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© Anselm Spoerri Lecture 4 Human Visual System –Recap –3D vs 2D Debate –Object Recognition Theories Tufte – Envisioning Information
© Anselm Spoerri Human Visual System – Recap Sensory Representations Effective because well matched to early stages of neural processing Physical World Structured Stages of Visual Processing 1 Rapid Parallel Processing 2Slow Serial Goal-Directed Processing Visual System Detects CHANGES + PATTERNS Luminance Channel More Important than Color Pre-Attentive Features Position Color Simple Shape = orientation, size Motion Depth
© Anselm Spoerri Gestalt Laws – Recap Proximity Similarity Continuity Symmetry Closure Relative Size Figure and Ground
© Anselm Spoerri Space Perception – Recap Depth Cues Shape-from-Shading Shape-from-Contour Shape-from-Texture Shape-from-Motion
© Anselm Spoerri Simple Lighting Model – Recap Diffuse Lambertian SpecularAmbient Shadows Light from above and at infinity Diffuse, Specular and Ambient Reflection Depth Cues
© Anselm Spoerri Depth Cues – Relative Importance – Recap Depth Contrast Depth (meters) Occlusion Relative size Convergence accommodation Binocular disparity Motion parallax Aerial
© Anselm Spoerri 3D vs 2D Debate - Display Abstract Data in 3D? Depth Cue Theory –Depth cues are environmental information about space Occlusion most important Depth Cue Perspective may not add anything by itself Stereo important for Close Interaction Motion important for 3D layout Surface Perception –Shape-from-Shading –Shape-from-Texture
© Anselm Spoerri Relative Position Judgment Fine Judgments - threading a needle –Stereo is important –Shadows –Occlusion Large Scale Judgments –Perspective –Motion parallax –Stereo is not important
© Anselm Spoerri Image + Object Recognition Properties of Image Recognition –Remarkable image recognition memory –Up to 5 images per second –Applications in image searching interfaces –Easier to Recognize than to Recall Image Based Theories –Template theories based on 2D image processing Structural 3D Theories –Extract structure of a scene in terms of 3D primitives
© Anselm Spoerri Template Theories Template with simple morphing operations
© Anselm Spoerri Template Theories – Scale Matters Visual degrees = 4 optimal for object perception
© Anselm Spoerri Geon Theory
© Anselm Spoerri Geon Theory (cont.) 3D Primitives “Geons” Structural skeleton Shape from shading is also primitive
© Anselm Spoerri Canonical Silhouettes
© Anselm Spoerri Recognition – Processing Stages
© Anselm Spoerri Pattern Finding & Recognition – 3D vs 2D 34% memory errors 21% errors 20% memory errors 11.4% errors
© Anselm Spoerri Edward Tufte Books The Visual Display of Quantitative Information Envisioning Information Visual Explanations
© Anselm Spoerri Tufte - Minard's Napoleon's March to Moscow
© Anselm Spoerri Tufte - Escape Flatland: Napoleon's March Enforce Visual Comparisons Width of tan and black lines gives you an immediate comparison of the size of Napoleon's army at different times during march. Show Causality Map shows temperature records and some geographic locations that shows that weather and terrain defeated Napoleon as much as his opponents. Show Multivariate data Napoleon's March shows six: army size, location (in 2 dimensions), direction, time, and temperature. Use Direct Labeling Integrate words, numbers & images Don't make user work to learn your "system.” Legends or keys usually force the reader to learn a system instead of studying the information they need. Design Content-Driven
© Anselm Spoerri Tufte – Challenger Data: Launch? Graph obscures important variables of interest: temperature is shown textually and graphically; degree of damage is not mapped onto a nominal scale
© Anselm Spoerri Tufte – Challenger Data: Launch? Diagrams can lead to great insight, but also to lack of it
© Anselm Spoerri Cause of cholera epidemic in London in 1854? Modified in Visual Explanations by Edward Tufte, Graphics Press, 1997 John Snow’s deduction that a cholera epidemic was caused by a bad water pump
© Anselm Spoerri Tufte’s Measures Maximize data density Data density of graphic = Number entries in data matrix Area of data graphic Measuring Misrepresentation close to 1 Size of effect shown in graphic Size of effect in data Lie factor = Data ink ratio = Data ink Total ink used in graphic Maximize data-ink ratio
© Anselm Spoerri Tufte - Graphical Displays Should Show Data Focus on Content instead of graphic production Avoid Distorting what Data has to say Make Large Data Sets Coherent Encourage Eye to Compare Different Pieces of Data Reveal Data at several Levels of Detail Closely integrate Statistical and Verbal Descriptions
© Anselm Spoerri Example Stock market crash?
© Anselm Spoerri Example Show entire scale
© Anselm Spoerri Example Show in context
© Anselm Spoerri Tufte - How to Exaggerate with Graphs “Lie factor” = 2.8
© Anselm Spoerri Tufte - How to Exaggerate with Graphs “Lie factor” = 2.8 Error: Shrinking along both dimensions
© Anselm Spoerri When to use which type? Line Graph –x-axis requires quantitative variable –Variables have contiguous values –familiar/conventional ordering among ordinals Bar Graph –comparison of relative point values Scatter Plot –convey overall impression of relationship between two variables Pie Chart –Emphasizing differences in proportion among a few numbers
© Anselm Spoerri Tufte - Graph & Chart Tips Avoid Separate Legends and Keys Make Grids, labeling, etc., Very Faint so that they recede into background Graphical Integrity –Where’s baseline? –What’s scale? –What’s context? –Watch Size Coding: Height/width vs. area vs. volume Using Color Effectively –To label –To measure –To represent or imitate reality –To enliven or decorate
© Anselm Spoerri Tufte – Hierarchy of Visual Effects
© Anselm Spoerri Tufte – Hierarchy of Visual Effects
© Anselm Spoerri Tufte – Hierarchy of Visual Effects in Maps
© Anselm Spoerri Tufte – Be aware of visual artifacts
© Anselm Spoerri Tufte – Leverage Illusionary Contours
© Anselm Spoerri Tufte – Narratives of Space & Time
© Anselm Spoerri Tufte – Micro / Macro Readings - 2½ Displays Axonometric Projection To Clarify, Add Detail
© Anselm Spoerri Tufte – Micro / Macro Readings - 2½ Displays
© Anselm Spoerri Tufte’s Principles – Summary Good Information Design = Clear Thinking Made Visible Greatest number of Ideas in Shortest Time with Least Ink in the Smallest Space Principles –Enforce Visual Comparisons Show Comparisons Adjacent in Space –Show Causality –Show Multivariate Data –Use Direct Labeling –Use Small Multiples –Avoid “Chart Junk”: Not needed extras to be cute
© Anselm Spoerri Lecture 3 Human Visual System – Recap Human Visual System –Gestalt Principles –Motion Perception –Space Perception –Lighting Model –Depth.
Making Graphs. The Basics … Graphical Displays Should: induce the viewer to think about the substance rather than about the methodology, graphic design,
© Anselm Spoerri Lecture 14 – Course Review Human Visual Perception How it relates to creating effective information visualizations Understand Key Design.
Recap Iterative and Combination of Data Visualization Unique Requirements of Project Avoid to take much Data Audience of Problem.
Graphical Display and Presentation of Quantitative Information 13 February 2006.
Space Perception Depth Cues Tasks Shape-from-Shading.
Information Visualization in Data Mining S.T. Balke Department of Chemical Engineering and Applied Chemistry University of Toronto.
Lecture 06: Design II February 5, 2013 COMP Visualization.
Perceiving and Representing Structured Information using Objects.
Information Design Trends Unit Three: Information Visualization Lecture 1: Escaping Flatland.
Data Presentation A guide to good graphics Bureau of Justice Statistics Marianne W. Zawitz.
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Space Perception: the towards- away direction Cost of Knowledge Depth Cues Tasks Navigation.
Space Perception: the towards- away direction The third dimension Depth Cues Tasks Navigation Cost of Knowledge Interaction.
CMPT 880/890 Writing labs. Outline Presenting quantitative data in visual form Tables, charts, maps, graphs, and diagrams Information visualization.
SIMS 247 Information Visualization and Presentation Prof. Marti Hearst August 31, 2000.
1 CSE 2337 Chapter 3 Data Visualization With Excel.
What to “know”? ◦ Goals of information visualization. ◦ About human perceptual capabilities. ◦ About the issues involved in designing visualization for.
1 i247: Information Visualization and Presentation Marti Hearst Data Types and Graph Types.
In Documents. Using Graphics to Think Preparing the graphics first helps you get started and sets out the framework of your written product.
Jeffrey Nichols Displaying Quantitative Information May 2, 2003 Slide 0 Displaying Quantitative Information An exploration of Edward R. Tufte’s The Visual.
Tufte’s Design Principles CS Information Visualization Sept. 5, 2000.
Scientific Communication and Technological Failure presentation for ILTM, July 9, 1998 Dan Little.
Presented to: By: Date: Federal Aviation Administration Effective Data Presentation Fernwood Avenue Middle School December 21, 2011 Ferne Friedman-Berg,
Data Visualization. Lies, Damn Lies, and Bad Graphs.
DATA OUTPUT maps tables. DATA OUTPUT output from GIS does not have to be a map many GIS are designed with poor map output capabilities types of output:
1 Eric Rasmusen, March 10, 2014 Graphs and Tables.
Graphics and visual information English 314 Technical communication Note: To hide or reveal these lecture notes, go to VIEW and click COMMENTS. This lecture.
© Anselm Spoerri Lecture 2 Information Visualization Intro – Recap Foundation in Human Visual Perception –Sensory vs. Cultural –Attention – Searchlight.
A valid measure?. Statistical graphs: The good, the bad and the ugly.
Infographic (informational graphic) Edward TufteEdward Tufte in The Visual Display of Quantitative Information defines 'graphical displays' in the following.
CONFIDENTIAL Data Visualization Katelina Boykova 15 October 2015.
Interface Design Tufteism. What is Tufteism? The study of the philosophies of Edward Tufte.
©2007 by the McGraw-Hill Companies, Inc. All rights reserved. 2/e PPTPPT.
Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 3.1 Chapter Three Art and Science of Graphical Presentations.
Principles of Good Presentation Slides & Graphics November 21, 2008 Adapted from slides used by Katie Kopren.
4 September 2003Robert Morris University1 Thinking visually: A workshop with some attending ideas Robert Joseph Skovira, Ph.D. Professor of Computer Information.
Charts and Graphs V Types of Charts and Their Uses Why create charts? 1. Present the data in a visual method 2. Prevent distorting data 3. Present.
Data Visualization. Napoleon Invasion of Russia, 1812 Napoleon.
GNET INTRODUCTION TO CONTENT. GNET INTRODUCTION.
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Mark P. Baldwin Northwest Research Associates, USA Cargese UTLS Summer School, 6 Oct Data Graphics AndTypography.
1 Effective Communication Through Graphs: The do's and don'ts. Juan Paulo Ramírez PPC Brown Bag Meeting 29 November 2007.
CPSC 533C Static and Moving Patterns Presented by Ken Deeter Slides borrowed from Colin Ware’s PPT Slides.
© Anselm Spoerri Lecture 14 Annotated Nike Ad Course Review –Course Objectives.
start with… PURPOSE OF DATA DISPLAYS. ALL OF THEM. The reason for these displays—rather then just putting numbers in your paragraphs—is to help your readers.
In Documents. Using Graphics to Think Preparing the graphics first helps you accomplish two tasks: –get started –provide a visual framework for your written.
Visualization and Data Mining. 2 Outline Graphical excellence and lie factor Representing data in 1,2, and 3-D Representing data in 4+ dimensions.
Sheelagh Carpendale Design World Graphical Integrity largely from Edward Tufte, The Visual Display of Quantitative Information, Graphics Press, 1983.
William H. Bowers – Designing Look and Feel Cooper 19.
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