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(c) Chris Curran, 20011 Data Display Techniques Christine R. Curran, PhD, RN, CNA October, 2001.

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Presentation on theme: "(c) Chris Curran, 20011 Data Display Techniques Christine R. Curran, PhD, RN, CNA October, 2001."— Presentation transcript:

1 (c) Chris Curran, Data Display Techniques Christine R. Curran, PhD, RN, CNA October, 2001

2 (c) Chris Curran, Data Versus Information How does one determine which display format to use: Text, Table, Graph, Other…? How does display content / “ink” affect the amount of information obtained? rounding of numbers Labels: when and where use of white space How does color affect our ability to “see” information?

3 Human Cognitive Processes Humans want to organize data The human mind operates by association Humans process data through data reduction strategies Chunking of data Pattern recognition & exceptions to patterns are used to make judgments Analogy & metaphor are often used in learning & recall of information

4 (c) Chris Curran, Data Displays Should Facilitate Perception of salient features Comprehension of information Recall of the information

5 (c) Chris Curran, Data Versus Information Methods used to glean information from the volumes of data available to us: tools (calculators, computers) decision support systems data presentation

6 (c) Chris Curran, The data and the type of task drive the choice of display format How to Choose a Display Format

7 (c) Chris Curran, Types of Data Displays Words Headings Text Numbers Digital Numeric Table Analog Picture Graph Icon Video

8 (c) Chris Curran, Words Avoid all capital letters Use labels or symbols rather than a “key” Use Serif font for text Use San-serif font for headings

9 (c) Chris Curran, Text: Samples TITLE Text should be displayed in Serif font. One should avoid all capital letters. Title Text should be displayed in Serif font. One should avoid all capital letters.

10 (c) Chris Curran, Properties of Numerical Data Displays Digital task: symbolic data: discrete, quantitative focus:specific process: analysis display: table Analog task: spatial data: continuous, qualitative focus:holistic process: perception display: graph, icon

11 (c) Chris Curran, Principles of Numerical Data Displays Arrange data to convey meaning proximity of data use of white space navigation Make patterns and exceptions within the data obvious at a glance (seeing the data) rounding labeling & spacing display format

12 (c) Chris Curran, Digital Display: Tables Use in small data sets (20 numbers to be displayed or less) Used to display numbers

13 (c) Chris Curran, Rules for Table Displays Ehrenberg, 1977 Round to 2 significant or effective digits eliminate leading “0” trailing “0” does not matter Put figures to be compared in columns rather than in rows Add row & column averages (make the main effects explicit) Order rows & columns by size Show larger numbers above smaller numbers

14 (c) Chris Curran, Rules for Table Displays Ehrenberg, 1977 Spacing & layout White space is your friend Use white space to signal the chunks of data Single spacing guides the eye down the column Use gaps (white space) between groups (columns or rows) to guide the eye across the data & to cluster data Data meant to be compared should be close together

15 (c) Chris Curran, Data Rounding “Anyone who cannot learn to cope with rounding errors will probably not get much out of statistical data” Ehrenberg, 1977, pg. 282

16 (c) Chris Curran, Principle The Data should drive the order of the presentation. Displays should not be configured by the structure of the data collection methodology or analysis.

17 (c) Chris Curran, Table: Example

18 (c) Chris Curran, Table: Revised Example

19 (c) Chris Curran, Correlation Matrix: Example

20 (c) Chris Curran, Correlation Matrix: Example

21 (c) Chris Curran, Correlation Matrix: Revised Example

22 (c) Chris Curran, Graphical Data Display: A Form of Decision Support Goals find relevant data in a dynamic environment visualize the semantics of the domain reconceptualize the nature of the problem (Bennett, Toms & Woods, 1993)

23 (c) Chris Curran, The Power of a Graph Enables one to take in quantitative information in a qualitative way, organize it, and see patterns and structure not readily revealed by other means.

24 (c) Chris Curran, Graphical Perception The process of visual decoding of quantitative and categorical data from a graph. Cleveland, 1984

25 (c) Chris Curran, Analog Display: Graphs Used to display large datasets Types of Graphs: Universal - Literal Continuum

26 (c) Chris Curran, Universal Graph: Example

27 (c) Chris Curran, Literal Graph

28 (c) Chris Curran, Graphical Design Concepts & Principles Semantic Mapping (Roscoe, 1968; Kosslyn, 1989) Configural Displays (Garner, 1970) Chunking (Newell & Simon, 1973) Theory of Graph Comprehension (Pinker, 1981) 8 Visual Variables (Bertin, 1981) Emergent Features (Pomerantz, 1981) Data-Ink Ratio & Small Multiple (Tufte, 1983,1990, 1997) Elementary Perceptual Tasks (Cleveland & McGill, 1984) Proximity Compatibility (Wickens, 1986) Metaphor Graphics (Cole, 1988) Cognitive Fit (Vessey, 1991)

29 (c) Chris Curran, Design Principles for Computer Displays (Cole, 1994) Design for the analog mind and both hemispheres Design for correct encoding of information (represent the user’s model) Provide a clear context

30 (c) Chris Curran, Graphic Design

31 (c) Chris Curran, Visual Decoding of Graphs Requires Pattern Perception Pattern perception requires: detection visual grouping of a pattern estimation

32 (c) Chris Curran, Elementary Perceptual Tasks (ordered from most to least accurate) Position along a common scale Positions along nonaligned scales Length, Direction, Angle Area Volume, Curvature Shading, Color Saturation Cleveland & McGill, 1984

33 (c) Chris Curran, Position Along a Common Scale

34 (c) Chris Curran, Position Along Non-Aligned Scales

35 (c) Chris Curran, Length

36 (c) Chris Curran, Direction

37 (c) Chris Curran, Angle

38 (c) Chris Curran, Area

39 (c) Chris Curran, Volume

40 (c) Chris Curran, Curvature

41 (c) Chris Curran, Shading

42 (c) Chris Curran, Color Saturation

43 (c) Chris Curran, Elementary Perceptual Tasks Cleveland & McGill, COLOR SATURATION

44 (c) Chris Curran, Common Graphs by Elementary Perceptual Task

45 (c) Chris Curran, Recommendations: Based on Graphical Perception Parts of a Whole dot chart grouped dot chart bar charts (instead of divided bars or pie charts) Framed Rectangle Charts (instead of Shaded Statistical Maps Cleveland & McGill, 1984

46 (c) Chris Curran, Dot Chart

47 (c) Chris Curran, Grouped Dot Chart

48 (c) Chris Curran, Bar Charts

49 (c) Chris Curran, Grouped Bar Chart

50 (c) Chris Curran, Divided Bar Chart

51 (c) Chris Curran, Pie Chart

52 (c) Chris Curran, Framed Rectangle

53 (c) Chris Curran, Research Findings: Graphical Perception Perception of Change Line Graphs Grouped Bar Graphs Perception of Proportion Pie Charts Divided Bar Graphs (differs from Cleveland & McGill) Hollands & Spence, 1992

54 (c) Chris Curran, Cognitive Fit Vessey, 1991

55 (c) Chris Curran, Proximity Compatibility Principle To the extent that multiple aspects of data or information must be mentally integrated, they should be physically integrated or proximate in the display. Wickens, 1986

56 (c) Chris Curran, Emergent Features A property of the configuration of multiple dimensions of an object that does not exist when the dimensions are specified independently. Pomerantz, 1981

57 (c) Chris Curran, Innovative New Designs Metaphor Graphics

58 (c) Chris Curran, Clinical Data Display Cole, 1988

59 (c) Chris Curran, Metaphor Graphics: Database Display (Cole, 1988) Male Female

60 (c) Chris Curran, Clinical Data Display Patient Rectangle Ventilator Rectangle Rate (width) Volume (depth) Oxygen Alveolar Space Dead Space

61 (c) Chris Curran, Metaphor Icon Graph: Example

62 (c) Chris Curran, Metaphor Icon Graph: Questionable Example

63 (c) Chris Curran, Clinical Data Display Powsner, S. & Tufte,E.R., 1994

64 (c) Chris Curran, Tufte, 1997 Clinical Data Display

65 (c) Chris Curran, Color

66 (c) Chris Curran, Why Do We Use Color? Formatting Purposes Group data (patterns) Create focused attention to specific data (highlight data) Semantic Purpose (encode data) Create Realism Aesthetic Purpose (visual appeal)

67 (c) Chris Curran, Color is superior to size, shape or brightness as a mechanism to target a feature in a display When to Use Color

68 (c) Chris Curran, Eleven Colors That Are Never Confused White Black Gray Red Green Yellow Blue Pink Brown Orange Purple Kosslyn, 1994

69 (c) Chris Curran, General Guidelines: Use of Color Use warm colors in the foreground Have a large luminance contrast between the foreground and background Adjacent colors should have different levels of brightness Redundant color coding improves search tasks Color should be a secondary cue (always design for monochrome first) Travis, 1991

70 (c) Chris Curran, Kinds of Color Contrasts Light - Dark Cold - Warm Contrast of : hue saturation Complimentary Contrast (from color wheel)

71 (c) Chris Curran, General Guidelines: Colors Colors have cultural significance. Each individual sees, feels, and evaluates color in a very personal way.

72 (c) Chris Curran, General Guidelines: Colors Red: alert values, “warning” Blue: most easily distinguished but does not photocopy well Optic Yellow: (a greenish yellow color) most visible to humans Shades of Grey: Best for those who are color blind Use Conventional colors (e.g., forests are green)

73 (c) Chris Curran, Take Home Message How Data are displayed matters Displays should be configured around the data and not how it was obtained

74 (c) Chris Curran, Things to Consider How much data do I have? What cognitive task is needed? Are the data continuous or discrete? Am I making an exact or a relative judgment? Are the data static or dynamic? Are there display conventions about the subject area? Is the domain familiar to the audience?

75 (c) Chris Curran, Current Recommendations Tables: good for small datasets & to depict quantitative data where specific data are needed Pie Graphs: good for judging proportion Bar Graphs:display change or trends; excellent universal graph (better than line) Icons: good for synthesis of data and meaning; may be best for qualitative (relative) judgments Shapes / Figures: Best to display integrated data


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