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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https://www.functionalaestheticsbook.com
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Seitenzahl: 388
Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Acknowledgments
About the Authors
About the Technical Editor
Foreword
Introduction
A Fiat, Food, and Finding Commonality
This Book at a Glance
Reader Support for This Book
PART A: Perception
CHAPTER 1: The Science Behind Perception
Seeing and Understanding Imagery
Color Cognition
Saccade and Directed Attention
The Notion of Space and Spatial Cognition
Diagramming the World
Summary
CHAPTER 2: Perception in Charts
Visualization and Task
Chart as an Informational Unit
Unboxing Functional Aesthetics in the Physical World
Recursive Proportions
The Digitized Space: Creating Experiences on the Screen
Summary
CHAPTER 3: Charts in Use
The First Charts
Standardizing Visualization
The Shifting Role of Data Visualization
Maturity within the Profession
Summary
PART B: Semantics
CHAPTER 4: Coming to Terms
Statistical Graphics Are Inherently Abstract
Flattening the Curve
Toward Meaningful Depictions
Situating with Semiotics
Summary
CHAPTER 5: Vagueness and Ambiguity
How Tall Is Tall?
Spicy or Hot—What's the Difference?
Clarification, Repair, and Refinement
Iconicity of Representation
The Art of Chart
Summary
CHAPTER 6: Data Literacy
Navigating Data Literacy
The Impact of Writing
Data Orality
Changing Exposition Styles
Data Literacy Democratization
Summary
CHAPTER 7: Data Preparation
Hairy Dates
Common Transformations
Clarity in Conversation
Shaping for Intent
Prepping for the Future
Data Enrichment
Summary
CHAPTER 8: Scaling It Down
Generalization
Natural Sizes
Fat Fingers and Small Screens
Color as a Function of Size
Thumbnails and Visual Summaries
Summary
CHAPTER 9: Cohesive Data Messages
Cohesion in Designing Visualizations
Analytical Conversation
Summary
CHAPTER 10: Text and Charts
Medium Being the Message
Types of Text
Balancing Text with Charts
Chart and Text Agreement
Text in Analytical Conversation
Making Data More Accessible
Text for Supporting Reading Fluency
Summary
PART C: Intent
CHAPTER 11: Defining and Framing
Analytical Intent
Register
Repair and Refinement
Pragmatics
Practicing Intent
Summary
CHAPTER 12: Visual Communication
Do What I Mean, Not What I Say
Register in Charts
Registers in Composition
Mood and Metaphor
Beyond Language Communication
Expansion and Contraction
Summary
CHAPTER 13: Scaffolds
Visualization Scaffolding
Scaffolding Data Discovery
Scaffolding Natural Language Recommendations
Analytical Conversation to Repair and Refine
Summary
CHAPTER 14: Balancing Emphasis
Individual Choices
Collective Choices
Correcting Common Problems
View Snapping
Summary
CHAPTER 15: Mode
Navigate Like a Local
Revisiting Analytical Chatbots
Video Killed the Radio Star
Beyond the Desktop
Future Forward
Summary
PART D: Putting It All Together
CHAPTER 16: Bringing Everything Together
Addressing the Paper Towel Problem
Crafting Recipes for Functional Aesthetics
Summary
CHAPTER 17: Close
Data in Everything and Everywhere
New Tools and New Experiences
Sign-off
Technical Glossary
Index
Copyright
Dedication
End User License Agreement
List of Tables
Chapter 5
TABLE 5.1 A sample data table. Where in the world is Paris?
TABLE 5.2 Number of books purchased over the first four months
Chapter 7
TABLE 7.1 Variations in date formats
TABLE 7.2 Sample housing dataset
TABLE 7.3 Associating the Price attribute for defining the concept “expensiv...
TABLE 7.4 Associating the area attribute for defining the concept
large
Chapter 16
TABLE 16.1 Maturity matrix
List of Illustrations
Introduction
FIGURE 0.1 A view into a restaurant space
FIGURE 0.2 Arrangement of food in a bento box
Chapter 1
FIGURE 1.1
Impression, Sunrise
by Claude Monet
FIGURE 1.2
Impression, Sunrise
converted to black-and-white in GIMP. The sun...
FIGURE 1.3 Colors and the Stroop effect
FIGURE 1.4 A semantically resonant color palette of ice cream flavors
FIGURE 1.5 Illustrating the effect of contrast on legibility
FIGURE 1.6 Closure
FIGURE 1.7 Common region
FIGURE 1.8 Figure and ground in Rubin's vase
FIGURE 1.9 Proximity (Emergence)
FIGURE 1.10 Similarity
FIGURE 1.11 Pandemic arrow markers and signage at ZombieRunner Coffee, a cof...
FIGURE 1.12 Physics leverage explained by mass and distance equation
FIGURE 1.13 The first design for the French press
FIGURE 1.14 T and O map by Isidore of Seville
FIGURE 1.15 Exaggeration
FIGURE 1.16 Simplification
FIGURE 1.17 Aggregation
FIGURE 1.18 Typification
FIGURE 1.19 Merge
FIGURE 1.20 Label prominence
Chapter 2
FIGURE 2.1 Hand-drawn route map
FIGURE 2.2 Examples of line charts for various task targets and actions
FIGURE 2.3 Ten elementary perceptual tasks and their efficacy for making rel...
FIGURE 2.4 A sampling of charts used in the study of pie and donut chart enc...
FIGURE 2.5 Mantelpiece design based on recursive proportions
FIGURE 2.6 George Walker's workshop
FIGURE 2.7 An example frame for charts
FIGURE 2.8 An alternative frame for charts
FIGURE 2.9 A dashboard showing containers and proportions framing the variou...
Chapter 3
FIGURE 3.1 Ishango bone
FIGURE 3.2 Marshall Islands stick chart photographed by Jim Heaphy
FIGURE 3.3 Re-creation of Ibn Sahl algebra graphics
FIGURE 3.4 William Playfair's time series chart
FIGURE 3.5 Cholera map and coxcomb charts
FIGURE 3.6a Composite of a few Du Bois charts
FIGURE 3.6b Composite of a few Du Bois charts
FIGURE 3.7 Showing collective sensemaking around case data
FIGURE 3.8 Timelines and publish dates
FIGURE 3.9 Sample pictorial-style dashboard
FIGURE 3.10 The perceptual dashboard
FIGURE 3.11 Adapted from Baker-Shenk and Cokely
FIGURE 3.12 Functional aesthetics paradigm at a glance
Chapter 4
FIGURE 4.1 Popular symbols
FIGURE 4.2 Carl Sagan's scatterplot
FIGURE 4.3 Carl Sagan's plot with icons
FIGURE 4.4 The original chart (left) and the derivative showing an iconic vi...
FIGURE 4.5 Chinese characters for
root
,
tree
,
woods
, and
forest
(left to rig...
FIGURE 4.6 Blissymbols
FIGURE 4.7 Profit by Category showing proportional brushing in bar charts wi...
FIGURE 4.8 Bird Strikes by Kelly Martin
Chapter 5
FIGURE 5.1 Vague concepts can be interpreted along a spectrum from concrete ...
FIGURE 5.2 The stimuli shapes used in understanding how to represent vague c...
FIGURE 5.3 Eviza's interface shows a map of earthquake data in the United St...
FIGURE 5.4 An Indian thali of dishes that can be both “spicy hot” and “tempe...
FIGURE 5.5 Transforming ambiguous birthday strings into precise date types
FIGURE 5.6 ASL signs drawn by Deaf graphic artist Brittany Castle
FIGURE 5.7 Home and factory weaving in England during the industrial revolut...
FIGURE 5.8 A graphic similar to the ISOTYPE charts
FIGURE 5.9 An iconic representation of stacked books and a bar chart
FIGURE 5.10 An area chart and line graph showing book purchasing trends
FIGURE 5.11 Breaking the illusion
Chapter 6
FIGURE 6.1 Data literacy circle
FIGURE 6.2
Hangul
written to highlight letter and syllable features
FIGURE 6.3 Featural elements
FIGURE 6.4 Additive design
FIGURE 6.5 Selected letters (base, aspirated, tense)
FIGURE 6.6 Korean vowel harmony
FIGURE 6.7 Orality exposition style
FIGURE 6.8 Burtin's antibiotic data
FIGURE 6.9 Dashboard with residues of data orality
FIGURE 6.10 Literate culture exposition
FIGURE 6.11 Graphicacy as a third-tier skill
Chapter 7
FIGURE 7.1 Validating uniqueness to understand shape
FIGURE 7.2 Filtering data
FIGURE 7.3 Joining
FIGURE 7.4 Parsing
FIGURE 7.5 Partial view of ScriptSource data
FIGURE 7.6 Abstract reshaping map
FIGURE 7.7 Abstracted data shaping diagram
FIGURE 7.8 Simplified data prep flow
FIGURE 7.9 A relational model
FIGURE 7.10 Semantic joining of country name and country code columns from t...
FIGURE 7.11 Automated sorting for Likert scales
FIGURE 7.12 Column splitting
Chapter 8
FIGURE 8.1 Line chart generalized to different display sizes
FIGURE 8.2 Retargeting dashboards to different mobile display sizes
FIGURE 8.3 Colors that are distinct in bars are more difficult to distinguis...
FIGURE 8.4 Left: Original. Right: Enhanced web thumbnail.
FIGURE 8.5 Bottom: Original web pages. Top: Corresponding visual snippets.
FIGURE 8.6 Semanticons for the file
Japanese_VGA_Driver.exe
FIGURE 8.7 Left: Original dashboard. Center: Thumbnail without semantic resi...
FIGURE 8.8 Small multiples of brains showing highlighted activity regions fo...
Chapter 9
FIGURE 9.1 Sample comic drawn by author
FIGURE 9.2 Identifying where color clarifies
FIGURE 9.3 Semantic color use in Kelly Martin's visualization
FIGURE 9.4 Semantic color use around metrics
FIGURE 9.5 A non-cohesive sequence
FIGURE 9.6 A cohesive sequence
FIGURE 9.7 Simplified style tile
FIGURE 9.8 Frames differentiated by design elements
FIGURE 9.9 A comic using negative space to make a point
FIGURE 9.10 Kelly Martin's viz modified to draw emphasis to use of space
FIGURE 9.11 Effects of chart design on negative space
FIGURE 9.12 Kelly Martin's visual poetry
FIGURE 9.13 Coral highlighting the alignment relationships
FIGURE 9.14 Conversational transitions model
FIGURE 9.15 Example results of various forms of natural language interaction...
Chapter 10
FIGURE 10.1 Bird Strikes dashboard with text and numbers highlighted
FIGURE 10.2 Different types of text highlighted and boxed
FIGURE 10.3 Boxed text shows titles.
FIGURE 10.4 Quantifiers are highlighted in sea green.
FIGURE 10.5 Qualifiers are highlighted in peach.
FIGURE 10.6 Boxed text shows annotations.
FIGURE 10.7 Framing text is highlighted in grey.
FIGURE 10.8 Functional text is highlighted in Lilac.
FIGURE 10.9 Text is not balanced with the visual.
FIGURE 10.10 Text is balanced with the visual.
FIGURE 10.11 Line chart example
FIGURE 10.12 The 30-year fixed mortgage rate reached its peak of 18% in 1981...
FIGURE 10.13 Text used to annotate the spike in the orange line
FIGURE 10.14 The Sentifiers system shows interactive text as a response.
FIGURE 10.15 Narrative Science NLG plug-in for Tableau
Chapter 11
FIGURE 11.1 Functional aesthetics—completing the cycle with intent supportin...
FIGURE 11.2 A histogram addressing the user's analytical intent for seeing t...
FIGURE 11.3 A map shown in response to the user's query, “show me the life e...
FIGURE 11.4 Evizeon uses pragmatics to support a follow-up to remove condos ...
FIGURE 11.5 Assessing a visualization for intent
FIGURE 11.6 After evaluating intent
Chapter 12
FIGURE 12.1 COVID clutter: the signs have signs.
FIGURE 12.2 Revisiting COVID arrows and signage at ZombieRunner Coffee
FIGURE 12.3 Analytical conversation example: The initial chart response
FIGURE 12.4 Analytical conversation: Expanding yet preserving original chart...
FIGURE 12.5 Analytical conversation: Creating a new visually cohesive visual...
FIGURE 12.6 Adjusting the register of a chart
FIGURE 12.7 “Wealth and Health Inequality” re-creation by Lilach Manheim Lau...
FIGURE 12.8 “Wealth and Health Inequality Deluxed” by Lilach Manheim Laurio...
FIGURE 12.9 “Wealth and Health Inequality” by Lilach Manheim Laurio with det...
FIGURE 12.10 Trans-Siberian Orchestra visualization showcasing visual metaph...
FIGURE 12.11 Deictic referencing and widget interaction in a dashboard
Chapter 13
FIGURE 13.1 Instructions to help make a paper airplane
FIGURE 13.2 Visualization without scaffolds to direct reading and interactio...
FIGURE 13.3 Dashboard using visual scaffolding techniques
FIGURE 13.4 Sneak Pique showing map and calendar autocompletion widgets
FIGURE 13.5 Sneak Pique showing a pair of date and numerical range widgets...
FIGURE 13.6 Example of utterance recommendations in Snowy
FIGURE 13.7 Snowy suggests follow-up utterances based on the current chart i...
FIGURE 13.8 Snowy recommends deictic utterances when a user selects marks in...
FIGURE 13.9 Scaffolds in cha(t)bot, a Slack interface
Chapter 14
FIGURE 14.1 Chart selection affects emphasis.
FIGURE 14.2 Emphasis only on the zero line (a) and additional emphasis on ne...
FIGURE 14.3 A starting dashboard template with minimal emphasis
FIGURE 14.4 A small change with color alters emphasis.
FIGURE 14.5 Overemphasis on color without using other parameters
FIGURE 14.6 Corrected emphasis by altering parameters and chart selection
FIGURE 14.7 Emphasis created by dual encoding of charts
FIGURE 14.8 Iterations on strategic emphasis
FIGURE 14.9 Dashboard without view snapping
FIGURE 14.10 Dashboard with view snapping on the Profit Ratio axis
FIGURE 14.11 Kelly Martin's visual poetry as originally designed
FIGURE 14.12 Kelly Martin's visual poetry by use of space
FIGURE 14.13 Kelly Martin's visual poetry with alignment
Chapter 15
FIGURE 15.1 Digital menus introduce a new mode for food ordering.
FIGURE 15.2 A typical street scene in Old Delhi, India, where locals and tou...
FIGURE 15.3 Hand-drawn route map to the wedding destination (revisited)
FIGURE 15.4 Digital wayfinding on websites
FIGURE 15.5 Dashboard before wayfinding
FIGURE 15.6 Dashboard with both visual and natural language landmarking
FIGURE 15.7 Interaction with two landmarks enabled
FIGURE 15.8 Interaction transcript with an Alexa Echo analytical chatbot
FIGURE 15.9 Mobile-first designed lengthwise
FIGURE 15.10 Side-to-side scrolling
FIGURE 15.11 Mouse clicking vs. touching on points (Microsoft Surface)
Chapter 16
FIGURE 16.1 A lovely dessert to end a meal
FIGURE 16.2 Functional aesthetics paradigm
FIGURE 16.3 The perceptual dashboard started in Chapter 3
FIGURE 16.4 Mapping our evaluation
FIGURE 16.5 Donor dashboard with Named Sponsorships selected
FIGURE 16.6 Donor dashboard with Amount selected
Chapter 17
FIGURE 17.1 Revisiting the four elements that introduced each book part
FIGURE 17.2 Visualizing IoT data in a factory setting
FIGURE 17.3 Accessing smart appliance data from a mobile device
FIGURE 17.4 Dashboard showing wait times in mm:ss at a quick-serve restauran...
FIGURE 17.5 A dashboard showing the wait times and events inside the restaur...
FIGURE 17.6 Interacting with the dashboard shows the stats for the various t...
FIGURE 17.7 The GRaSP system showing interaction between mobile devices and ...
Guide
Cover
Title Page
Copyright
Dedication
Acknowledgments
About the Authors
About the Technical Editor
Foreword
Introduction
Table of Contents
Begin Reading
Technical Glossary
Index
End User License Agreement
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Functional Aestheticsfor data visualization
Vidya Setlur
Bridget Cogley
Acknowledgments
This book would not have come together if it were not for the support and encouragement of so many people. We both would like to thank Patrick McCormick, Brandi Beals, and Madison and Milwaukee Tableau User Groups (TUGs) for your impeccable timing and brilliance for putting us in the right place at the right time. Without all of you and a famous blue Fiat, this book would never have happened.
We are also grateful to so many individuals who have helped with this project: October 22, 2020, to be precise, is when our book journey started after Jim Minatel, the associate publisher at Wiley, responded to our book proposal. We thank him for believing in us and being a good partner during the book-writing process. We'd like to thank our project manager, Brad Jones, for patiently doing all the content wrangling, keeping us honest with deadlines, and gently hustling us with his positivity. Also, thanks to Pete Gaughan, Barath Kumar Rajasekaran, and Melissa Burlock for helping with operations to move the content along to finish. Jock Mackinlay, our technical editor, has been an awesome sounding board, applying his vast experience in both academia and industry to enrich so many parts of this book. We thank Pat Hanrahan for graciously writing the foreword and inspiring us to think of how semantics with visual representation can help make sense of the world.
Vidya would like to thank her mother, Jayashree Raghavan, for basically taking over the kitchen during the writing process, texting her lunch and dinner menus so that she could emerge from her she-cave to eat. Her father-in-law, Rangaswamy Setlur, has been her personal cheerleader all these years, reading the manuscripts and providing kind notes of encouragement. She owes a great amount of gratitude to all her research colleagues for their wisdom and intellect and to her friends and neighbors, who have generously shared their joy and pride during the writing process. Finally, the long days just felt a little sweeter with Milo, her pandemic pup, with his playfulness and love.
Bridget would like to thank Mike Cogley for bringing tea and driving the Fiat where this all started and Dominic Cogley for the laughs and inventions while writing this book. The practitioner sections are stronger due to the extra eyes from Allen Hillery, Michelle Frayman, and Sarah Pallett. Bridget would be remiss if she didn't thank her mother, Sheree McKay, for teaching her to have a keen eye and to put things in the right place. Friends and family lent their support, even if it made no sense to them. The patience is much appreciated. Colleagues past and present made this process easier, providing much-needed motivation and encouragement. No book acknowledgment is complete without celebrating the cats that keep the laughs and fuzz coming: Fluffles, Minx, and Jaxx.
And we thank you, the reader, for giving these ideas a space to have this conversation.
About the Authors
Vidya Setlur is the Head of Research at Tableau Software. Prior to joining Tableau, she worked as a principal research scientist at the Nokia Research Center for seven years. She earned her doctorate in 2005 at Northwestern University. Her area of expertise is in natural language processing and computer graphics. The goal of her work is to develop new computer algorithms and user interfaces that enhance visual communication and understanding of the semantics of the underlying data. Since joining Tableau Research in 2012, Vidya has worked on various projects and prototypes ranging from semantic icon encodings to chatbot interfaces. She has also explored analytical conversation using principles of language pragmatics that led to Tableau's first natural language feature, Ask Data.
Bridget Cogley is the Chief Visualization Officer at Versalytix and long-time consultant in the data visualization space. As an interpreter turned analyst, she brings an interdisciplinary approach to data analytics. Her dynamic, engaging presentation style is paired with thought-provoking content, including ethics and data visualization linguistics. She has a deep interest in the nuances of communication, having been an American Sign Language interpreter for nine years. She is a Hall of Fame Tableau Visionary. Her work incorporates human-centric dashboard design, an anthropological take on design, ethics, and language. She extensively covers speech analytics and open text. Prior to consulting, Bridget managed an analytics department.
About the Technical Editor
Jock D. Mackinlay is the first Technical Fellow at Tableau Software, an expert in visual analytics and human-computer interaction. He believes that well-designed software can help a wide range of individuals and organizations to work effectively with data, which will improve the world. Jock joined Tableau in 2004 after being on the PhD dissertation committee of Chris Stolte, one of the cofounders of Tableau. Jock got a computer science PhD at Stanford University in 1986 and joined the research team at Xerox PARC that coined the term, “Information Visualization.” In 2009, Jock received the IEEE Visualization Technical Achievement Award for his seminal technical work on automatic presentation tools and new visual metaphors for information visualization.
Foreword
Data is everywhere. Manipulating data is now considered an essential computer skill, along with searching for information, document editing, and slide making. Spreadsheets are no longer just number-crunching tools; by reading data into a tabular grid, they are now the gateway toward more sophisticated data analysis and graphing. Analyzing and graphing data is taught in high school along with experimental science and analytical reasoning. Solving almost any problem involves data. It is essential to understand your data, to be able to reason about it, and to draw out findings and conclusions. Data-based decision making is becoming a core doctrine of modern businesses and organizations.
How do people interact with data? The answer is obvious. They must be able to see it, as a list, table, map, time series, scatterplot—a visualization. The power of visualization leads naturally to the question of how to present data to people so they can best understand it. This book provides answers to this essential question.
The answers start with how to graph data. Obvious questions are how best to discern different magnitudes or compare values. Is a bar graph better than a pie chart? The next level is how to distinguish categories using color and shape. And how to perceive trends and other patterns. Even basic graphics can benefit from good graphic design. The techniques of graphical design show how to emphasize the most important information and deemphasize the less important. The answers to these questions require that we understand visual perception.
The more interesting questions arise when we think of visualizations as a form of communication. In this view, visualizations are a type of language—a visual language. Now new questions arise. How do we assign meaning to what we see? What do icons and colors mean, what do they represent and symbolize? These types of questions give us a deeper understanding of how visualizations work. A visualization is no longer a simple graph of data. A visualization contains a message; it tries to communicate information to us. We can use it as a form of rhetoric to spur people to action. At the next level, how do we interact with our data using visualizations? How can they be used to help us think? How can we have a dialog with our data?
This book by Vidya and Bridget takes you on this journey. It provides insight into how we see, interpret, understand, and reason about data. The book provides a summary of research findings and useful advice for the practitioner. It will enable you to use visualizations more effectively as you analyze data and communicate what you found to the people around you.
Pat Hanrahan
Introduction
You enter the restaurant, escaping quickly from the cold. The large glass windows minimize the distance between the brisk air outside and the warm air inside. The generous wooden beams lead you deeper into the hubbub of the restaurant, until you find your sweet spot, the highly coveted booth embedded in a tube similar to the one in Figure 0.1. The space is playful and inviting. The table is almost spartan, but the lighting, the slightly raised platform, and the delightful paradox between the openness of the window and the intimacy of the space draw you in. You and a few friends step eagerly into the booth, awaiting the dining experience you've read about.
FIGURE 0.1 A view into a restaurant space
Jon Tyson/Unsplash.com
You take in the aesthetics of the space as you wait for your server. The concrete around you forms a perfect circle. The wooden flooring supports your feet as you gently tap them in anticipation. The light above emits a warm glow, suspended from the top and allowing light to fill the whole chamber. It's surprisingly warm, the velveteen seat cushions softly supporting you. The architecture is open, but you're nestled comfortably away from the noise. You laugh, feeling both free and cozy.
The meal arrives, served in a beautifully arranged black-lacquered bento box similar to what is seen in Figure 0.2. One of the visceral pleasures of the lunch box is the arrangement. The intentionality of the food placement is juxtaposed with the sheer beauty of the delicacies. There is a state of delicate tension as each dish gets its space with a deliberate order that helps guide your culinary experience. The food is separated, no sauces mixing, and the wasabi and ginger are placed where they can easily be pinched with chopsticks and added to the sushi. The main dish occupies the largest container. A supporting dish of cooked vegetables sits with the pickled condiment. Salad and sides are placed in smaller containers acting as cheerful companions to the protagonists of the meal. The black framing neatly highlights the shape of each part and provides an implicit guideline for order and hierarchy. The meal is a delight, with each varied piece harmoniously playing an essential part that is functional yet aesthetic.
FIGURE 0.2 Arrangement of food in a bento box
These are the experiences we hope to provide our users, one that balances form with function and delight with efficiency. It's leveraging beauty to provide both a form and function that support an ideal. While crafting charts and dashboards to help people see and understand data, we often spend time trying to find the right balance between a functional design and an aesthetically pleasing design that breaks through the clutter and noise. Functionality is often thought to be obvious; graphics have a functional purpose, which is to make it easy for the reader to understand the patterns in the data. The aesthetic component is less obvious. How do we make charts visually delightful in a way that takes the process of visual analysis to a whole new level of joy? The aesthetics are seen instantly. The charts indicate that the visualization author is skilled. They hint that the design is also functional. The joy of the aesthetics also compensates for the daunting aspects of data work. This visceral feeling is something that we can actually relate to in our own lives through the sights, sounds, and smells of the world as we experience it. The challenge, of course, is that aesthetic design is hard to achieve, hence this book.
Imagine in our restaurant scenario, and you head to the restroom just before leaving. After washing your hands, you go to grab a paper towel. The paper towel dispenser is one of those automatic ones and you wave your hands to no avail. Finally, it spits out the tiniest bit of paper. It's not even sufficient to grab, and so you dry off your hands on your clothing.
The paper towel problem is common. It comes wrapped with the best of intentions, from reducing waste to eliminating the need to touch anything, yet it falls short. The intents compete—we want people to use fewer paper towels, but they still need to be accessible. Too often, inaccessibility wins and dysfunctional behavior ensues. We end up using more, not less, or none at all.
This experience happens with data visualization as well. We create charts and our users flail when the intentions of the chart and that of the user do not align. They struggle to navigate our carefully laid dashboards, tidy explanations, and lovely frames. In short, we've done everything right, but it's still landing wrong. Just as with paper towels, our charts can fall short in ways we don't anticipate. They include hidden barriers—often chosen more to preserve an aesthetic or meet a certain goal than to provide a function—making it far too easy to do or interpret the wrong thing.
This is not a design book about charts.
Rather, it's a book focused on how the intentionality of the data in the chart can be expressed through thoughtful design sensibilities. The book explores the interplay between what we see (perception), what information is encoded (semantics), and what we mean (intent). Combined, this triad creates a powerful subsystem in the production of data-driven graphics. We call this concept functional aesthetics as a way of using beauty and form to guide and support function. Throughout this book, we'll highlight that charts are more than just visual artifacts; they come embedded with hidden linguistic components and interactivity that we can use to more clearly articulate our intent.
If you've picked up this book, we're assuming that beyond choosing effective charts, you're looking for ways to improve how your visualizations communicate. This could be presented as a traditional dashboard or as a data-driven app. It might be as an infographic or through data journalism. You might also be developing the next tool to help facilitate the making of data-driven graphics, as either a researcher or developer. Wherever you are using data to create an experience, we hope this book will help you move beyond literal chart making to semantically resonant data-centric creations. Legends, text, spacing, and so many hidden elements dictate how your work is read. This book is designed around that larger experience, so we're assuming you have a level of comfort with chart selection and statistically accurate data representation.
In this book, we'll center on the idea of data-driven experiences and how we can use functional aesthetics to both transport and cue our readers to the actions and insights we hope they'll take. We'll incorporate delight as a reasonable benchmark. Elegant design vanishes, becoming a backdrop in our memories and leaving us with a frictionless experience. We recognize it in physical spaces with a near-reverence for the place, as we saw with the restaurant example. Viscerally, we feel it, such as when we find a kitchen item stored just exactly where we'd put it, or a chair nestled in the perfect place to sit and enjoy the sunlight. Experiences delight and transcend the sum of the parts.
This book is also a conversation, one held between a researcher and a practitioner. We hope to challenge our own assumptions and to explore this subsystem deeper. Vidya Setlur, a researcher, has spent years investigating and integrating linguistics into her work, including Ask Data, a search engine within Tableau designed to display charts in semantically resonant ways. Bridget Cogley, a practitioner, has spent well over a decade working in data analysis and visualization. In addition to consulting experience centered on visualizations that captivate, she draws from her background interpreting American Sign Language to incorporate semantics in visualization. Our backgrounds form two sides of a coin: what the research says about our ideas and how they can reasonably be incorporated into practice for better results.
A Fiat, Food, and Finding Commonality
Beyond Binaries: A Conversation
It all started in a Fiat, headed to Milwaukee to attend a local TUG event.
Vidya sat tucked in the backseat, suitcase nearby. Bridget sat in the front passenger seat while her husband drove. Prior to this trip, Vidya and Bridget had spoken only briefly. They had finished delivering their presentations in Madison the day before and were preparing to repeat them again in Milwaukee. Vidya presented on color theory and semantics, while Bridget discussed the logic of dashboards. Together, these sessions represented two pieces of a puzzle—how semantics and visual aesthetics together help people see and understand their data.
One hundred twenty miles and a lunch later, a friendship started, and so did this book.
As we have this conversation, both with you and ourselves, we also want to expose where ideas in this book originate. Are they researched with specific points proven, a theory that's perhaps still evolving, or perhaps a common practice that stems from practitioner experience or trend-following? Throughout the book, we will use the following visuals to highlight where these ideas reside.
Some ideas tie back to a theory that we have or that's shared in academic or practitioner circles. For ideas solidly in the theory realm, we'll place the circle in the left corner.
Other ideas are common in practice. It could be anecdotal—we find users at scale reject a specific chart or end up editing our work to specific charts quite frequently. Sometimes, charts in the real world inspire ours as a form of “data fashion.”
Research centers on answering specific questions, such as how users perceive certain graphics or interpret them. Many of these ideas originate from hypotheses, inquiry, and prototypes.
In this example, a particular idea is mostly a theory, with some use in practitioner circles. You will notice we are using location, and that the dot can land in a variety of spaces.
Through this visualization, we hope to make easier to understand what's explicitly researched, what's sometimes extrapolated from that research, and what's potentially thought but not fully proven scientifically. Here's how you'll see the triangles used in the book:
This book is a continuation of that conversation started in the car, a dialog between a researcher and a practitioner. It is a means to examine all the knowledge we have across a number of disciplines and find ways to integrate them holistically into a system of practice. Throughout this book, we will discuss research, current working theories, and how these concepts can be implemented in practice.
This Book at a Glance
This book isn't designed to be an introduction to data visualization. We assume that you have read various introductory books, have a plethora of chart choosers, and are looking to move to that next level in data visualization. Maybe you're struggling with endless feedback cycles or seeking novel ideas in how to approach unifying charts into a cohesive unit. You might be an analyst, a researcher, or a professional in a dedicated field comfortable with visualizing statistics. Maybe you're in marketing and looking to shift the paradigm in how infographics are designed. Beyond charts, we hope this book challenges you to look at data in a new light, a view where charts construct part of a data message and that perception, semantics, and intent inform how visuals are understood.
While many examples in this book are designed in Tableau, we intend that the principles presented can be used in any software or programming language. This book is not designed to walk you through code snippets or the making of a particular chart or dashboard. Instead, it's a conceptual challenge and a paradigm shift.
We hope this book is a dialog, one that introduces ideas and a philosophy to design.
Here's how this book is structured.
PART A: Visual Perception
Part A will focus on perception and common practice. As the dominant dialog in academic and practitioner circles most frequently centers on perception, we will cover this lightly and reference other resources. We will first discuss how the human visual system is extremely adept at processing visual information such as color, shape, and size. By encoding data in these visual attributes, visualization tools offload cognitive work to the perceptual system, enabling users to focus on answering questions about their data rather than reading and comparing data values. We will then delve into common practice, such as the creation of charts and dashboards. We will also explore interaction patterns on the authoring side as well as for consumption.
PART B: Semantics
Semantics is the study of meaning. Language is a way of expressing and elucidating meaning in various ways; it makes use of a wide range of linguistic tools such as words, intonation, imagery, and gestures. In Part B, we explore how semantics can communicate visual expression through well-designed dashboards, effectively conveying the goals of the author to their audience as well as understanding the analytical goals of the user during data exploration. We will describe practitioner and researcher perspectives on how understanding the meaning of data and what it represents can in turn, help inform aesthetic yet useful ways of visualizing the data. In the next unit of the book, we will discuss in more detail what these analytical goals are, what we refer to as intent.
PART C: Intent
A user expresses an information need as an intent when interacting with another human or with a computer system. Humans are particularly skilled at attempting to understand or seek clarification of an intent that is expressed. However, humans and computers have asymmetric and complementary skills, particularly in the areas of data, semantics, and aesthetics. Also, human-to-machine interaction can be particularly challenging as the human's and computer's mental models of what they would like to seek needs to be expressed and interpreted clearly. Part C will walk through various techniques for designing analytical conversations between data and the human in relation to their intent.
PART D: Putting It All Together
As we step into the last section of the book, we will discuss how perception, semantics, and intent come together as a whole. Revisiting concepts from previous chapters, we provide key takeaways to help empower the reader, that’s you.
Let's dig in.
Reader Support for This Book
Companion Files
As you read through this book, references are included for supporting material. You can find a bibliography of these references on the support page for this book at www.wiley.com/go/fafordataviz. Additional information about the book, including a glossary of terms, newsletters, and regular updates about the book can be found at www.functionalaestheticsbook.com.
How to Contact the Publisher
If you believe you've found a mistake in this book, please bring it to our attention. At John Wiley & Sons, we understand how important it is to provide our customers with accurate content, but even with our best efforts, an error may occur.
In order to submit your possible errata, please email it to our Customer Service Team at [email protected] with the subject line “Possible Book Errata Submission.”
Yosemite Valley
Csondy/Getty Images
PART APerception
The description of a single visual memory highlights the way our senses are so important to how we experience the world.
As Ansel Adam says,
Yosemite Valley, to me, is always a sunrise, a glitter of green and golden wonder in a vast edifice of stone and space.
We are equipped with five senses—sight, hearing, smell, taste, and touch—to help understand the environment around us. Our senses have the uncanny ability to convert real-world information into electric and chemical signals that can be processed by the brain. Perception refers to the set of processes we use to make sense of these signals that we encounter every second, from the page of this book in front of you, to the smell of freshly brewed coffee emanating from your kitchen. Our perceptions are based on how we interpret all these different sensations, which are sensory impressions we get from the stimuli in the world around us.
Close your eyes. What do you remember about that restaurant where you dined? The color of the walls, the angle of the shadows, the smell of the food before you took that first bite? Whether or not we know it, we selectively attend to different things in our environment. Our brains simply don't have the capacity to attend to every single detail in the world around us. The first step of perception is the decision of what to attend to. Depending on the environment, and depending on us as individuals, we might focus on a familiar stimulus or something new. When we attend to one specific thing in our environment—whether it is a smell, a feeling, a sound, or something else entirely—it becomes the attended stimulus.
Once we've attended to a stimulus and our brains have received and organized the information, we interpret it in a way that makes sense using our existing knowledge of the world. Interpretation simply means that we take the information that we have sensed and organized and turn it into something that we can categorize. This happens unconsciously thousands of times a day.
The topic of perception is among the oldest and most important in psychology. Significant literature by leading experts explores this at far greater depths than we will explore in this book. However, an understanding of how the human visual system works will provide the necessary foundation in this book for unraveling the characteristics of what makes charts functionally aesthetic. In Part A, we will explore the various aspects of human perception and how they each play an important role in seeing and understanding data.
CHAPTER 1The Science Behind Perception
The human visual system is quite intricate and remarkable. Cameras often serve as a common reference point when first starting to understand the mechanics of the eye itself. Like the lens on a camera, the cornea serves as the outermost lens and window to the world, protecting the viscous eye but letting light enter. The colorful iris acts as a shutter mechanism to control light. Similar to the camera metaphor, the eye can expand and contract to focus properly. While there are optical similarities between a camera and the eye in the way they capture an image, a camera does not have perceptual or cognitive abilities and the metaphor starts to break down when we get to how the brain interprets this information.
Seeing and Understanding Imagery
Beyond a picture that we perceive, our brains take this visual information and perform calculations on it. Think about it as you drive. You are analyzing in real time the placement of your car against the other cars as well as how fast the other cars are moving. When you buy a new car, your perception has to adjust from that vantage point: your new car might be smaller or larger, affecting all of your calculations and processes. Vision is an information processing phenomenon where neurons in our brain perform calculations on the signals so that we reason about what is in the imagery we see and how we act on it. In other words, it's not a simple picture or movie that we perceive.
Long before Isaac Newton discovered that colors were physical and quantifiable phenomena of light's spectrum, skilled artists figured out ways to trick our brain's circuitry. They often used color in clever ways to simulate illusions of the mind, whether it is creating the perception of depth on a flat surface or the sense of movement in a static object.
Claude Monet, an impressionist painter from the late 1800s, used unrealistic luminances in his masterpiece, Impression, Sunrise, which is shown in Figure 1.1. The scene deceptively looks rather simple; a few small rowboats at the port of Le Havre at sunrise in the foreground with the orange-red sun being the focal element. The layered hazy brushstrokes create the illusion of depth despite their imprecise details. Yet this piece is far more complex than it looks.
FIGURE 1.1Impression, Sunrise by Claude Monet
Claude Monet/Wikimedia Commons
