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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, 20011 Data Display Techniques Christine R. Curran, PhD, RN, CNA October, 2001

2 (c) Chris Curran, 20012 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, 20014 Data Displays Should Facilitate Perception of salient features Comprehension of information Recall of the information

5 (c) Chris Curran, 20015 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, 20016 The data and the type of task drive the choice of display format How to Choose a Display Format

7 (c) Chris Curran, 20017 Types of Data Displays Words Headings Text Numbers Digital Numeric Table Analog Picture Graph Icon Video

8 (c) Chris Curran, 20018 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, 20019 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, 200110 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, 200111 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, 200112 Digital Display: Tables Use in small data sets (20 numbers to be displayed or less) Used to display numbers

13 (c) Chris Curran, 200113 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, 200114 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, 200115 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, 200116 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, 200117 Table: Example

18 (c) Chris Curran, 200118 Table: Revised Example

19 (c) Chris Curran, 200119 Correlation Matrix: Example

20 (c) Chris Curran, 200120 Correlation Matrix: Example

21 (c) Chris Curran, 200121 Correlation Matrix: Revised Example

22 (c) Chris Curran, 200122 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, 200123 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, 200124 Graphical Perception The process of visual decoding of quantitative and categorical data from a graph. Cleveland, 1984

25 (c) Chris Curran, 200125 Analog Display: Graphs Used to display large datasets Types of Graphs: Universal - Literal Continuum

26 (c) Chris Curran, 200126 Universal Graph: Example

27 (c) Chris Curran, 200127 Literal Graph

28 (c) Chris Curran, 200128 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, 200129 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, 200130 Graphic Design

31 (c) Chris Curran, 200131 Visual Decoding of Graphs Requires Pattern Perception Pattern perception requires: detection visual grouping of a pattern estimation

32 (c) Chris Curran, 200132 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, 200133 Position Along a Common Scale

34 (c) Chris Curran, 200134 Position Along Non-Aligned Scales

35 (c) Chris Curran, 200135 Length

36 (c) Chris Curran, 200136 Direction

37 (c) Chris Curran, 200137 Angle

38 (c) Chris Curran, 200138 Area

39 (c) Chris Curran, 200139 Volume

40 (c) Chris Curran, 200140 Curvature

41 (c) Chris Curran, 200141 Shading

42 (c) Chris Curran, 200142 Color Saturation

43 (c) Chris Curran, 200143 Elementary Perceptual Tasks Cleveland & McGill, 1984 10 000 COLOR SATURATION

44 (c) Chris Curran, 200144 Common Graphs by Elementary Perceptual Task

45 (c) Chris Curran, 200145 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, 200146 Dot Chart

47 (c) Chris Curran, 200147 Grouped Dot Chart

48 (c) Chris Curran, 200148 Bar Charts

49 (c) Chris Curran, 200149 Grouped Bar Chart

50 (c) Chris Curran, 200150 Divided Bar Chart

51 (c) Chris Curran, 200151 Pie Chart

52 (c) Chris Curran, 200152 Framed Rectangle

53 (c) Chris Curran, 200153 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, 200154 Cognitive Fit Vessey, 1991

55 (c) Chris Curran, 200155 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, 200156 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, 200157 Innovative New Designs Metaphor Graphics

58 (c) Chris Curran, 200158 Clinical Data Display Cole, 1988

59 (c) Chris Curran, 200159 Metaphor Graphics: Database Display (Cole, 1988) Male Female

60 (c) Chris Curran, 200160 Clinical Data Display Patient Rectangle Ventilator Rectangle Rate (width) Volume (depth) Oxygen Alveolar Space Dead Space

61 (c) Chris Curran, 200161 Metaphor Icon Graph: Example

62 (c) Chris Curran, 200162 Metaphor Icon Graph: Questionable Example

63 (c) Chris Curran, 200163 Clinical Data Display Powsner, S. & Tufte,E.R., 1994

64 (c) Chris Curran, 200164 Tufte, 1997 Clinical Data Display

65 (c) Chris Curran, 200165 Color

66 (c) Chris Curran, 200166 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, 200167 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, 200168 Eleven Colors That Are Never Confused White Black Gray Red Green Yellow Blue Pink Brown Orange Purple Kosslyn, 1994

69 (c) Chris Curran, 200169 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, 200170 Kinds of Color Contrasts Light - Dark Cold - Warm Contrast of : hue saturation Complimentary Contrast (from color wheel)

71 (c) Chris Curran, 200171 General Guidelines: Colors Colors have cultural significance. Each individual sees, feels, and evaluates color in a very personal way.

72 (c) Chris Curran, 200172 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, 200173 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, 200174 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, 200175 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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