• Nominal (named/categorised): apples, oranges, pears
• Ordinal (ordered): small, medium, large
• Interval (measured): the interval between 2:30pm and 3:00pm is the same as the interval between 10:30am and 11:00am
• Ratio (measured, has zero value): 0 tonnes of apples, 40 tonnes of apples.

What makes the NOIR concept important is that it can define every data type.

Then, you have Mackinlay’s Automating the Design of Graphical Presentations of Relational Information, which defines representational types, and can also be described by NOIR.

Put them together, and you have Zhang’s A Representational Analysis of Relational Displays which matches the data structure (NOIR) with the representation (also defined by NOIR).

What does this mean? It means the best way to represent a nominal dataset is by giving it a hue, as opposed to making different sizes of circles. Why? Because people might interpret, say, that blueberries are 10 times better than apples since area as a representation communicates a ratio data type.

So what does this mean for information aesthetic visualisation? Well, some have argued that form follows data and that’s the end of that. That data structure alone defines representation.

But data isn’t just a structure — there’s meaning behind a dataset, there’s insights, trends, feelings, issues that it addresses, people it involves… and THAT should all go into the representation as well.

So these are Andrea’s (pending) Information Aesthetic Meta-Representational Considerations:

• Feeling (leave people with an emotion): you can delight them, make them sad, or disgust them: just make them FEEL something. We’re not a soma-fied Brave New World.
• Interaction & Animation (give choice, show perspectives): it’s unlikely you can fit all the attributes of your data into one neat, 2D screen-based representation, so use interaction: this means we can add representations like animation (shaking, swooshing, scaling, flying, dropping, hovering…) and hovering, clicking, dragging, scaling, rotating…
• Learn (collaborate): information aesthetic visualisations should be collaborative. Just like ManyEyes, people should be able to leave comments, screenshots of their insights and things they’ve found. The meta-data is more valuable than the data in many ways, and should be shared and tapped into.
• Meaning (intent): your data (for information aesthetic visualisations, at least) is trying to tell you something! What is it? Is it trying to tell you that the environment is going to shit and we should do something about it? Is it trying to tell you that people living close to growers markets cook at home more?

The issues of global warming aside, it was interesting to note the different approach that this film took to Inconvenient, especially how numbers were communicated.

Although we almost-unanimously praise the combination of infographics, photography, and commentary by Al Gore, I find Home equally, if not more, compelling to watch – yes, even without infographics!!

Why? Because:

• there’s a narrative; not just any narrative, but one which every person watching the film is a part of.
• seeing the actual impact of humans rather than a removed/abstract/conceptual line graph or chart is far more relatable
• being guided, having a stream of video plus voice-over lends more to the imagination. There’s something inherently different about watching a presentation versus a film.

There’s a very good paper out there by Dalbello & Spoerri (2005) Statistical representations from popular texts for the ordinary citizen, 1889–1914 which argues that representational visuals can aid communication to audiences through their strong relationship with everyday life.

I wonder all of this after realising that people need shitloads of explanation when you show them a datavis project or an infographic.

If everyone in the field argues that visual metaphor, abstract form that follows our perception, Gestalt theory, and understanding of user models of interaction can produce visualisations that are easy to use, familiar… then why is it so hard?!

People have to be ‘trained’ to use computer systems, but they shouldn’t have to for information aesthetic datavis. Instead, datavis must take qualities of well-designed products (a la words from Don Norman) and perhaps even the intuitiveness of art.

1. Good Art Inspires. Good Design Motivates.
2. Good Art Is Interpreted. Good Design Is Understood.
3. Good Art Is a Taste. Good Design Is an Opinion.
4. Good Art Is a Talent. Good Design Is a Skill.
5. Good Art Sends a Different Message to Everyone. Good Design Sends the Same Message to Everyone.

I wonder: where would you insert information aesthetics? Would it replace art or design? (First disregarding the whole issue of what a ‘good information aesthetic visualisation’ is to begin with!)

1. Motivates: the point about effectively communicating a message is key here.
2. Interpreted or Understood? Information aesthetic visualisations should be firstly understood (through familiarity, intuitive interaction, etc) with an overall meaning, but be open to interpretation through interaction. This one is contentious because in many ways design can be interpreted, too. Heck, anything can!
3. Opinion: this one is dicey, too, but largely, it’s about opinion. I would say that subjective taste does play a part when you get into the less-familiar modes of representation, and people would still have taste-based judgements about colour choice and style.
4. Talent & Skill: definitely! You need to have an emotional connection to the data – an intrinsic ability to bring out meaning and insights – and what the visualisation is trying to communicate, but you need the know-how to effectively do this.
5. Different & Same: this is the same as point 2.

The most salient message here is that, if information aesthetics can replace ‘design’ in most of these instances, then it in fact becomes a part of the design and HCI fields. Ie: not the computer science or software engineering field which information visualisation comes from, and where a lot of people argue that it shouldn’t be anyway!