Questions for 4-19

Few

Rating:  4

1) How do we balance ensuring that we present our information in multiple formats for higher level consumption with the issue he raises about our brains having a limited ability to hold multiple items in our minds at the same time? Look at Fig. 35.5. Here is a visualization that represents information in multiple formats, helping the reader to better understand the underlying data. But images such as this can sometimes be intimidating; a quick glance can make the display seem complicated, even if it actually simplifies the data upon closer inspection. When it comes to data visualization how much is too much and how much is not enough?

2) Do the principles relating to human perception that Few discusses hold true for different types of learners? Some people learn better by seeing or doing or hearing – would the principles of visualization discussed here apply equally to all these groups? Looking at his early example in figures 35.1 and 35.2 isn’t it possible that someone who works well with numbers and learns by doing would see figure 35.1 and compare the values of the numbers in their head almost automatically? Not that this would negate anything Few says. Such a person would still probably glean the same information more quickly from the graphical figure, but for such a person looking at figure 35.1 can we really say “this table fails” as Few does?

3) The node and link visualization can be an excellent tool for showing relationships between entities other than variables. The social media example used in the article is a good one. But I worry that we embrace it so readily because it looks nice. Is this really an effective method for demonstrating relationships? Looking at the figure can you tell me Amanda’s relationship to Nick? How about Scott’s relationship to Ken? Or Jason’s relationship to Christine? All of this is on the chart, but because the chart is cluttered with so many nodes it is not readily apparent (at least, it isn’t to me). To quote Few’s introduction, “’a picture is worth a thousand words’ - often more - but only when the story is best told graphically rather than verbally and the picture is well designed.” How could this visualization be better designed to overcome some of its weaknesses?

Icke

Rating:  3

1) Does it seem strange to anyone else that Icke spends 6.5 pages talking about the system aspects of VA, and crams user aspects and human-machine collaboration aspects into less than one page total while trying to tell us that user input and system computing power need to be balanced?

2) Icke says that the correct algorithms must be chosen for the dataset with which you are working but he doesn’t really explain how this is done. What factors are important to consider when deciding on analytics? What are the available options when it comes to analytics? There is a lot of information in the paper about the options available when it comes to visualization types, but it is quiet about the analytics themselves. Since various types of data and visualizations are discussed wouldn’t it make sense to expect a cursory discussion of available analytical tools?

3) What would Few say about this article? Would he agree with the visualizations Icke uses? Do they incorporate aspects of human perception? Looking at figure 35.9 from Few’s article, are Icke’s visualizations in-balance or out-of-balance when looking at the brain functions of seeing vs. thinking?

Lam et al.

Rating:  2

1) What is the difference between the process-oriented CTV scenario and the visualization-oriented UP scenario? They both seem to be evaluating how a program conveys information and how that information is received. It just seems to me that these types of evaluations would have a lot of overlap.

2) Why are the visualization scenarios so much more common than the process ones? I understand that some of it comes from the traditions of the HCI and CG fields, but the process scenarios are certainly not unknown since they’re 15% of the evaluations looked at in this study, and if they truly provide profitable information wouldn’t companies be interested in exploring those options? I wonder if the methodology of this paper had been changed to include papers from more than the 4 sources listed if that would have changed the percentages significantly.

3) Each year the number of papers that include evaluations increases. The authors mention that, according to the review by Barkhuus and Rode, the quality of these evaluations may have remained the same. What is the impact of static evaluations on the future development of visualization resources? Are the current evaluations sufficient or should we be investigating new methods that could spark developers to reach for a higher standard?