Lecture 12 Flashcards
What defines a successful visualization
A successful visualization is one that efficiently and accurately conveys the desired information to the targeted audience, while bearing in mind the task or purpose of the visualization.
Introduction 2
A visualization may be ineffective for a number of reasons.
It might be too confusing or complex to be interpreted by the intended audience
Some of the data may have been distorted, occluded, or lost during the mapping process.
There might be lack of support for view modification or color map control.
Aesthetics can influence the success of a visualization; a visually unappealing presentation can affect an audience’s willingness to look at the images
Steps in Designing Visualization
Creating a visualization involves
Deciding how to map the data fields to graphical attributes
Selecting and implementing methods for modifying views
Choosing how much data to visualize.
Additional information regarding the data being shown (e.g., labels) and the mapping (e.g., a color key) are also essential to facilitate interpretation, and must be integrated into the visualization.
The final consideration is the overall aesthetics of the resulting display.
Mapping from Data to Visualization
To create the most effective visualization for a particular application, it is critical to consider the semantics of the data and the context of the typical user.
By selecting data-to-graphics mappings that cater to the user’s domain-specific mental model, the interpretation of the resulting image will be greatly facilitated.
In addition, the more consistent the designer is in predicting the user’s expectations, the less chance there will be for misinterpretation.
Intuitive mappings also lead to more rapid interpretation, as translation time is reduced.
Some examples of intuitive mapping are color to temperature, length of line to distance.
In the graph on the next slide, images of planets are used to plot the relationship between the distance from the planet to the sun and the duration of its orbit.
Selecting and Modifying Views
One view is rarely sufficient to convey all the information contained in the data. So, intuitive controls for setting and customizing the views needs to be given to the user.
Views modification inclusion in visualization should be considered based on user priorities.
Scrolling and zooming operations are needed if the entire data set cannot be presented at the resolution desired by the user.
Color map control is almost always desirable, minimally supporting a set of different palettes, and preferably offering the user control of either individual colors or the complete palette.
Mapping control allows users to switch between different ways of visualizing the same data.
Scale control permits the user to modify the range and distribution of values for a particular data field prior to its mapping
Level-of-detail controls provide the ability to eliminate or highlight detail, supporting views at different levels of abstraction.
Information Density
One of the key factor while designing visualization is – How much to display?
It can lead to two situations:
Very less information, sometimes called “gratuitous graphics” like graph showing only percentage of male and female in a sample.
Too much information which can lead to confusion.
Keys, Labels and Legends
A common problem with many visualizations is that insufficient information is provided to the user to allow unambiguous and accurate interpretation. This supporting information should begin with a detailed caption indicating the particular data fields being displayed, and the mappings that were used.
Keys, Labels and Legends 2
The use of grid and tick marks can be both a boon and a curse to the visualization. Poor choices of the types of markings and the density used can occlude the data being displayed and lead to a cluttered appearance. Clearly, one should avoid the extremes.
Using Color with Care
One of the most frequently misused parameters in visualization design is that of color.
Selecting the wrong color map or attempting to convey too much quantitative information through color can lead to ineffective or misleading visualizations.
Also, since color perception is context-dependent (a particular color will appear quite different, depending on adjacent colors), the characteristics of the data itself can influence how the colors are perceived.
Finally, it must be remembered that many people are color blind or color confused; it has been determined that as many as ten percent of all males have some form of color deficiency.
Using Color with Care 2
If the visualization task involves absolute judgment, keep the number
of distinct numeric levels low.
Use Color with Care 3
Use redundant mappings if possible, e.g., map a particular field to
both color and size, to improve the chances of the
data being communicated accurately.
Using Color with Care 4
In creating a color map for conveying numeric information, make sure that both hue and lightness are changed for each entry
