bears out in fact, but the pictures certainly set off my startle reaction.

The power of rich visual modeless feedback

One of my favorite aspects of product design is the feedback mechanism. When I think of feedback, I think fundamentally about the car dashboard. Nearly every action that a driver makes in a car is responded to with one or more forms of feedback whether audible, tactile or visual.

When turning into a left lane, the driver will (hopefully) use the turn signal lever to indicate the change of lanes. Pulling the lever anti-clockwise will activate the turn signal on the exterior of the car, but will also offer the following feedback:

• Audible: The dashboard will emit a clicking sound
• Visual: A green arrow will flash on and off in the dashboard
• Tactile: The lever will click or nudge over

All of these feedback mechanisms work in tandem to communicate with the driver that the turn signal is active and working. As a side note, if you’ve ever activated your turn signal and it emitted a clicking sound at double the normal rate, it usually means that one of your lights is dead (this is considered negative audible feedback). That’s great design when you consider how impossible it would be to turn on your signal indicator, get out of the car, run around it to check all the lights are working and then jump back in again, all at 30 miles an hour!

Continue reading...

VizFarm: Visualization jamboree

Last night a couple of us made it out to the VizFarm, July's installment of the incredibly successful IxDA San Francisco monthly event. The format was interesting: 19 presenters, speaking for 5 minutes each on a single visualization or visualization-related project. The brevity of the presentations was reminiscent of Pecha Kucha and certainly served to keep things moving and provide for a serious diversity of material, even if I wished I could hear a bit about some of the projects. (Also, I should say fellow Cooperista Chris Noessel and myself were both presenters and we certainly found it easier to prepare for this than a longer format. This is a good way to encourage participation from a community.)

The visualizations described topics included genetic sequencing workflow, Grand Prix motorcycle racing results, air-traffic control, correlations between deforestation and carbon emissions, as well as between transit times and home prices, and of course, the slightly self-referential but always enjoyable topic of uncovering meaning in qualitative design research.

A recurring theme in many of the presentations was how visualizations can help to uncover answers to complicated questions like "Where are there opportunities to reduce the amount of time it takes to sequence a human genome?" or "Where should I be looking if I want to buy a home that costs under £500k and is within an hour commute to central London?" By structuring the data and display in the right way, we can start to rely on people's abilities to recognize visual patterns to see complex situations in new ways.

Photo: Many Eyes

Of course, readers of Tufte will be familiar with a lot of this — in an academic sense, at least. But there's a huge challenge in making these useful to those who are less familiar with infographic conventions. To address complex questions in a visualization, the creator must communicate the utility of the levers and dials to "readers" at a variety of levels, which require a certain degree of visual and quantitative literacy, and can (potentially) further burden the display/interface.

Martin Wattenberg and Fernanda B. Viégas have made an attempt at this with IBM's Many Eyes project, the goal of which is to "democratize visualization and to enable a new social kind of data analysis." (To avoid any confusion, I'll mention that Many Eyes was not part of the proceedings last night.)

What do you think? Does something like this stand to help citizens better understand the world they live in, without the slant and filter of news organizations? What can we do to provide these new ways of seeing things to audiences unaccustomed to reading data visualizations?

Seeing patterns in research findings

We’re always on the lookout for engaging ways to communicate the patterns we uncover in our research. What factors cluster into significant groups? What are relevant attributes and relationships? What trends do we see?

Shan Carter and Amanda Cox at the New York Times recently produced a fantastic interactive chart highlighting the voting patterns along several demographic factors in the Democratic primaries. (You can read more about this graphic from Shan Carter here.)

I love the idea of starting with this approach and overlaying additional factors to draw out relationships and relative importance. In the Times example, imagine the squares drawn in relative proportion to the number of delegates in play; color and saturation representing the percentage of Democratic votes in the 2004 presidential election. Combining multiple factors does complicate the visual, so care must be taken to preserve the clarity that makes it so effective.

At Cooper, we often do something similar, with behavioral variables of interview subjects plotted along major axes, combined with demographics like age, organization type and role, to paint a picture of the interrelated web that helps us make meaning of a diverse human population. We always try to walk through these visualizations with a story that ascribes meaning to the observations, but providing clients (and ourselves) with an opportunity to interact with the data in a well-curated way really emphasizes the relevant factors and helps everyone understand the patterns we use to drive decisions and take action.