Just to prove that the graphic presentation is new, take a look at this selection of images from  BibliOdyssesy, a blog devoted to “amazing archival images from the internet”.  (There’s a book too.)  The image above is titled, “A timetable indicating the differences in time between the principle cities of the world”, with their air line distances from Washington.  It was published in 1883, in Philadelphia by WM Bradley.

These images come from the David Rumsey map collection.  This is the life’s work of a map collector who has created a virtual museum.  (This must surely be the future of curation.  Tom Campbell, the new Director of the Metropolitan Museum of Art, plans to put the Met’s entire collection online.)

My first reaction looking at these is “where’s the interactivity, man?”  We’re so used to things popping away in flash, that these look rather flat.   But when you look at them closely, there’s an intensity to the way every calculation is made, then drawn, by a human.

What’s interesting about them is that they have the same mixture of amateurism and fascination with new technology (in this case, printing technology) that has energized today’s data visualization revolution.

Monday I wrote a post about a very annoying graph that keeps popping up in Keynote presentations.  Edward Tufte invented the concept of Chart Junk.  His most famous work – The Visual Display of Quantitative Information was published in 1983.  It is probably the most important book ever written on data visualization.  It made the top 100 non-fiction books of the 20^th century on Amazon.com.  It is packed with examples of best and worst practices in the history of data visualization.  Even the book itself is designed beautifully.  Tufte wanted its design to follow the principles it put forward.  He therefore decided to publish it himself, having to take a 2^nd mortgage to finance it.  You have to admire the man’s determination!  In this book Tufte laid out his key data visualization principles.   

Graphical Integrity

Visual representations of data must tell the truth.  Tufte shows a whole range of graphs that either over or under represent the effects in the data.  He does this by calculating a graph’s Lie Factor which can be calculated by dividing the size of the effect shown in the graphic by the size of the effect in the data.  If the Lie Factor is greater than 1 the graph overstates the effect.  Tufte goes on to list the following 6 principles of graphical integrity : 

1.  The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented

2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data.

3. Show data variation, not design variation.

4. In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.

5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data.

Graphics must not quote data out of context.

Here is one of Tufte’s examples of a graph with low graphical integrity.  According to Tufte the Lie Factor of this graph is 14.8.  A numerical change of 53% is represented by a graphical change (size of horizontal lines) of 783%.

Data-Ink

Data Ink is the ink on a graph that represents data.  Tufte claims that good graphical representations maximize data-ink and erase as much non-data-ink as possible.  He put forward the data-ink ratio which is calculated by 1 minus the proportion of the graph that can be erased without loss of data-information.  He puts forward the following 5 principles related to data ink :

1. Above all else show data.

2. Maximize the data-ink ratio.

3. Erase non-data-ink.

4. Erase redundant data-ink.

5. Revise and edit

Tufte tests these principles on a whole range of examples to come up with a wide range of fresh designs that dramatically improve the legibility of the graphs.  Here is an example with a very high data-ink ratio.  It’s an electroencephalogram – a graph that records the electrical activity from the brain.  This graph would have a data-ink ratio of 1.

Chartjunk

Tufte has a whole chapter dedicated to what he calls Chartjunk – the excessive and unnecessary use of graphical effects in graphs.  He calls out moiré vibration, heavy grids and self-promoting graphs that are used to demonstrate the graphic ability of the designer rather than display the data.  This is according to Tufte possibly the worst graph ever : “A series of weird three-dimensional displays appearing in the magazine of American Education in the 1970’s delighted the connoisseurs of the graphically preposterous.  Here five colors report, almost by happenstance, only 5 pieces of data (since the division within each adds to 100%).  This may well be the worst graphic ever to find its way into print.”

Data Density

The data density of a graph is the proportion of the total size of the graph that is dedicated displaying data.  Tufte prefers high data density graphs.  He wants us to maximize data density and the size of the data matrix within reason.  One way of achieving this he claims is through the Shrink Principle.  He claims that most graphs can be shrunk way down without losing legibility or information.

Small Multiples

Small multiples are series of the same small graph repeated in one visual.  Tufte says that small multiples are a great tool to visualize large quantities of data and with a high number of dimensions.  Below is an example that usesTufte’s sparklines to create a graph of small multiples.  According to Tufte sparklines are data-intense, design-simple, word-sized graphics.  Here is an example of how they are use to show movement in stocks.  This graph has pretty high data density as well.

The New York Times created an interactive map that shows where the outbreaks of the Swine Flue are located.  

These types of epidemiological maps have been around for a while.  In fact, they were the very first examples of geographic representations of data.  Probably the most famous example is John Snow’s map of the 1854 Cholera outbreak in London. 

Snow plotted the cholera cases on a map of Soho in London and he marked the water pumps with a cross.  This showed that there was a high concentration of cholera cases around a water pump on Broad Street. 

Click on the map to download high resolution maps

This evidence prompted the local authorities to investigate the pump.  They found that it was contaminated and they replaced its handle.  This is a great example of the power of data visualization.  Finding the cause of the local epidemic could have been achieved through statistical analysis but here a visual representation of the data on a map was far more efficient. 

John Snow was thanked for his contribution to the fight against cholera with a commemorative pump on the exact location of the old contaminated one.  The pub on the corner was also named after him.  John Snow however was a teetotaler – he only drank boiled water … .