Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

Users love simple and familiar designs – Why websites need to make a great first impression by Javier Bargas-Avila, Senior User Experience Researcher at YouTube UX Research

I knew it didn’t take long to love/hate a website but…:

I’m sure you’ve experienced this at some point: You click on a link to a website, and after a quick glance you already know you’re not interested, so you click ‘back’ and head elsewhere. How did you make that snap judgment? Did you really read and process enough information to know that this website wasn’t what you were looking for? Or was it something more immediate?

We form first impressions of the people and things we encounter in our daily lives in an extraordinarily short timeframe. We know the first impression a website’s design creates is crucial in capturing users’ interest. In less than 50 milliseconds, users build an initial “gut feeling” that helps them decide whether they’ll stay or leave. This first impression depends on many factors: structure, colors, spacing, symmetry, amount of text, fonts, and more.

As a comparison, the post cites the blink of an eye taking from 100 to 400 milliseconds.

Raises the bar on the 30 second “elevator speech” doesn’t it?

Pass this on to web page, topic map (and other) semantic technology and software interface designers in general.

How would you test a webpage given this time constraint? (Serious question.)

Useful junk?: the effects of visual embellishment on comprehension and memorability of charts by Scott Bateman, Regan L. Mandryk, Carl Gutwin, Aaron Genest, David McDine, and Christopher Brooks.

Abstract:

Guidelines for designing information charts (such as bar charts) often state that the presentation should reduce or remove ‘chart junk’ – visual embellishments that are not essential to understanding the data. In contrast, some popular chart designers wrap the presented data in detailed and elaborate imagery, raising the questions of whether this imagery is really as detrimental to understanding as has been proposed, and whether the visual embellishment may have other benefits. To investigate these issues, we conducted an experiment that compared embellished charts with plain ones, and measured both interpretation accuracy and long-term recall. We found that people’s accuracy in describing the embellished charts was no worse than for plain charts, and that their recall after a two-to-three-week gap was significantly better. Although we are cautious about recommending that all charts be produced in this style, our results question some of the premises of the minimalist approach to chart design.

No, I didn’t just happen across this work while reading the morning paper. 😉

I started at Nathan Yau’s Nigel Holmes on explanation graphics and how he got started and followed a link to a Column Five Media interview with Holmes, Nigel Holmes on 50 Years of Designing Infographics, because of a quote from Holmes on Edward Tufte that Nathan quotes:

Recent academic studies have proved many of his theses wrong.

which finally brings us to the article I link to above.

It may be the case that Edward Tufte does better with charts designed with the minimalist approach, but this article shows that other people may do better with other chart design principles.

But that’s the trick isn’t it?

We start from what makes sense to us and then generalize that to be the principle that makes the most sense for everyone.

I fear that is also the case with the design of topic map (and other) interfaces. We start with what works for us and generalize that to “that should work for everyone.”

Hard to hear evidence to the contrary. “If you just try it you will see that it works better than X way.”

I fear the solution is to test interfaces with actual user populations. Perhaps even injecting “randomness” into the design so we can test things we would never think of. Or even give users (shudder) the capacity to draw in controls or arrangements of controls.

You may not like the resulting interface but do you want to market to an audience of < 5 or educate and market to a larger audience? (Ask one of your investors if you are unsure.)

Beyond The Pie Chart : Creating new visualization tools to reveal treasures in the data by Hunter Whitney.

From the post:

The New Treasure Maps

If a picture’s worth a thousand words, what’s the value of an image representing a terabyte of data?

Much of the vast sea of data flowing around the world every day is left unexplored because the existing tools and charts can’t help us effectively navigate it. Data visualization, interactive infographics, and related visual representation techniques can play a key part in helping people find their way through the wide expanses of data now opening up. There’s a long history of depicting complex information in graphical forms, but the gusher of data now flowing from corporations, governments and scientific research requires more powerful and sophisticated visualization tools to manage it.

Just as a compass needle can give us direction in physical space, a chart line can direct our way through data. As effective as these simple lines may be, they can only take us so far. For many purposes, advanced data visualization methods may never replace Excel, but in our data-saturated world, they might well be the best tools for the job. UX designers can play a key role in creating these new tools and charts. In these treasure maps of data, perhaps UX marks the spot.

Start of what promises to be an interesting series of posts on visualization.

Interactive Dynamics for Visual Analysis by Jeffrey Heer and Ben Shneiderman.

From the article:

The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data.

Visualization provides a powerful means of making sense of data. By mapping data attributes to visual properties such as position, size, shape, and color, visualization designers leverage perceptual skills to help users discern and interpret patterns within data. [cite omitted] A single image, however, typically provides answers to, at best, a handful of questions. Instead, visual analysis typically progresses in an iterative process of view creation, exploration, and refinement. Meaningful analysis consists of repeated explorations as users develop insights about significant relationships, domain-specific contextual influences, and causal patterns. Confusing widgets, complex dialog boxes, hidden operations, incomprehensible displays, or slow response times can limit the range and depth of topics considered and may curtail thorough deliberation and introduce errors. To be most effective, visual analytics tools must support the fluent and flexible use of visualizations at rates resonant with the pace of human thought.

The goal of this article is to assist designers, researchers, professional analysts, procurement officers, educators, and students in evaluating and creating visual analysis tools. We present a taxonomy of interactive dynamics that contribute to successful analytic dialogues. The taxonomy consists of 12 task types grouped into three high-level categories, as shown in table 1: (1) data and view specification (visualize, filter, sort, and derive); (2) view manipulation (select, navigate, coordinate, and organize); and (3) analysis process and provenance (record, annotate, share, and guide). These categories incorporate the critical tasks that enable iterative visual analysis, including visualization creation, interactive querying, multiview coordination, history, and collaboration. Validating and evolving this taxonomy is a community project that proceeds through feedback, critique, and refinement.

This rocks! I missed it earlier this year but you should not miss it now! (BTW, if you see something interesting, post a note to patrick@durusau.net. I miss lots of interesting and important things. Share what you see with others!)

Two lessons I would draw from this article:

1. Visual analysis, enabled by the number-crunching and display capabilities of modern computers, is just in its infancy, if that far along. This is a rich area for research and experimentation.
2. There is no “correct” visualization for any data set. Only ones that give a particular analyst more or less insight into a given data set. What visualizations work for one task or user may not be appropriate for another.