Week 3

The Basics

The purpose of visualization is to make it easy for the user to see the patterns, the similarities, the differences in the data.

This involves the variation in the data itself, the variation in the representation of that data,  and the ability of a human being to perceive variation.

In general you do not want to let the computer use its default values. Unless you are using a specific program for a specific field the default values will not be right for your work. This is especially true of programs like Word and Excel (though both have improved a lot in the last couple years in this regard).

Tables and simple graphs are going to come up fairly often in visual analytics. Even with all the fancy new visualization options we have there are still very good reasons to use simple tables and charts when possible.

Lets start with tables - the format of a table can greatly enhance or reduce the readability.

Here is a table from the US Environmental Protection Agency from a few years ago - the Total Emissions column of data is centered making it very hard to compare the values within.

National Carbon_Monoxide Emissions in 2002
Source Sector Total Emissions
Electricity Generation 652,314
Fires 14,520,530
Fossil Fuel Combustion 1,499,367
Industrial Processes 2,414,055
Miscellaneous 33,786
Non Road Equipment 22,414,896
On Road Vehicles 62,957,908
Residential Wood Combustion 2,704,197
Road Dust 0
Solvent Use 3,294
Waste Disposal 2,018,496

A better version of the table would be the following where both the sources and the amount of emissions are easier to see and quickly grasp:

National Carbon_Monoxide Emissions in 2002
Source Sector  Total Emissions
 Electricity Generation 652,314 
 Fires 14,520,530 
 Fossil Fuel Combustion 1,499,367 
 Industrial Processes 2,414,055 
 Miscellaneous 33,786 
 Non Road Equipment 22,414,896 
 On Road Vehicles 62,957,908 
 Residential Wood Combustion 2,704,197 
 Road Dust
 Solvent Use 3,294 
 Waste Disposal 2,018,496 

Here is a made-up table - its hard to see any pattern in the Yes/No Values.


A better version (if all of the cells are filled with one of two values) would be:


A different better version of the table using colour to help highlight the pattern would be:


Here is a table from the Nielsen Games page:

The Usage Min % column is hard to read because its left justified.

Original table for game console usage

This version below is easier to read because the right column of numbers is right justified. The decimal points aline and bigger numbers look bigger. I also moved the text off the grid lines to make them more readable.

Revised table for game console usage

Be careful of significant digits

Your table should not show more accuracy than the accuracy of the data collection. The computer will happily compute an average out to an alarming number of digits, but if you only took measurements to one decimal point then that's as far as you should show any derived (average, min, max, median, etc) values.

Programs may also reduce your significant digits by eliminating trailing zeros (turning 4.20 into 4.2) so you will want to force all the data of the same type collected in the same way to have the same number of significant digits.

For presentations, your tables should only show as much accuracy as needed to get your point across. If two values differ by 100 then you don't need to show those values to the third decimal place. The additional detail in the numbers gets in the way of seeing the bigger trend. You can keep another slide hidden in the slide morgue after the end of your talk that has all the explicit details in case someone is interested.

Here is another table from the same Nielsen page. Again left justifying the numbers makes things harder to read, but there are also an issue of significant digits. We can presume since they have been in the survey business a long time that they do have faith in their data out to that degree of significance, and very likely that number of digits is necessary to disambiguate data further down the table, but since they are just presenting the top 10, the extra digits get in the way.

Original table for videogame playing

The next version makes it easier to see the overall relationships. Another possible change would be to convert the data on minutes per week into data on hours per week. Its hard to have an intuitive sense of '546 minutes'. If you are telling a friend how long a movie you saw last night was do you say it was 140 minutes long, or do you say 2 hours and 20 minutes long?

Keep your audience in mind when creating a table. You will want to keep all of your data in its highest resolution form, but when you present it, present just the right amount of detail for the people you will be speaking to. More technical people will want more detail; less technical people will want the information at a higher level. Some people want to see detailed trends, others just overall trends. Don't reuse your charts for different audiences, create new ones targeted towards the specific audience.

Revised table for
          videogame playing time

I should point  out that if I was creating these tables myself for these notes then I would use white text on a black background, since these web pages have a black background

A bit more on text. You have several general choices of font styles to use

And one font, comic sans, deserves some mention on its own. Here is one good link (with profanity) about comic sans. In the summer of 2011 there were quite a few blog posts devoted to a 100 page US Army PowerPoint presentation using comic sans e.g. this one.

Scientists do this kind of thing as well. How long does it take for you to read the title screen here? - https://www.youtube.com/watch?v=nLacmrM5xQw

Since we are focusing on interactive computer-based visualizations, you should start with a sans-serif font like Helvetica and only change it if you have a very good reason.

Here is a nice infographic on type - http://www.buzzfeed.com/lenkendall/a-guide-to-typography-infographic-wh6

Familiar words are recognized by shape

O lny srmat poelpe can raed tihs.
I cdnuolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg. The phaonmneal pweor of the hmuan mnid, aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, t he olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rgh it pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Amzanig huh? yaeh and I awlyas tghuhot slpeling was ipmorantt! if you can raed tihs psas it on !!"

Simple charts

Charts are pretty ubiquitous in visualization and visual analytics, usually used in combination with several other representations of the same data (e.g. geographic) so its important to get the charts right. Its also important to think about the differences between a static chart used in print or on the web, and a dynamic chart that the user can manipulate by hovering over elements with a mouse or clicking on an item in the chart, or having the chart dynamically update based on interactions with other elements of the visualization.

Here is an example charting the population of the USA over the last 8 years. First up is an overly dynamic 3D chart with a hard to read set of population numbers and a trend that is made even more pronounced by the 3D viewpoint. Please do not create charts like this.

Overly 3D chart of US

Here is a less exciting but much more useful version where the data is shown in 2D and the population values have commas to make it easier to see what the numbers actually are. Another good possibility would be to make the vertical column "Population (in Millions)" and then have 270, 275, 280 etc as the vertical values.

More readable 2D chart
        of US population

Here are a couple variants using lines with the actual data points highlighted. The big difference is in the Y-axis. One chart suggests there is slow steady growth; the other suggests rapid steady growth.

Vertical scale
        suggesting slow growth   Vertical
        scale suggesting rapid growth

in all these cases a simple interaction is to allow the user to hover or click on one of the boxes and see the actual data values, or dynamically change the Y axis.

and now lets go back to the video game console data from above.

First let's see a couple charts from the older version of Microsoft Excel. The older Microsoft Excel just wasn't very good at making charts - the colours hurt your eyes, the odd grey background shouldn't be there, etc. Its best just to avoid using the older Excel to make charts. It takes too much time to fix everything that is wrong. Please do not create charts like this.

The latest version of Excel is much better in dealing with colours and layout, but has also included lots of 3D bling that should be avoided. 3D distorts the data and adds in unnecessary details that makes it harder to see what's really going on. Please do not create charts like this.

Instead we can display the data without the 3D. By default Excel with pick the colours for the various data values as seen above. If the data values are unrelated then the colours should be unrelated, but here we could also use the colour to relate consoles made by different manufacturers (blue for Sony, red for Nintendo, green for Microsoft, and grey for Other, with the more saturated colours for their latest releases.)

The pie chart makes it easy to see how each console compares to the whole, but the bar chart makes it easy to see how they compare to each other. In an analysis tool you may need both views simultaneously to see the current year, and then additional visualizations to see the values over time.

Here are a couple other pie chart examples. A good one comes from:

a bad one comes from our local fox news affiliate:

There are many different kinds of charts

A really good book to look at for an introduction to this sort of thing is Edward Tufte's 'The Visual Display of Quantitative Information.'

Another good reference is Robert Harris' Information Graphics - A Comprehensive Illustrated Reference. Here is a nice overview of different kinds of charts:

We will talk about various kinds of charts throughout the course.

Naturalness is an important design principle - better when the properties of the representation match the properties of the thing being represented. 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 reflectively. In experiential cognition we perceive and react efficiently. In reflective cognition we use our decision making skills.


Before you create a chart you should know whether it will eventually appear in colour or greyscale. Colour is more prevalent today than in the past since more people are getting their information in digital form, but some conferences and journals will still only print in greyscale, and some people still get their information through photocopies.

It would be good if the colours you choose also work for people who are colour blind.

8 percent of men
1 percent of women

Are you colour blind? You can check on Wikipedia - http://en.wikipedia.org/wiki/Ishihara_color_test

Here is an image of a color wheel seen with Protanope and Deuteranope colour blindness.

You should at least make sure that you data doesn't blend together or disappear for people who are colour blind The colours I chose in the last couple graphs are OK, but an even better way is to avoid using green in your charts since red/green is the most common form of colour blindness.  Photoshop can be used to check images (View menu, Proof Setup, Color Blindness), as can the tool at http://colororacle.cartography.ch/ and couple good web sites to check your graphics are:  http://www.vischeck.com/vischeck/ and http://colorfilter.wickline.org/

Here is a nice set of on-line color tools:

The Eye

There is a nice diagram of the eye at:

Light is focused by the cornea and the lens onto the retina at the back of the eye.

Vitreous humor - liquid inside the cornea is close to water, and has the same index of refraction as water. If we are under water the light is not refracted, but it is refracted if we are not in water.

Light passing through the center of the cornea and lens hits the fovea (or macula).

Human eye has 2 types of photosensitive receptors: cones and rods



The cones are highly concentrated at the fovea and quickly taper off around the retina. For colour vision we have the greatest acuity at the fovea, or approximately at the center of out field of vision. Visual acuity drops off as we move away from the center of the field of view. However, we are very sensitive to motion on the periphery of our vision, so we can see movement even if we can't see what is moving.

The rods are highly concentrated 10-20 degrees around the fovea, but almost none are at the fovea itself - which is why if you are stargazing and want to see something dim you can not look directly at it.

There is also the optic nerve which is 10-20 degrees away from the fovea which connects your eye to your brain. This is the blind spot where there are no cones and no rods. We can not see anything at this point though we are so used to this that we do not notice it unless we try to see the blind spot.

Bill Sherman's diagram

Try the following link if you want to see (or not see) your blind spot:
with this being the simplest diagram to use

You need to close one eye, look at the plus sign and then move your head towards and back from the screen until the black circle disappears. When you are at the correct distance the size of your blind spot is about the size of that black circle.

Here is a nice short YouTube video that shows the same effect - http://www.youtube.com/watch?v=O7jpJ12lBjg&feature=relmfu

What happens when we walk from a bright area into a dark area, say into a movie theatre? When we are outside the rods are saturated from the brightness. The cones which operate better at high illumination levels provide all the stimulus. When we walk into the darkened theatre the cones don't have enough illumination to do much good, and the rods take time to de-saturate before they can be useful in the new lower illumination environment.

It takes about 20 minutes for the rods to become very sensitive, so dark adjust for about 20 minutes before going stargazing.

Since the cones do not operate well at low light intensities we can not see colour in dim light as only the rods are capable of giving us information. The rods are also more sensitive to the blue end of the spectrum so it is especially hard to see red in the dark (it appears black).

To human beings, brightness (perceived intensity) has a logarithmic scale, not a linear scale which gives us a contrast ration of 100:1 under normal conditions and 1,000,000:1 if we dark adapt.

Our field of view for each eye is 60 degrees inwards towards the nose and 100 degrees outwards, 60 degrees up and 75 degrees down

The 'resolution' of the average human eye has been measured by different people in different ways. In general it seems to be 1 arc minute (where 60 arc minutes  = 1 degree), but that's only in the very center of our field of view at the fovea (within a few degrees) and under bright lighting conditions, with high contrast images, and we can only recognize shapes (e.g. the letter E on a Snellen vision chart) that are twice that big.

Chromatic Colour




Currently believed there are three kinds of cones in the human eye, one attuned to red (more like yellow), one to green, and one to blue (Young and Helmholtz) 

Light is electromagnetic energy with wavelengths from 400nm - 700nm

peak red response at 580nm (reddish-yellow)
peak green response at 545nm (greenish-yellow)
peak blue response at 440nm

There is a nice graph at http://www.normankoren.com/Human_spectral_sensitivity_small.jpg

So the idea is to add an amount of red and an amount of green and an amount of blue to produce a wide range of colours.

Unfortunately we can not generate all the colours that the eye can see using an RGB CRT or LCD or LED at this point. We also can not generate all the colours that the eye can see using photographic film (though it can display a larger part of the visible spectrum than a monitor)

Some advice on the use of colour:

3 kinds of lies: lies, damn lies, and statistics (quote attributed to several different people)

Here is a comparison of 3 graphics of the same data.

The first is from Time Magazine (4/9/79) via Tufte

Oil Prices Represented as Barrels

The second is from the Sunday Times (12/16/79) via Tufte

Oil Prices with odd Y-axis

The modern graphic below from inflationdata.com is a much more truthful representation of the data. Both scales are linear and in easy to understand units. The source of the data is cited. Contextual information is given at interesting points in the graph.

Better Visualization of Oil Prices

Nice graphic, so of course we ask how would you enhance this visualization if it was software-based?

Here is  another way to view the price of oil - geographically - as gasoline prices in the US as of January 2014 by county from gasbuddy.com.  In general prices are pretty similar within each state showing some variety on a zip code basis.


Back to the Lie Factor:

another one from the New York Times, 8/9/78 via Tufte:
          Economy as a Perspective Road

The mileage standards rise from 18 to 27.5 which is a 53% increase, but the difference in the sizes of the lines representing those values from the New York Times is 783% which is almost 15 times larger ... dramatic, but not very truthful. If we graph it without the extra perspective we see the following:

and another one from the Los Angeles Times (8/5/79) via Tufte:

          Family Doctor Graphic

Here a 1D value is represented by a 2D image. The widths of the images are proportional to the values being represented, and the heights of the images are also proportional to the values, which makes the visual differences much greater than the differences in the actual data. If you need to use a series of 2D images to represent a series of 1D values then the 2D areas of those images should be proportional to the values.

here is a nice one from http://www.math.yorku.ca/SCS/Gallery/lie-factor.html
Doctor's Income

There are some graphical embellishments but basically we have two bar charts showing two roughly linear series of data ... so what's wrong?

Below is a more truthful version of the data where the X-axis is spread out linearly:

Doctor's Income as a
          Simple Chart

and finally what Tufte considered one of the worst

what is this chart telling us? It is telling us the percentage of college students that were under 25 from 1972 through 1976. That's only 5 values.


Here is a line chart from http://www.fao.org/worldfoodsituation/en/ showing food prices over the last four years with the years overlapping which can help show seasonal variations

Here is another county-based map - Pop vs Soda vs Coke from

Pop-Soda-Coke Distribution in the US
more data on this at http://en.wikipedia.org/wiki/Names_for_soft_drinks#United_States

There is also a version of this data on a state-by-state basis at manyeyes. What trends would be hidden by a state-by-state view?


Here is an interesting map of US population from Time Magazine


Time Multimedia - This is Where
        We Live

here are some more bad ones - http://www.math.yorku.ca/SCS/Gallery/say-something.html

another issue is how big to make your visualization - here is a partial answer from Google (which has unfortunately moved into their analytics suite and is no longer available as a standalone - http://browsersize.googlelabs.com/

Coming Next Time

Information  Visualization

last revision 9/1/2015