Visualizing with Text

Visualizing with Text uncovers the rich palette of text elements usable in visualizations from simple labels through to documents. Using a multidisciplinary research effort spanning across fields including visualization, typography, and cartography, it builds a solid foundation for the design space of text in visualization. The book illustrates many new kinds of visualizations, including microtext lines, skim formatting, and typographic sets that solve some of the shortcomings of well-known visualization techniques. Key features: More than 240 illustrations to aid inspiration of new visualizations Eight new approaches to data visualization leveraging text Quick reference guide for visualization with text Builds a solid foundation extending current visualization theory Bridges between visualization, typography, text analytics, and natural language processing The author website, including teaching exercisesand interactive demos and code, can be found here. Designers, developers, and academics can use this book as a reference and inspiration for new approaches to visualization in any application that uses text.

Contents List of Figures and Credits , xvii Foreword, xxiii Preface, xxvii About the Author, xxix Part I Defining Text Elements Chapter 1 ? Why Visualize with Text? 3 1.1 WHY TEXT? 3 1.2 500 YEARS OF PUSHING TEXT OUT OF VISUALIZATIONS 4 1.3 (RE)LEARNING FROM HISTORY 10 1.3.1 Cartography 10 1.3.2 Typography 11 1.3.3 Tables 13 1.3.4 Science Classification and Notation 14 1.3.5 Code Editors 18 1.3.6 Alphanumeric Charts 19 1.3.7 Art and Poetry 20 1.3.8 Graphic Design and Advertising 20 1.3.9 Comics 22 1.3.10 Post-Modern Text 23 1.3.11 Data Visualization 24 1.4 FURTHER READING 26 Chapter 2 ? The Design Space of Visualization with Text 27 2.1 IS TEXT VISUALIZATION? 27 2.1.1 Visualization as Visual Patterns 28 2.1.2 Visualization as Organized Inventory 30 2.1.3 Visualization as Communication 31 2.2 VISUALIZATION DESIGN SPACE TODAY 31 2.2.1 Visualization Anatomy 31 2.2.2 Visualization Encoding 31 2.3 PREPROCESSING TEXT FOR THE VISUALIZATION PIPELINE 36 2.4 DERIVING A VISUALIZATION PIPELINE FOR TEXT 37 2.5 FURTHER READING 40 Chapter 3 ? Characterizing Text 43 3.1 LITERAL DATA 43 3.1.1 Functional Benefits: The Data Contains Text 44 3.1.2 Perceptual Benefits: Fast, Efficient Access to Detail 44 3.1.3 Cognitive Benefits: Reasoning Aid 47 3.1.4 Language Constraints 48 3.2 TYPOGRAPHIC ATTRIBUTES 49 3.2.1 Alphanumeric Glyphs (i.e. Letters and Numbers) 50 3.2.2 Symbols and Paired Delimiters 51 3.2.3 Weight (and Bold) 52 3.2.4 Oblique Angle (and Italic) 53 3.2.5 Underlines 54 3.2.6 Case (Upper, Lower, Small Caps, and Proper) 55 3.2.7 Width (Condensed/Expanded, Scaling, and Spacing) 56 3.2.8 Typeface (i.e. Font) 57 3.2.9 Low-Level Font Parameters: X-Height, Contrast, Stress, Serif Types, etc. 59 3.2.10 Shifting Baseline and Text on a Path 61 3.3 NON-TYPE VISUAL ATTRIBUTES 62 3.3.1 Size 63 3.3.2 Rotation 64 3.3.3 Fill Color 64 3.3.4 Outline and Outline Color 64 3.3.5 Gradients or Drop-Shadows 65 3.3.6 Superimposition and Contrast 66 3.3.7 Distortion and Extrusion 66 3.3.8 3D Orientation 66 3.3.9 Motion 67 3.3.10 More: Texture, Blur, Transparency, Etc. 67 3.4 MARKS AND TEXT SCOPE 67 3.4.1 Point Marks: Characters, Codes, Syllables, and Words 68 3.4.2 Line Marks: Phrases and Sentences 69 3.4.3 Area Marks: Paragraphs and Chapters 69 3.4.4 Readability of Text 71 3.5 TEXT LAYOUTS: PROSE, TABLES, AND LISTS 71 3.5.1 Prose 71 3.5.2 Tables 72 3.5.3 Lists and Indices 73 3.6 TEXT INTERACTIONS 74 3.7 TEXT CHARACTERIZATION FOR VISUALIZATION DESIGN SUMMARY 76 3.8 FURTHER READING 77 Chapter 4 ? Using the Design Space 79 4.1 STRUCTURED DATA AND BERTIN’S PERMUTATIONS 80 4.2 UNSTRUCTURED DATA ANALYSIS AND NLP 82 4.3 MULTIPLE ATTRIBUTES 85 4.4 ROLES FOR TEXT IN VISUALIZATIONS 86 4.5 VISUALIZATION BUSINESS OPPORTUNITIES 90 4.6 FURTHER READING 93 Part II Labels Chapter 5 ? Point Labels 97 5.1 LABELS AS POINT MARKS 97 5.2 READING IS FASTER THAN INTERACTING 97 5.3 CODES AS LABELS 99 5.4 FULL LABELS 102 5.5 GROUP LABELS AND VERY LONG LABELS 104 5.6 MANY LABELS AND LONG LABELS 106 5.7 MASSIVE DATA, LABELS, AND ZOOM 108 5.8 FURTHER READING 110 Chapter 6 ? Distributions 111 6.1 HIGHLIGHTING VALUES IN STEM AND LEAF PLOTS 112 6.2 LITERAL LEAVES 113 6.2.1 Literal Leaves Showing Alphanumeric Codes 113 6.2.2 Literal Leaves Showing Words and Phrases 114 6.3 LITERAL STEMS AND LITERAL LEAVES 116 6.3.1 Literal Stems and Leaves with Codes 116 6.3.2 Literal Stems and Leaves with Words 118 6.3.3 Literal Stems and Leaves with Phrases 120 6.4 STEMS AND LEAF HIERARCHIES AND GRAPHS 121 6.4.1 Simple Stems and Leaf Hierarchy 121 6.4.2 Stems and Leaf Graph 122 6.4.3 Stems and Leaf Hierarchies on a Corpus 124 6.5 STEMS AND LEAF INTERACTIONS 125 6.6 FURTHER READING 130 Chapter 7 ? Microtext Lines 131 7.1 TEXT ON PATHS 131 7.2 THE NEED TO VISUALIZE MANY TIMESERIES 132 7.2.1 Line Charts with Many Lines 135 7.2.2 Microtext and River Labels with Many Lines 138 7.2.3 Do Microtext Lines Work? 140 7.2.4 Interactive Microtext Line Charts 141 7.3 MICROTEXT APPLIED TO OTHER VISUALIZATION LAYOUTS 143 7.4 FURTHER READING 144 Part III Formats Chapter 8 ? Sets and Categories 149 8.1 CHALLENGES VISUALIZING MULTIPLE CATEGORIES 149 8.2 INDICATING SET MEMBERSHIP WITH TEXT 151 8.3 TYPOGRAPHIC VENN AND EULER DIAGRAMS 153 8.4 TYPOGRAPHIC GRAPHS 156 8.5 TYPOGRAPHIC SCATTERPLOTS 161 8.6 TYPOGRAPHIC MOSAIC PLOTS 162 8.7 TYPOGRAPHIC BAR CHARTS WITH STACKED LABELS 165 8.8 HANDLING MANY CATEGORIES 168 8.8.1 Many Different Visual Attributes 168 8.8.2 Visual Attributes Applied to Individual Characters 171 8.8.3 Decoding vs. Noticing a Difference 171 8.8.4 Going Further 172 8.9 FURTHER READING 172 Chapter 9 ? Maps and Ordered Data 175 9.1 PROBLEMS WITH THEMATIC MAPS 176 9.2 TYPOGRAPHIC THEMATIC MAP WITH A SINGLE ORDERED VARIABLE 177 9.3 MULTI-VARIATE TYPOGRAPHIC THEMATIC MAPS 179 9.4 HANDLING LONG LABELS 180 9.5 SCALING TO THOUSANDS OF LABELS 180 9.6 NON-DISTORTED TYPOGRAPHIC MAPS 181 9.7 TYPOGRAPHIC SCOPE: PARAGRAPHS AND GLYPHS 181 9.8 DO TYPOGRAPHIC THEMATIC MAPS WORK? 184 9.9 TYPOGRAPHIC ORDERING WITH OTHER ATTRIBUTES AND LAYOUTS 186 9.10 FURTHER READING 187 Chapter 10 ? Ratios and Quantitative Data 189 10.1 QUANTITATIVE DATA 189 10.2 PROPORTIONS ALONG A STRING (BAR CHARTS WITH LONG LABELS) 190 10.2.1 Proportions along Words and Phrases 190 10.2.2 Proportions along Lines of Text 191 10.2.3 Proportions to Indicate Ranges 191 10.2.4 Proportions, Distributions, and Areas 192 10.2.5 Proportions in Paragraphs 196 10.2.6 Stacked Proportions 200 10.2.7 Multiple Proportions 200 10.2.8 Semantic Proportions and Expressive Text 203 10.3 POSITIONS ALONG A STRING 204 10.4 CAVEATS, ISSUES, AND LIMITATIONS 205 Part IV Text Layouts Chapter 11 ? Prose and Prosody 211 11.1 ENHANCED READING 211 11.2 SKIM FORMATTING 212 11.3 FORMATTING LETTERS FOR PRONUNCIATION, SPELLING, AND PROSODY 218 11.4 FURTHER READING 220 Chapter 12 ? SparkWords 221 12.1 HISTORIC PRECEDENT FOR SPARKWORDS 221 12.2 SPARKWORDS DEFINED 222 12.3 SPARKWORDS IN NARRATIVE 222 12.3.1 Categoric SparkWords 222 12.3.2 Ordered SparkWords 223 12.3.3 Quantitative SparkWords 228 12.4 SPARKWORDS IN LISTS 230 12.5 SPARKWORDS IN TABLES 230 12.5.1 Orders of Magnitude 230 12.5.2 Tables with Data Added into Typographic Formats 233 12.6 FURTHER READING 237 Chapter 13 ? Opportunity and Checklist 239 13.1 VALIDATION 242 13.2 CHECKLIST 242 13.2.1 Language 242 13.2.2 Legibility 243 13.2.3 Alphanumeric Codes 244 13.2.4 Formats 244 13.2.5 Long Labels 245 13.2.6 Layout Challenges 246 13.2.7 Typeface 246 13.2.8 Interactions 247 13.2.9 More 247 Chapter 14 ? References 249 14.1 ACKNOWLEDGMENTS 249 14.2 PEER-REVIEWED RESEARCH 249 BIBLIOGRAPHY 252 INDEX, 263

Richard Brath has been actively involved in the research, design, and development of data visualization and visual analytics since 1990. His research interests include exploration of the boundaries of visualization – such as this book regarding text and visualization – as well as graph visualization, automated insights, 3D, spreadsheets, aesthetics, and machine learning. From a commercial perspective, Richard focuses on the creation of unique, innovative visualizations that are in use by hundreds of thousands of users. Richard originally acquired a degree in architecture and worked in industrial design, special effects, and 3D animation. With the opportunity to solve business challenges with interactive computer graphics, Richard switched to visualization, creating one of the first interactive 3D financial visualizations on the web (1996). Richard is a partner at Uncharted Software, where his team creates a wide variety of visualizations, ranging from small mobile screens to multi-screen video walls. These visualizations are used in domains such as financial markets, professional sports, health care, journalism, and customer analytics. Richard has a personal blog at richardbrath.wordpress.com.