Lecture 1

Into to the Course and VTK

(with some noted originally from Bill Sherman NCSA, and others from my CS 422 course)

Information about the Course - Syllabus, presentations, projects etc.

How this class relates to to other similar / related CS courses


CS 422
 User Interface Design Focus on developing effective user interfaces
Every spring
CS 426
 Video Game Programming Focus on creating complete audio visual interactive (and fun) experiences Every term
CS 488
 Computer Graphics I Focus on the basics of how computers create images on screens, OpenGL Once per year
CS 522
 Human Computer Interaction Focus on interaction and evaluation of interactive environments Every fall
CS 523
 Multi-Media Systems Focus on the creation of Educational Worlds Once per year
CS 525
 GPU Programming Focus on shaders and parallel processing Fall even years
CS 526
 Computer Graphics II Focus on Scientific Visualization
 Spring odd years
CS 527
 Computer Animation Focus on creating realistic motion Spring even years
CS 528
 Virtual Reality Focus on immersion Fall odd years

Scientific Visualization

Webster defines Visualization as:

  1. formation of mental visual images
  2. the act or process of interpreting in visual terms or of putting into visible form

Hamming: "The purpose of computing is insight not numbers"

What are the advantages? (adapted from [Ware 2000])

How do we make good visualizations? (adapted from [Tufte 1983])

We start off talking about Charles Joseph Minard's 1861 graphic showing Napoleon's losses during his 1812 march to and from Moscow - possibly the best statistical graph ever drawn ... why?

Napoleons March on Moscow
from p41 of The Visual Display of Quantitative Information

The chart show 6 variables

If you stopped by the maps exhibit at the field museum in the fall on 2007, the original was on display there. More Minard maps can be seen at: http://cartographia.wordpress.com/category/charles-joseph-minard/

If you want to see more really good visualizations then this site is worth checking out: http://www.math.yorku.ca/SCS/Gallery/

Here are a couple simple examples where 2d visualiztion techniques in one case did have and in the second case should have had an important positive effect that I like to use as examples in the User Interface Design and Programming course:

The first was a visualization created by Dr. John Snow(1813-1858) a distinguished British Anesthesiologist who plotted over 500 deaths in central London from Cholera in September 1854.

Here is the graphic reprinted in the Visual Display of Quantitative Information, p24 (there is another good related map in Visual Explanations, P30.)

A really good book to read if you are interested in this is 'The Ghost Map' by Steven Johnson, published in 2006. If you prefer, there is a TED talk here: http://www.ted.com/talks/steven_johnson_tours_the_ghost_map.html

And more information is available online at: http://www.ph.ucla.edu/epi/snow.html

If you want to look at this area now, you can tell google earth to go to 'Golden Square, London, Greater London, W1F, UK'


one of the original maps by John Snow


(E.W. Gilbert's simplified version of John Snow's map - more information on this version can be found at: http://www.ph.ucla.edu/epi/snow/cartographica39(4)1_14_2004.pdf)

Deaths are marked by dots and the location of the 11 water pumps in the area are marked with Xs. The deaths seemed centered around the Broad St. pump. When people stopped using the pump, the epidemic ceased. Note that at the time the infectious theory of disease was not generally accepted. Disease was believed to be caused by morbid poisons coming from dead bodies and decaying organic matter, and spread through the air.

Here is some of his own text: (full text available at
http://bbh.hhdev.psu.edu/courses/440/SnowCholera/snow_on_cholera_exercise.htm)

"Very few of the fifty-six attacks placed in the table to the 31st August occurred till late in the evening of that day. The eruption was extremely sudden, as I learn from the medical men living in the midst of the district, and commenced in the night between the 31st August and 1st September."

 "The greatest number of attacks in any one day occurred on the 1st of September, immediately after the outbreak commenced. The following day the attacks fell from one hundred and forty-three to one hundred and sixteen, and the day afterwards to fifty-four. A glance at the above table will show that the fresh attacks continued to become less numerous every day. On September the 8th-- the day when the handle of the pump was removed--there were twelve attacks; on the 9th, eleven: on the 10th, five: on the llth, five; on the 12th, only one: and after this time, there were never more than four attacks on one day. During the decline of the epidemic the deaths were more numerous than the attacks, owing to the decease of many persons who had lingered for several days in consecutive fever.

"There is no doubt that the mortality was much diminished, as I said before, by the flight of the population, which commenced soon after the outbreak,- but the attacks had so far diminished before the use of the water was stopped, that it is impossible to decide whether the well still contained the cholera poison in an active state, or whether, from some cause, the water had become free from it."

 The last sentence above is important to note. Snow himself can not state that removing the pump handle definitively stopped the outbreak.

Here is some of the actual data:

Date # of Fatal Attacks Deaths
8/19 1 1
8/20 1 0
8/21 1 2
8/22 0 0
8/23 1 0
8/24 1 2
8/25 0 0
8/26 1 0
8/27 1 1
8/28 1 0
8/29 1 1
8/30 8 2
8/31 56 3
9/01 143 70
9/02 116 127
9/03 54 76
9/04 46 71
9/05 36 45 10% of the neighbourhood dead in 1 week
9/06 20 37
9/07 28 32
9/08 12 30
 pump handle removed
9/09
 11
 24
9/10
 5
 18
9/11
 5
 15
9/12
 1
 6
9/13
 3
 13
9/14
 0
 6
9/15
 1
 8
9/16
 4
 6
9/17
 2
 5
9/18
 3
 2
9/19
 0
 3
9/20
 0
 0
9/21
 2
 0
9/22
 1
 2
9/23
 1
 3
9/24
 1
 0
9/25
 1
 0
9/26
 1
 2
9/27
 1
 0
9/28
 0
 2
9/29
 0
 1

 and a chart of that data:


John Snow's visualization has a number of good features that you should strive for:

1. Place data in the appropriate context for assessing cause and effect
2. Allow the viewer to make quantitative comparisons
3. Encourage search for alternative explanations  and contrary cases 
4. Indicate level of certainty and possible errors in the data

but its not just about making a graphic, but making a good graphic. A bad graphic may hide the truth.

This was the last great cholera outbreak in London.

Of local note there is an urban legend that Chicago had 80,000+ fatalities from cholera when in August 1885 a rainstorm dropped 7" of rain on Chicago in one day, overflowing the drainage systems and causing raw sewage to flow into the lake and back into the cities drinking water. The storm happened, the fatalities did not thanks to a shift in the winds.

The second is a discussion of the Challenger disaster with refs from Tufte's "Visual Explanations."

The engineers at Morton Thiokol who designed the solid rocket boosters for the shuttle opposed the launch and faxed 13 diagrams to NASA management to make their case - they failed in large part because of what infomation they chose to present and the way they presented that information, but also because of time and information constraints.

There is a nice overview here:
 http://www.nasaspaceflight.com/2007/01/remembering-the-mistakes-of-challenger/

including the following:

‘We discussed what might happen below our 40 degree qualification temperature and practically to a man we decided it would be catastrophic,’ added Morton Thiokol's Bob Ebeling.

‘Thiokol recommended that we could not launch until the weather warmed up in the afternoon,’ said NASA senior manager Jud Lovingood. ‘Well I told them they couldn’t make that recommendation. They had to give us a temperature that we could launch with.’

A formal presentation would have to be made, two hours after speaking with Lovingood and just 15 hours before launch, via a teleconference at which Thiokol would need to given their reasoning for a no launch decision – a power contractors held, but were scared to make given the effects on the Shuttle schedule.

Thiokol engineer Roger Boisjoly – one of two specialists (the other being Arnie Thompson) on the SRB joint seals – grabbed anything he could from his office to show how the temperature would lead to a failure of the SRB’s O-ring and the destruction of the Shuttle.

‘Unfortunately in our rush we didn’t have time for a dry run at what we’d present to NASA,’ noted Boisjoly. ‘I had no idea what my colleagues would present and I had no idea what I’d bring to the meeting.’

‘The entire Thiokol group recommended no launch,’ remembered Ebeling, as they recommended a minimum launch temperature of 53F (11C). The expected rubber stamping of that recommendation was expected from NASA on the other end of the teleconference. However, they would be proven wrong.


There had been several earlier flights with O-ring problems and the issues were being worked through to create a less flawed design, so NASA knew there were continuing concerns, but the amount of data available from the 24 previous shuttle flights was limited. For example the Morton Thiokol engineers did not have temperature data available for all of the previous shuttle flights (air temperature, or the much more useful O-ring temperature.) Seven earlier flights had O-ring issues, and those launches were at temperatures (F) of 53, 57, 58, 63, 70, 70, 75, and only two of those had serious 'blow by' issues, one at 53F and one at 75F. There were problems when it was warm; there were problems when it was cool. No shuttle had launched below 53 degrees F before.

The key table that the engineers produced was:



The table shows that there were problems seen in four rocket tests, and two actual launches, and then what the assumption would be for the temperatures at the Challenger launch. This table should also be seen in the larger context of many years of work between NASA and Morton Thiokol on the development of the rockets, so everyone involved in the meeting should have been able to put this data into context. One thing this table leaves out is data on tests and launches where there were no problems.

When NASA basically asked Morton Thiokol to prove that it was unsafe to launch the engineers were given an almost impossible task given the time and information available.

Even after the disaster and the during the investigation when there was more time and more information available bad graphics were still being created. Looking at the O-Ring damage over the previous 24 shuttle missions, the data was presented in chronological order showing the location and extent of the damage sustained to the left and right boosters and the temperature at launch time. This hides the pattern.


If instead of using chronological order the same data was presented in ascending temperature order the pattern is a bit more clear

If instead we remove all the extraneous imagery and do a simple plot of temperature vs damage (a weighted average of erosion, heating, and blow-by) then the pattern becomes much clearer. To really do analysis you would still want to be able to get access to the more detailed data - this just gives you a nice overview.


The Challenger example gets talked about a lot in terms of ethics and responsibility and there are various views on the topic. One lengthy critique of Tufte's conclusions (which brings up several very important points but also reads into Tufte's work an attitude that I do not feel while reading it) is given here:
http://www.onlineethics.org/cms/17453.aspx

sci-viz still very much an art - algorithms and routines are a starting point but it takes experience and creativity to use them effectively

What Makes a Good Graphic

How big is an acre

People understand new information relative to what is already understood

Here is a comparison of a good graphic and a bad graphic, making use of a Choropleth map, dealing with Radon from Things that Make Us Smart, p70-71.

Why is the first version bad:

- density scale is not an ordered additive sequence - the viewer must keep referring back to the legend
- 'white' states are assumed to have low levels of radon when they are actually not part of the data

Coming Next Time

The Basics, Part I

You should start taking a look at vtk by going to www.vtk.org and grabbing an executable version or the source code to compile.

Another very nice piece of software built on top of vtk is ParaView (www.paraview.org) which acts like a front end to vtk and encourages you to read in datasets and apply filters and generate visualizations. I would suggest downloading paraview and start playing with it. Version 3 is in alpha testing and is pretty stable, though it doesn't have all of the filters from version 2 implemented yet.

last revision 7/17/09 - updated the challenger discussion