#!F-adobe-helvetica-medium-r-normal--18*
#!N 
#!CNavyBlue #!N  #!Rall616 Presentation: Issues and Techniques #!N 
#!EC #!N #!N Visualization is used to represent natural phenomena that 
are inherently visual themselves, but probably more often, it is used 
to "visualize" non-visual phenomena. The process of making something visual means 
making choices on the part of the program author or designer. 
Clearly, without a sound scientific basis for these choices, this can 
become a purely artistic venture. While computer graphics can be used 
to make beautiful artwork, that is presumably not the point of 
using visualization to help study, analyze, or understand data. This does 
not mean that you should forego good design in making your 
visualization scene understandable. Remember and use the "rules" of design mentioned 
above, including proper, legible annotation, reasonable choices for colors, and so 
on. These things are determined partly by the medium you are 
working in and partly by the rules of good layout and 
design. #!N #!N But what color is a magnetic field? What 
color is hot? What color is high? How fast should molecules 
vibrate? How quickly should a metallic surface move as it changes 
phase? These decisions must be made by the program author. Probably 
the three most critical choices are color, scale, and speed. #!N 
#!N In visualization, color is used precisely because it is  #!F-adobe-times-medium-i-normal--18*   
not #!EF realistic. That is, to emphasize an area of interest, 
red is commonly used. Or, a strong contrast color can be 
used against a field of fairly neutral colors. However, there are 
some cultural color choices that you may find inappropriate to violate. 
For historical and to some degree natural reasons, we tend to 
make color gamuts that indicate red as the "highest" and blue 
the "lowest." Particularly with temperature, we can associate blue with "cool" 
or water/ice color, and red with "hot" or flame/sun color. To 
some degree, this gamut is related to the color of heated 
metal, but of course, the metal color does not pass through 
green at the midway point, and the color scale does not 
end at white like white-hot metal, so this too is only 
a loose analogy. But try inverting a color map of temperature 
to make red cool and blue hot and you will probably 
find you have to perform mental gymnastics to interpret it "correctly." 
If you are mapping altitude, however, red is not necessarily best 
associated with the "high" point: after all, the highest altitudes are 
snow-covered and lower altitude deserts are frequently "red-hot"! Actually, color-mapping altitude 
is almost purely an artistic endeavor, but at least it has 
a long history and literature in cartography. Consulting the "traditional" textbooks 
for a field may indicate how users in that discipline "prefer" 
things to be mapped. It is generally unwise to start a 
new schema for your visualization if you wish it to be 
immediately accessible to other viewers familiar with the discipline. But relating 
new ways of visualizing data to the old methods may be 
a good way to provide new insights for everyone involved. #!N 
#!N Remember that to use interpolation, the basis of your assumptions 
is that the phenomenological space studied is continuous and linear. If 
you have reason to believe the sampling was not done over 
a domain that can be linearly interpolated, you should certainly not 
be using linear interpolated images to understand the data. You may 
need to collect more data on a finer grid to resolve 
such problems. Since Data Explorer supports irregular grids, this is not 
a problem for the software, as long as you provide the 
correct data sampling. Also, be aware that trying to read too 
much detail out of an image is an error. You cannot 
accurately assess detail at a resolution equal to or less than 
your sampling rate (the Nyquist law states that you cannot derive 
valid signal from noisy information at less than twice your sample 
rate). For example, occasionally, you will see peculiar color artifacts that 
arise when data and therefore interpolated colors change rapidly at the 
scale of the sampling mesh. In those cases, the best bet 
is to "zoom out" to see only the big picture: do 
not try to read between the lines! #!N #!N Related to 
sampling rate in space is sampling in time. Be sure you 
have collected enough time step detail to ensure you have not 
completely missed some important transitional state that might have occurred in 
the middle of an animated sequence. It is acceptable to skip 
through the entire range of time steps during the development of 
your animation, but be sure to fill in the gaps before 
the final presentation is analyzed. #!N #!N As in traditional statistical 
plotting, a computer can all too easily permit the author to 
scale objects or graphs into wildly distorted aspects. In charting, there 
are some simple rules of thumb: it is often suggested that 
the aspect ratio (height/width) be about 0.75 to 1.00 for a 
2-dimensional chart. This may require rescaling one axis, and naturally, both 
axes and their scales must be shown. It is also bad 
form to start an axis at one point then create a 
break part way along, causing a visual foreshortening. And it is 
also inappropriate to start an axis at a point other than 
the origin if the intent of the chart is to represent 
absolute amounts of quantities being compared side by side. All of 
these rules of thumb are employed to make "good" charts; nevertheless, 
these rules are too often violated even in the mainstream media. 
#!N #!N Unfortunately, these traditional rules of scale do not help 
us much when we create 3-dimensional objects of arbitrary shape. So 
it becomes incumbent upon you to make sensible decisions in depicting 
objects never before seen by any viewer. It will be very 
easy to exaggerate a 3-D height field by changing the scale 
factor in Rubbersheet. You can make the one high point in 
the data leap as high as Mt. Everest. If that point 
is in fact a special value in your data, this may 
be an appropriate thing to do. If not, you may wish 
to choose a scale better suited to depicting the entire surface. 
On the other hand, if there are peaks, you must avoid 
"crushing" the entire surface to lessen the high points. Doing so 
could lead to potential misinterpretation of your results. #!N #!N For 
many researchers, Data Explorer will be the first program they have 
used that permits them to create and view animation or motion 
playback of their data. This new temporal dimension is often a 
source of problems until the author gets the hang of things. 
Here are a few tips as you develop your own "moving 
pictures." #!N #!N First, remember that your viewers have never seen 
this phenomenon before. Give them a chance to absorb it: looping 
the entire sequence is usually helpful. You do not want to 
bore the viewer to death, but visualization is not a TV 
commercial: cutting to a new scene every two seconds is not 
a good editing technique for communicating difficult visual information. As we 
discussed in the section on Animation, showing the same sequence at 
more than one speed helps a viewer notice different information in 
the very same scene. #!N #!N Visualization allows users (fortunately) to 
wildly distort time scales. One video may show the movement of 
tectonic plates, another the gyrations of atoms in a gas. One 
scale is millions of years, the other billionths of seconds, but 
both are brought into the "video" scale of one frame every 
thirtieth of a second. Clearly, you must use some kind of 
clock annotation, especially if you plan to change playback rates, and 
even more importantly, if you plan to show different data sets 
using the same type of animation. The user must be given 
a proper sense of how two animations compare in their duration 
if sense is to be made of these animated sequences. #!N 
#!N However, humans are not particularly good at visual comparison from 
memory. We are good at pattern recognition and comparison, but we 
have inadequate temporal rate memories; we do not remember detail in 
relation to time because we do not have good time-keeping reference 
systems in our brain. That implies that you must either choose 
to show comparisons based on precisely the same time duration and 
playback rate (thus factoring out the time dimension), or, much better, 
show two motion sequences at the same time in the same 
picture. One way to accomplish this is to render two sets 
of images, then use the Arrange module to construct an animation 
showing the two sequences side by side. This technique is important 
if the two phenomena vary in a scientifically critical way during 
the process; for example, if one phase change event is virtually 
complete after 40% of the entire time step series and another 
phase change after 60%, this may represent one of the important 
findings of your research. But if you show the viewer first 
one sequence, then the other, very few people will be able 
to make a solid visual comparison from their memory. It is 
much more visually impressive to show the two phase change simulations 
side by side, starting at the same time, and proceeding for 
the same number of time steps. #!N #!N Animation must also 
proceed quickly enough for the mind's eye to perceive it as 
animation. Imagine taking each time step of your simulation, making a 
35mm slide, and loading up a slide carousel with ninety slides. 
A viewer who is shown each slide for 5 seconds is 
unlikely to perceive the "motion." Put on videotape, the same sequence 
of images takes only 3 seconds. This may be too fast: 
the entire event may flash by too fast for the viewer 
to see any change. You may need to double-record each image 
(i.e., slowing things down by one-half) making the video take 6 
seconds. Another way (more computationally expensive) is to generate twice as 
many raw data files and twice as many images. This will 
yield smoother animation, but may be too costly for your resources. 
Of course, some events can be shown in 3 seconds: maybe 
everything stays the same for 1.5 seconds, then "pops" into a 
new configuration. Slowing this down too much might hide the importance 
of the sudden transition to a new state. Again, you, the 
user familiar with the field and with the phenomenon become a 
judge and a designer. You have to make wise decisions based 
on a desire to accurately and honestly depict the behavior under 
study with the purpose of illuminating other viewers, not impressing them 
with spectacular computer graphics displays. #!N #!N #!N  #!F-adobe-times-medium-i-normal--18*   Next Topic 
#!EF #!N #!N  #!Limd,dxall618 h Importing Data: File Formats  #!EL  #!N  #!F-adobe-times-medium-i-normal--18*   #!N 
