I keep on getting struck by the similarities between vis and computing concepts — pre-attentive processing seems to be an analog of GPU and level2 cache memory, attributing bandwidth to senses, etc.

But, I think there is more — visualization really is an attempt to break us out of expected ways of looking at very large datasets and make us look at the datasets from different angles. We can comprehend a few things at a time, but when faced with very large datasets, we seem to not change our methodology, and plow ahead with what would be appropriate for a data points but is inappropriate for lots of data points. Visualization is an attempt to bring into our main vision what may otherwise lie at the periphery of our vision.

In the 12 items of Ware chapter, I found 11 – long term memory as cognitive skills not repositories – interesting, which has a strong meaning not only for visualization but communication as a whole. It implies that the visualisation should not just present the data efficiently, but that it should be done in a way to motivate cognitive processes of the viewer. it would be great if the corresponding chapter in the textbook elaborates this subject more. Also I’d like to see if there is more on “how” patterns can be found, other than the example of simplification that was demonstrated in the humpback whale case.

As for Tufte, one question: I had the impression that in both main cases (Cholera, Shuttle) the researchers already knew what they were looking for, with visualisation refining where they should look. I wished he would also deal with cases that are the other way around, how to look for the right questions.

Fekete et al.’s data mining introduction and economic model of value fit right in where Tufte left off. However, I think that the cost – insight efficiency argument is more or less rhetorical, rather than a measurable theory of assessing a specific visualisation technic.

Tuft’s chapter points well the importance of right design to get the true information and shows WHY InfoVis is useful.

After reading those two chapters, especially Tuft’s chapter, I was confused where I am in visualization area as a statistics student because what Tuft’s examples are doing is what I would do. This confusion can be resolved after reading the paper (The value of Information visualization) – information visualization as an expansion of exploratory data analysis plus information theory and psychological theories, etc.
This paper well explained HOW and WHY InfoVis is useful, giving me a big picture after all.

As a relevant example, I heard that people well trained in abacus calculation are capable of imaging the abacus in the head and do the calculation in the head using the imaginary abacus. This tells me two things – 1. cognitive benefits by using visualization, 2. cognitive process becomes more automatic.

It’s a little bit too much for me to read, I still enjoy the chapters. I choose Colin Ware, CH. 9 first, then Tufte’s, then Fekete’s paper. For me it’s more like from easier to harder.
Colin Ware’s Charpter 9 did a good job to summary the ongoing content of the book into 12 points, while pointing out the 4 implications for good design. I especially like the example about the humpback whales, and the idea of using robbon for the tracking is very creative. For me, I thick the success of this example is also because of it did a proper amout of abstraction: omitting a lot of “too-detailed” information such as the whole body of a whale, while preserving most data of their track. It seems to me that a more detailed model will damage the ability for a design to support pattern finding, and a too-simlified model will lose significant data.

I fell Tufte’s chapter more technical but also interesting since so many case studies are provided. A more efficient way I found is to read Fekete’s paper first (since it gives more systematic theory-point view), and then go to Tufte’s paper for comparison and exampling.

Finally, comment cannot be edited… happens to me too….

One thing that is not very clear from this chapter is “Who is the target
audience ? Are they painters, mass communication designers, scientists who
are looking for hidden patterns in their dataset.

This chapter also supports the well known fact that computers and humans
are complementary. Humans are unmatched patterns finder and computers are
superior at churning out and pre-process high bandwidth data. And the
design challenge is to transform data into a form where important features
can be easily interpreted by humans.

Chapter 2: Visual and Statistical Thinking:
******************************************
This interesting chapter can be summarized in two sentences from the text.

1. There are right ways and wrong ways to show data, there are displays
that reveals the truth and display that do not.

2. Visual representation of evidence should be governed by principles
of reasoning about quantitative evidence.

Two examples have been given in the chapter (1) London Cholera Epidemic
(2) Decision to Launch Space Shuttle.

But In my view, both the examples shows great human logical thinking to find
the cause of an effect than the mapping tools that were used to display them.
In both the examples, there were strong hypothesis, and correlations and
visualization was just a tool to express them to the masses. Perhaps the main
people ( John Snow and Space Engineers) drew the conclusions without the aid
of data visualization with their great intuitive knowledge. Therefore I think
the title of the paper” Display of Evidence for Making Decision” is confusing
as decision were probably already made.

So perhaps I didn’t like the two examples given in the chapter, but I support
the two main points which are generic.

The Value of Information Visualization:
***************************************
I think this paper repeats concepts given in early twp papers

The Value of Information Visualization – I found this paper to be a bit odd. To me it seems like its a scatter shots of a whole bunch of justifications for visualizations, in an attempt to have at least one of those strike a cord with the reader. However, I did like how there is an explanation of automatic analysis, and a clear way to determine if using a visualization is the best way to go.

I have some real problems with a lot of the language in the rest of the chapter too. How about this one: “Various kinds of information are combined in a temporary nexus of meaning”. “Nexus” simply means “a place where things come together, so obviously a “nexus of meaning” is where “various kinds of information are combined”. The sentence is completely vacuous. (The only word that adds any meaning is “temporary”.)

The fist of the “4 implications” of the active vision model also grated a bit:
– “To support the pattern-finding capability of the brain; that is, to turn information structures into patterns.”
First of all, this is not an “implication” – it’s more of a design goal. Also – what is the difference between “information structures” and “patterns”? How does reading this sentence make it clearer how to do this? What does it mean to *not* turn “information structures” into “patterns”?

Granted, Ware makes the point that verbal reasoning and spatial reasoning are quite different, but having a focus on visual thinking is not a license to dispense with clear writing. (Writing for entertainment, however, can be.) It does, however, make a good contrast with Tufte, who at least writes clearly.

Fortunately the pictures are much more informative, they almost make up for the goofy writing.

Tufte, Ch. 2: I read this book back in August:
http://www.amazon.com/Ghost-Map-Steven-Johnson/dp/1594489254
It appears that Tufte’s book predates this one by almost 10 years.

The section on hiding data through aggregation was interesting and thought provoking, and felt like a bit of a digression – it didn’t come across that that was an actual problem with Snow’s presentation – but simply that this was a good example to talk about the phenomenon.

I felt that he was taking a bit of a cheap shot at Feinman – yes, a real experiment should have had 2 clear glasses, one with ice, one without – but Feinman didn’t have 2 glasses. He had one paper cup, one strip of rubber, and he almost had to do without that much. The point wasn’t to figure out the properties of cold rubber – the engineers had already done that – the point was to compensate for the engineers’ inability to communicate that the rubber really does behave differently when it’s cold. Incidentally, Feinman chose a mock experiment as his way of doing so because that would leverage his authority as a famous scientist in the minds of the public. I don’t think anyone could read that story and come away with a significantly different understanding, and yet Tufte had to belabor the fact that mock-science should never masquerade as the real thing. Granted, it’s not a good idea in general to mix mock science and real science, but in this case, it was an extremely effective bit of communicating. One would have to show that Feinman damaged the overall state of scientific awareness of the public before complaining about it being bad science.

Chapter 2 of the Tufte’s book describes the scientific principle, making controlled comparison, by examples of both good and bad sides. To make the key parts visualized, statistical and graphical reasoning should be taken in to consideration. The former example in this chapter is how people used data to support their viewpoint in more than 150 years ago.

The paper, The Value of Information Visualization, provides examples of Tufte’s with slightly different viewpoints. One of the features I like is the explanation of statistical data analysis, called data mining here. Figure 6 illustrates the regression result simply and clearly, with all the useful statistics shown on a single plot. Figure 7 does the similar procedure but with more visualization contents, which somehow cannot be summarized easily by statistics tables. The equation of economic model of value makes the visualization quantitative, which helps improving the efficiency. I think the main topic of this paper is to show how and why is InfoVis useful.