book

Beautiful Visualization

by Julie Steele, Noah Iliinsky

June 2010

Beginner

415 pages

14h 7m

English

O'Reilly Media, Inc.

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How This Book Is OrganizedConventions Used in This BookUsing Code ExamplesHow to Contact UsSafari® Books OnlineAcknowledgments
What Is Beauty?NovelInformativeEfficientAestheticLearning from the ClassicsThe Periodic Table of the ElementsThe London Underground MapOther Subway Maps and Periodic Tables Are Weak ImitationsHow Do We Achieve Beauty?Step Outside Default FormatsMake It InformativeIntended messageContext of use.Make It EfficientVisually emphasize what mattersUse axes to convey meaning and give free informationSlice along relevant divisionsUse conventions thoughtfullyLeverage the AestheticsPutting It Into PracticeConclusion
Question + Visual Data + Context = StorySteps for Creating an Effective VisualizationFormulate the QuestionGather the DataApply a Visual RepresentationSizeColorLocationNetworksTimeUsing multiple visual presentation methodsHands-on Visualization CreationData TasksGathering the dataSorting the data: The discovery versionSorting the data: The technical versionFormulating the QuestionGrouping the dataApplying the Visual PresentationA note about area and circlesPresenting the data with country mapsBuilding the VisualConclusion
Wordle's OriginsAnatomy of a Tag CloudFilling a Two-Dimensional SpaceHow Wordle WorksText AnalysisFinding wordsDetermining the scriptGuessing the language and removing stop wordsAssigning weights to wordsLayoutWeighted words into shapesThe playing fieldPlacementIntersection testingIs Wordle Good Information Visualization?Word Sizing Is NaïveColor Is MeaninglessFonts Are FancifulWord Count Is Not Specific EnoughHow Wordle Is Actually UsedUsing Wordle for Traditional InfovisConclusionAcknowledgmentsReferences
Why Use Color in Data Graphics?1. Vary Your Plotting Symbols2. Use Small Multiples on a Canvas3. Add Color to Your DataSo Why Bother with Color?If Color Is Three-Dimensional, Can I Encode Three Dimensions with It?Luminosity As a Means of Recovering Local DensityLooking Forward: What About Animation?MethodsConclusionReferences and Further Reading
The Need for a Better ToolLondon CallingNew York BluesBetter Tools Allow for Better ToolsSize Is Only One FactorLooking Back to Look ForwardNew York's Unique ComplexityGeography Is About RelationshipsInclude the EssentialsLeave Out the ClutterColoring Inside the LinesSweat the Small StuffTry It OnUsers Are Only HumanA City of NeighborhoodsOne Size Does Not Fit AllConclusion
Techniques and DataColorMotionAnomalies and ErrorsConclusionAcknowledgments
Early Social GraphsSocial Graphs of Amazon Book Purchasing DataDetermining the Network Around a Particular BookPutting the Results to WorkSocial Networks of Political BooksConclusionReferences
Building the VisualizationGathering the Raw DataComputing the Voting Affinity MatrixVisualizing the Data with GraphVizThe Story That EmergedWhat Makes It Beautiful?And What Makes It Ugly?LabelsOrientationParty AffiliationConclusionReferences

The Visualization TechniqueYELLOWPAGES.COMQuery LogsCategorical SimilarityVisualization As a Substrate for AnalyticsThe VisualizationAdvantages and Disadvantages of the TechniqueThe Netflix PrizePreference SimilarityLabelingCloser LooksCreating Your OwnConclusionReferences
Visualizing Social NetworksWho Wants to Visualize Social Networks?The Design of SocialActionCase Studies: From Chaos to BeautyThe Social Network of Senatorial VotingThe Social Network of TerroristsReferences
Depicting Group EditingThe DataHistory Flow: Visualizing Edit HistoriesAge of EditAuthorshipIndividual AuthorsHistory Flow in ActionCommunicating the ResultsChromogram: Visualizing One Person at a TimeShowing All the DataWhat We SawAnalyzing the DataConclusion
Categorical DataParallel SetsVisual RedesignA New Data ModelThe Database ModelGrowing the TreeParallel Sets in the Real WorldConclusionReferences
Briefing and Conceptual DirectionsUnderstanding the Data SituationExploring the DataFirst Visual DraftsThe Visual PrincipleThe Final ProductAll SubmissionsBy PrizeBy CategoryBy CountryBy YearBy Year and CategoryExhibitionConclusionAcknowledgmentsReferences
The More, the Better?Databases As NetworksData Model Definition Plus EmergenceNetwork DimensionalityThe Matrix MacroscopeReducing for ComplexityFurther Matrix OperationsThe Refined MatrixScaling UpFurther ApplicationsConclusionAcknowledgmentsReferences
Getting Data: The Article Search APIManaging Data: Using ProcessingThree Easy StepsFaceted SearchingMaking ConnectionsConclusion
Collecting Some DataLet's Clean 'Em FirstPython, Map/Reduce, and HadoopThe First Pass at the VisualizationProcessingThe Underlay MapNow, Where's That Data We Just Processed?Scene 1, Take 1No ScaleNo Sense of TimeTime-LapseScene 1, Take 2Let's Run This Thing and See What Happens!The Second Pass at the VisualizationBack to That Scale ProblemMassaging the Data Some MoreThe New Data FormatVisual Scale and Other Visualization OptimizationsGetting the Time Lapse WorkingSemiautomatingMath for Rendering Time-Lapse VideoSo, What Do We Do with This Thing?ConclusionAcknowledgments
Our Multimodal ArenaOur Roadmap to Creative ThinkingBeauty and SymmetryThe Computational MediumInterpretation As a FilterProject DiscussionAllobrainArtificial NatureHydrogen BondHydrogen AtomHydrogen Atom with SpinCoherent Precession of Electron SpinConclusionReferences
BackgroundImpact on Forensic WorkThe Virtual Autopsy ProcedureData AcquisitionComputed tomography: Use of dual energy CTMRI: Use of synthetic magnetic resonance imagingVisualization: Image AnalysisObjective DocumentationAdvantages and Disadvantages of Virtual AutopsyThe Future for Virtual AutopsiesConclusionReferences and Suggested Reading
Principles of AnimationAnimation in Scientific VisualizationLearning from CartooningThe Downsides of AnimationGapMinder and Animated ScatterplotsToo many dots?Testing Animated ScatterplotsExploration with animation is slowerAnimation is less accuratePresentation Is Not ExplorationTypes of AnimationDynamic Data, Animated RecenteringA Taxonomy of AnimationsStaging Animations with DynaVisPrinciples of AnimationConclusion: Animate or Not?Further ReadingAcknowledgmentsReferences
Visualization: It's an Elephant.Visualization: It's Art.Visualization: It's Business.Visualization: It's Timeless.Visualization: It's Right Now.Visualization: It's Coded.Visualization: It's Clear.Visualization: It's Learnable.Visualization: It's a Buzzword.Visualization: It's an Opportunity.

Overview

Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.

This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.

Explore the importance of storytelling with a simple visualization exercise
Learn how color conveys information that our brains recognize before we're fully aware of it
Discover how the books we buy and the people we associate with reveal clues to our deeper selves
Recognize a method to the madness of air travel with a visualization of civilian air traffic
Find out how researchers investigate unknown phenomena, from initial sketches to published papers

Contributors include:

Nick Bilton,Michael E. Driscoll,Jonathan Feinberg,Danyel Fisher,Jessica Hagy,Gregor Hochmuth,Todd Holloway,Noah Iliinsky,Eddie Jabbour,Valdean Klump,Aaron Koblin,Robert Kosara,Valdis Krebs,JoAnn Kuchera-Morin et al.,Andrew Odewahn,Adam Perer,Anders Persson,Maximilian Schich,Matthias Shapiro,Julie Steele,Moritz Stefaner,Jer Thorp,Fernanda Viegas,Martin Wattenberg,and Michael Young.