Week 1

Intro to the Course and Visualization

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

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

CS 422
User Interface Design developing effective user interfaces
Every spring
CS 424
Visualization & Visual Analytics
interactive 2D visualization of different types of data
Every fall
CS 426
Video Game Programming creating complete audio visual interactive (and fun) experiences Every spring
CS 488
Computer Graphics I basics of how computers create images on screens, OpenGL Every fall

Some goals for this course:


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])

here is a recent data visualization to start off with ...

and for something a bit more dynamic

This course will also deal with Visual Analytics -  using interactive visualizations to enhance the analysis of large amounts of data - that is, the visualization is not the end-product but rather it is the means by which people can understand complex phenomena

Many analytical reasoning tasks follow this process

We are going to start by looking at some early visualizations.

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?

Joseph Minard's 1861 graphic showing
        Napoleon's losses during his 1812 march to and from Moscow
The image is discussed in detail on 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 (including the one above) can be seen at: http://cartographia.wordpress.com/category/charles-joseph-minard/

Like many analytical visualizations this gives us a way to see relationships between different tyes of related data. Today we have the ability to make dynamic visualizations that encourage active exploration beyond just looking. How would you enhance this visualization if it was software-based?

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

One in particular worth looking at was this 'coxcomb' done by Florence Nightingale in 1858 during the Crimean War showing deaths from wounds, other causes, and preventable disease as a way to encourage better hygiene to avoid cholera, typhus, dysentery, etc. More detail at https://www.sciencenews.org/article/florence-nightingale-passionate-statistician

Its hard to find a high-quality original but there are some good reproductions that have made including a nice one from the economist here: http://www.economist.com/images/20071222/5107CR3B.jpg

'coxcomb' graphic by
      Florence Nightingale in 1858 during the Crimean War showing deaths
      from wounds, other causes, and preventable disease

In March 1855 the Sanitary Commission arrived in Turkey, improving the water supply, sewage removal, and ventilation. Deaths from preventible diseases immediately drop dramatically. Returning from Turkey, Nightingale wanted to show the importance of hygiene, and while tables show the data, a graphic could have more immediate impact on the reader.
There are good things here and things that could be improved. Again, how would you enhance this visualization if it was software-based?

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

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

one of the
        original Broad Street cholera maps by John Snow
one of the original maps by John Snow - Deaths are marked by dashes and the location of the water pumps in the area are marked with circles.

E.W. Gilbert's simplified
        version of John Snow's map
(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. 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. Snow thought that water was involved in the transmission of Cholera so he already had an idea what to look for.

Here is some of his own text:

"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 11th, 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 from John Snow. Note that about 4000 people live in the area.

Date # of Fatal Attacks Deaths Significant Events
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 neighborhood now dead within 1 week
9/06 20 37
9/07 28 32  75% of population had left the area
9/08 12 30
 pump handle removed





















and a chart of that data:
Chart of Cholera Deaths

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

#3 is particularly interesting. There are areas near the Broad Street pump with no/few fatalities and there are a few fatalities far from the pump. Those suggest that maybe our hypothesis is wrong.

John Snow visited families of the deceased that lived far from the pump. Some preferred the taste of the water at Broad Street as it was usually more clear than the others. Some had children that went to school near the Broad Street Pump.

What about the areas near the pump with no fatalities. One was a brewery employing 70 men. The other was a work house with over 500 inmates that had only 5 deaths from cholera, and it had its own water pump.

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

Good and
          Bad Aggregations of John Snow Map Data

As a result of John Snow's work this was the last great cholera outbreak in London.

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

Google Maps
        image showing the Broad Street area today

and here is a photo of me from the summer of 2012 standing at the commemorative pump in what is now called Broadwick st. The John Snow pub is visible in the background.

Photo of Andy standing by
        Broad Street commemerative pump

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 city's drinking water. The storm happened, the fatalities did not, thanks to a shift in the winds.

And a modern visualization from the Guardian comparing different infectious diseases:


Now lets look at some common mistakes.

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.

US radon levels - bad

US radon levels - bad
        illustration - Legend

US radon levels - better

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

Now you may be thinking, hey that was back in the 80s, desktop publishing software was new, people wouldn't do something like that today ...

Here is a recent graphic showing the spread of H1N1. What states are the hardest hit? What is the order of the colours?
H1N1 in the US graphic

Here is the legend:

H1N1 in the US graphic - Legend

aside form the poor color choices, one thing to note is that the colour scheme is based on number of cases in each state, so more populous states and less populous states are treated equally. Since the most populated state (California) with 38 million people has 65 times the population of the least populated state (Wyoming) with only 576,000 people, a chart based on the percentage of the population with H1N1 could look very different. Both ways are useful and legitimate, but you want to make sure your audience is drawing the correct conclusions.

Here is the colour scheme from the centers for disease control:

Influenza in the US

What states are the hardest hit? What is the order of the colours?

Green is usually good and red is usually bad, but blue and pink?

as we will see next week this kind of color scheme is not very good for quantitative values.

and here is what that same image looks like to someone who is color blind:
Influenza in
      the US graphic - color blind version

Here is a link to the current (somewhat better) one:

and again, h
ow would you enhance this visualization if it was software-based?

Some basic principles from Norman:

Schneiderman: “Overview first, zoom and filter, details on demand”

How big is an acre

People understand new information relative to what is already understood

Here is a familiar image in an unfamiliar orientation.

        down' map of the americas

When information is first presented, the user should be able to quickly orient themselves.

When a map program starts up it should start up with a view that makes it obvious what the map is showing. Maybe that is using your current location with your position clearly labelled, or maybe its a view the country or city that you are accessing the map program from. The zoom factor should also be appropriate enough - if you are initially zoomed in too far you may not see enough landmarks to judge the scale of the map.

Principles of graphical excellence  from Tufte (a slightly longer list now that you've seen some examples):

Here are some examples for class discussion:

and now, for the weather, which is a common analysis task that we all undertake. We look at temperature data, precipitation data and make decisions on how to dress and/or how to get to/from work. We need to know what the weather is before we go out in the morning, depending on our job what the weather will be during the day, and what the weather will be like when we try to get home.

For everyday activities we usually aren't looking for really specific information (is it going to be 84 degrees F or 83 degrees F, is it going to rain 0.2 inches or 0.3 inches) but rather ranges of temperature  (cold, mild, hot, really hot) or rainfall (cloudy, light rain, thunderstorms, tornado, hurricane.) There is also the general unpredictability of the weather, so we are used to predictions having some variability.

Normally we just care about the weather where we live and work. If we are traveling we will need to look wider, or if we are interested in the weather where friends/family are living, or where some sporting event is talking place.

We normally only care about the weather near the surface but if you're involved in the airline industry, especially as a pilot, you care about a much larger volume of weather.

If you job is to predict the weather or study the climate then you need much more accurate data over larger areas and longer ranges of time.

How much data is just enough for your purposes and how easy is it to understand:

Weather map version

what are the steps you need to go through to figure out what the temperature in Chicago IL, or Las Vegas, NV.

Weather map version 2

Weather map version 3

Weather map version 4

Weather map version 5

For a slight change here is a precipitation forecast map:

And again, same question, how would you enhance these visualizations if they were software-based?

For simple comparisons to historical values, which makes things somewhat more analytical, this is a nice site:

Some sites are also converting the raw statistics into something more personalized like the following 'Frizz Factor' map from intellicast.

        factor graphic 1

and the corresponding "Frizz Forecast" from Accuweather.

        factor graphic 2

A forecast is going to give us some sense of what is coming - with rain and snow it may help to see the actual shape and previous path of the storm.

        radar map

Radar images are nice for knowing where the storms are right now - moving (animated) radar images are better for knowing where they have been and predicting where they are heading and when they will get there.

low rez: http://www.weather.com/maps/maptype/dopplerradarusnational/usdopplerradar_large_animated.html

high rez: http://radar.weather.gov/Conus/full_loop.php

National detailed radar

Here are a couple images showing how hard it can be to lay out data on a map even with a lot of resolution.

map of international
      fiber optic networks

map of inational fiber
        optic networks

Here is an image from Information Anxiety, P286. Here the problem is over designing the graphic. Trying to make the graphic 'exciting' makes it harder to get information from it. 

'exciting' graphic of US
        weather map

Today its easy to make things look '3D' with software. We need to be careful what view we choose, even of a familiar object.

There was a nice recent introduction to different kinds of visualizations in the June 2010 issue of Communications of the ACM - link

The projects in the class are going to be written in D3.js, so if you should start taking a look at:




Coming Next Time

Introduction to Programming in D3.js

last revision 8/22/15