The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, … because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.Hal Varian, Google’s Chief Economist The McKinsey Quarterly, Jan 2009
Why Infoviz?
- Computers can store and process information at super-human scale
- But people are still superior to computers in many information processing activities
- See patterns
- Notice oddities
- Impose order
- Choose a suitable model
- Combination is more powerful than either
- Even if computer does most of the work, need a way for human to tell it what work to do
Why Infoviz?
Analyze
- Expand memory
- Find patterns
- Develop and assess hypotheses
- Discover errors in data
Communicate
- Communicate information to others
- “Seeing is Believing”,
- “Picture worth 1000 words”
- Share and persuade
- Collaborate and revise
Human Insight
| I | II | III | IV | ||||
|---|---|---|---|---|---|---|---|
| x | y | x | y | x | y | x | y |
| 10.0 | 8.04 | 10.0 | 9.14 | 10.0 | 7.46 | 8.0 | 6.58 |
| 8.0 | 6.95 | 8.0 | 8.14 | 8.0 | 6.77 | 8.0 | 5.76 |
| 13.0 | 7.58 | 13.0 | 8.74 | 13.0 | 12.74 | 8.0 | 7.71 |
| 9.0 | 8.81 | 9.0 | 8.77 | 9.0 | 7.11 | 8.0 | 8.84 |
| 11.0 | 8.33 | 11.0 | 9.26 | 11.0 | 7.81 | 8.0 | 8.47 |
| 14.0 | 9.96 | 14.0 | 8.10 | 14.0 | 8.84 | 8.0 | 7.04 |
| 6.0 | 7.24 | 6.0 | 6.13 | 6.0 | 6.08 | 8.0 | 5.25 |
| 4.0 | 4.26 | 4.0 | 3.10 | 4.0 | 5.39 | 19.0 | 12.50 |
| 12.0 | 10.84 | 12.0 | 9.13 | 12.0 | 8.15 | 8.0 | 5.56 |
| 7.0 | 4.82 | 7.0 | 7.26 | 7.0 | 6.42 | 8.0 | 7.91 |
| 5.0 | 5.68 | 5.0 | 4.74 | 5.0 | 5.73 | 8.0 | 6.89 |
- 4 x-y data sets
- Identical means, std. deviations, correlations, regressions
- What’s the difference?
The Datasaurus
Justin Matejka, George Fitzmaurice. Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. CHI 2017
Cholera Map
- John Snow
- Initiated the study of epidemiology
- Cholera outbreak in London
- At the time, believed transmitted by "miasma" through the air
- Snow plotted homes of infected patients
- Identified center of infection at a water pump
- Removing pump handle ended the outbreak
Nightingale's Rose
Example of polar area diagram by Florence Nightingale (1820–1910).
This "Diagram of the causes of mortality in the army in the East" was published in Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army and sent to Queen Victoria in 1858.
This graphic indicates the number of deaths that occured from preventable diseases (in blue), those that were the results of wounds (in red), and those due to other causes (in black).
The legend reads: The Areas of the blue, red, & black wedges are each measured from the centre as the common vertex. The blue wedges measured from the centre of the circle represent area for area the deaths from Preventable or Mitigable Zymotic diseases, the red wedges measured from the centre the deaths from wounds, & the black wedges measured from the centre the deaths from all other causes. The black line across the red triangle in Nov. 1854 marks the boundary of the deaths from all other causes during the month. In October 1854, & April 1855, the black area coincides with the red, in January & February 1855, the blue coincides with the black. The entire areas may be compared by following the blue, the red, & the black lines enclosing them.
- Work by Florence Nightingale
- Famed nurse, but also accomplished statistician
- Innovator in info viz
- Deaths during Crimean War
- Red: Wounds
- Blue: disease
- Led to commission of inquiry on sanitation
- Led to significant improvements in barracks and hospitals
Napoleon's Russian Campaign
Small Multiples
- Eadweard Muybridge (1878): do a horse's feet all leave the ground at once during a gallop?
- Small multiples arrange many similar visualizations together
- Permit comparison
- Animation uses adjacency in time instead of space
NYC Weather
Newspapers and magazines usually choose simplicity over detail, because it is hard to show much information in a single, comprehensible display. A collection of truly bad examples from the popular media are shown on our Darts pages. Here, the attention to detail, and to graphic design are evident.
This graph, from the New York Times (Jan. 7, 1979) shows--- both valiantly, and sucessfully--- 2200 numbers which summarize the trends and patterns in weather in New York City in 1980. The three aligned charts show temperature, precipitation, and relative humidity. In the graph of temperature, the area is filled between the daily low and daily high.
What makes this graph successful, in spite of the large amount of information presented are (a) clear visual comparisons between the 1980 data and the long-run average, (b) clear textual labels, (c) visual segregation between the three series. For example, it is easy to see that March and April were about of normal temperature, but a lot wetter.
- NY Times, 1981
- 2200 numbers in a comprehensible aggregate
- Look at your own city at this link
What is Visualization?
-
Transformation of the symbolic into the geometric
— McCormick et al. 1987 -
… finding the artificial memory that best supports our natural means of perception
— Bertin 1967 -
The use of computer-generated, interactive, visual representations of data to amplify cognition
— Card, Mackinlay, & Shneiderman 1999 - Abstraction → Perception
Why?
- “A picture is worth a thousand words”
- We receive information through our senses
- Far higher bandwidth than thought
- React to an image much faster than thinking
- Perceive and remember details without thinking
- Transform abstract data to visual variables
- Leverage pre-attentive processing
- Information we process without thinking
Taxonomy
