dealing with data from cameras
||User Interface Design||developing
effective user interfaces
visualization of different types of data
|CS 425||Computer Graphics I||basics of how computers create images on screens|
||Video Game Programming||creating complete audio visual interactive (and fun) experiences|
|CS 428||Virtual, Augmented, Mixed Reality||developing interactive head and hand tracked experiences|
Webster defines Visualization as:
Hamming: "The purpose of computing is insight not
What are the advantages? (adapted from [Ware 2000])
How do we make good visualizations? (adapted from [Tufte 1983])
William Playfair invented modern bar charts, as well as line and area charts in ' The Commercial and Political Atlas' in 1786 (though they built on earlier work from Joseph Priestly in the mid 1700ds), and pie charts in 'Statistical Breviary' in 1801.
We start off
talking about Charles Joseph Minard's 1861 graphic showing
Napoleon's losses during his 1812 march to and from Moscow -
regarded as one of the best statistical graph ever drawn ... why?
The image is discussed in detail on p41 of The Visual Display of Quantitative Information
The chart show
Like many analytical visualizations this one gives us a way to see relationships between different types of related data that help explain what we are seeing after the events.
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?
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 - more information on this version and other versions of the map can be found at: https://pdfs.semanticscholar.org/5550/ad5f21ef9af9635a393c53e534ff5db1457d.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.
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
||# of Fatal Attacks
|9/05||36||45|| 10% of
neighborhood now dead within 1 week
of population had left the area
||pump handle removed||9/29
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 his hypothesis is wrong.
John Snow (with help from
the local Reverend Henry Whitehead)
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 and brought water
back from that pump on the way home.
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.
We do not have data on
deaths based on age or sex from this outbreak, but we do have
that data from a cholera outbreak in Naples around the same
time, showing the percentage of people in those groups that
died. Any hypotheses made based on the geographic data should
also match this data.
but its not just about making a graphic, but making
graphic. A bad graphic may hide the truth depending on how you
cluster the 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'
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.
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.
Since there is already a lot of life and death in these examples lets look at one more famous one. There is a lot of data on the Titanic with very detailed datasets available. At the highest level one can count the number of survivors and victims (approx 499 passengers surviving out of 1316, and 212 crew members surviving out of 885) which tells one story, and then you can go deeper into the data and see other stories. Some of these stories are more obvious with raw numbers, and some are more obvious with percentages. Often we need both to tell a more complete story.
Instead of looking at overall numbers we can look at some overall percentages.
Putting the data in a
simple table gets us an initial visualization that can help us
see some trends, but already with 4 attributes its getting
tricky to summarize the data in a single table, so we have to
make some choices about what we are going to focus on.
|Class||male adult||male child||female adult||female child|
|Class||male adult||male child||female adult||female child|
the data to percentages focusing on survival can give us some
more insight, again starting at a high level, and then going a
|Class||male adult||male child||female adult||female child|
And then we can take this
data and convert it into simple visualizations like bar charts,
stacked bar charts, or pie charts.
I'd like you to take 10
minutes and create some quick visualizations from this data by
drawing, either using pencil and paper or using your finger on a
screen. The idea is to get some rough visualizations out quickly
that you can turn in by taking photos or screen shots of.
Now lets look at some common mistakes with showing data on maps
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:- scale of black lines, black, dark grey, black squares, light grey is not an ordered additive sequence - the viewer must keep referring back to the legend to try and figure out which is more
Here is the colour scheme
that WeatherBug used back in 2009:
by 2020 WeatherBug had adopted a better color scheme, and a map with fewer distracting elements - https://www.weatherbug.com/news/Weekly-Flu-Update
and again, how would you enhance this visualization if it was software-based rather than static?
Here is a blank map of the US
showing the state borders, Use the H1N1 percentage data above to
come up with a better map. You can use software to fill in the
states or print it out and color it in by hand. Agin you will be
turning this in either by taking a photo of your drawing or a
Some basic principles from Norman:
Representations that make use
of spatial and perceptual relationships make more effective
use of our brains. If these representations use arbitrary
symbols then we need to use mental transformations, mental
comparisons and other mental processes, forcing us to think
In experiential cognition we perceive and react efficiently. In reflective cognition we take time to use our decision making skills.
How big is an acre
Here is a familiar image in an unfamiliar orientation.
information is first presented, the user should be able to quickly
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. If you start out looking at the entire planet that may not be helpful to you either.
One of the most cited data visualization mantras which is a pretty good starting point for most visualizations: Schneiderman: "Overview first, zoom and filter, details on demand"
But as datasets get bigger and bigger with more and more dimensions and it becomes harder to even know what an overview would be, other mantras are starting to appear such as Van Ham and Perer: "Search, show context, expand on demand".
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.
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.
just care about the weather where we live and work, and our apps
focus on that. 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, or where a
newsworthy event is taking place.
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:
what are the
steps you need to go through to figure out what the temperature in
Chicago IL, or Las Vegas, NV?
For a slight change here is a precipitation forecast map:
And again, same question, how
would you enhance these visualizations if they were
You can also get maps for air
quality, pollen, etc.
Some sites are also converting the raw statistics into
something more personalized like the following 'Frizz Factor'
map from intellicast and the corresponding "Frizz
Forecast" from Accuweather.
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
high rez: http://radar.weather.gov/Conus/full_loop.php
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.
Please check the Homework tab under assignments in the top menu bar each week as there will generally be 'in class' assignments and after class assignments each week, including this one.