The visual display of quantitative data Joyce Chapman, Consultant for Communications & Data Analysis State Library of North Carolina,

Agenda  Visual perception and quantitative communication  Fundamental concepts of graphs  General design for communication This webinar will be recorded and made available here: 2

3 What is the message?

Visual perception and quantitative communication 4

Stimulus  Stimulation  Perception 5

Pre-attentive processing 6 Extremely fast, pre-conscious visual processing

Pre-attentive processing

Pre-attentive processing

Pre-attentive attributes Attributes of form 9

Pre-attentive attributes Attributes of color 10

Pre-attentive attributes Attributes of spatial position and motion 11

But which of these visual attributes can be used to encode quantitative information? 12

Pre-attentive attributes Very precise quantitative perception 13

Pre-attentive attributes Less precise quantitative perception 14

Pre-attentive attributes Scatterplots take advantage of 2D spatial positioning 15

Pre-attentive attributes Line charts also take advantage of 2D spatial positioning 16

Pre-attentive attributes Bar charts take advantage of 2D spatial positioning (the end of each bar) and line length 17

Pre-attentive attributes The humble pie chart 18

Pre-attentive attributes The humble pie chart Which is larger, B or D? 19

Pre-attentive attributes Some limitations of our brains  Up to 8 different hues  Up to 4 different orientations or sizes  Less than 10 of other attributes  We can only process one attribute at a time 20

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Fundamental concepts of graphs 23

Table A structure for organizing and displaying information. Quantitative values are encoded as text. Graph A visual display of quantitative information. Quantitative values are encoded as visual objects. 24

 When to use tables  When you will need to look up individual values  When you will need to compare individual values  When precise values are required  When the quantitative information to be communicated involves more than one unit of measure  When to use graphs  When the message is contained in the shape of values  To reveal relationships among multiple values  When there is a large amount of data to distill 25

How to choose a graph type Different types of quantitative relationships require different forms of graphs  Points  Lines  Bars  Shapes with 2D area 26

How to choose a graph type  Points 27

How to choose a graph type  Lines 28

How to choose a graph type  Lines 29

How to choose a graph type  Bars 30

How to choose a graph type  2D area 31

Relationships in graphs 1. Nominal comparison 2. Time series 3. Correlation 4. Part-to-whole 5. Deviation 6. Distribution 32

Relationships in graphs Nominal comparison Points lines bars 2D area ? 33

Relationships in graphs Nominal comparison Points lines bars 2D area  Categorical subdivisions have no connection  Values are discrete  Aims to highlight relative size 34

Relationships in graphs Nominal comparison 35

Relationships in graphs Time series Points lines bars 2D area ? 36

Relationships in graphs Time series Points lines bars 2D area  Our culture visualizes time as linear and left to right  The visual weight of bars detracts from message in the shape of the data  Points don’t work because dots floating in space cannot denote the sequential nature of time 37

Relationships in graphs Time series 38

Relationships in graphs Correlation Points lines bars 2D area ? 39

Relationships in graphs Correlation Points lines bars 2D area  Must show two sets of quantitative values in relation to each other instead of one  Both X and Y axis provide quantitative scales 40

Relationships in graphs Parts-to-whole Points lines bars 2D area ? 41

Relationships in graphs Parts-to-whole Points lines bars 2D area  Discrete value comparison  Individual bars are better than stacked bars 42

Relationships in graphs Parts-to-whole 43

Relationships in graphs Parts-to-whole 44

Relationships in graphs Deviation Points lines bars 2D area ? 45

Relationships in graphs Deviation Points lines bars 2D area  Usually teamed with another relationship  When combined with time-series, lines are best  When combined with anything else or standing alone, bars are usually used. 46

Relationships in graphs Deviation 47

Relationships in graphs Distribution Points lines bars 2D area ? 48

Relationships in graphs Distribution Points lines bars 2D area boxplots  The shape of the distribution is most important  Consider whether you have one or many distributions (lines for multiple, histogram for single) 49

Relationships in graphs Histograms: distribution 50

Relationships in graphs Box plots: distribution 51

General design for communication 52

"Above all else show the data." – Edward Tufte 53

Data-ink ratio 54

Data-ink ratio 55

Data-ink ratio 56

Who, what, where, when? 57 Create by the News & Observer, Contact Figure 1.

Avoid “Chart junk”: 3D effects for non-3D data 58

Maintain visual correspondence to quantity 59

60  eee Use zero-based scales  How much more satisfied were patrons at the Lilly library than the Iris library? With the baseline at zero  How much more satisfied were patrons at the Lilly library than the Iris library?

Concepts and charts for this presentation were borrowed from this book  Few, Stephen. (2004). Show me the numbers: designing tables and graphs to enlighten. Further reading, if you’re interested  Few, Stephen. (2009). Now you see it: simple visualization techniques for quantitative analysis.  Tufte, Edward. (1983). The Visual Display of Quantitative Information. 61

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Example: How could this chart be improved? Find more examples here: 64

Fix this chart Executives want to understand both the range of selling prices and the mean selling prices over 12 months. 65

Fix this chart Executives want to understand both the range of selling prices and the mean selling prices over 12 months. 66