Beautiful Data. The Stories Behind Elegant Data Solutions

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• Beautiful Data
  • SPECIAL OFFER: Upgrade this ebook with OReilly
  • A Note Regarding Supplemental Files
  • Preface
    • How This Book Is Organized
    • Conventions Used in This Book
    • Using Code Examples
    • How to Contact Us
    • Safari Books Online
  • 1. Seeing Your Life in Data
    • Personal Environmental Impact Report (PEIR)
    • your.flowingdata (YFD)
    • Personal Data Collection
      • Working Data Collection into Routine
        • Asynchronous data collection
    • Data Storage
    • Data Processing
    • Data Visualization
      • PEIR
        • Mapping location-based data
        • Experimenting with visual cues
        • Mapping multivariate location traces
        • Choosing a color scheme
        • Making trips interactive
        • Displaying distributions
        • Sharing personal data
      • YFD
    • The Point
    • How to Participate
  • 2. The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods
    • Introduction: User Empathy Is the New Black
      • What Is UX?
      • The Benefits of Applying UX Best Practices to Data Collection
    • The Project: Surveying Customers About a New Luxury Product
    • Specific Challenges to Data Collection
      • Challenges of Accessibility
      • Challenges of Perception
        • Building trust
        • Length of survey
        • Accurate data collection
        • Motivation
    • Designing Our Solution
      • Design Philosophy
      • Designing the Form Layout
        • Web form typography and accessibility
        • Giving them some space
        • Accommodating different browsers and testing for compatibility
        • Interaction design considerations: Dynamic form length
        • Designing trust
        • Designing for accurate data collection
        • Motivation
        • Reporting the live data results
    • Results and Reflection
  • 3. Embedded Image Data Processing on Mars
    • Abstract
    • Introduction
    • Some Background
    • To Pack or Not to Pack
    • The Three Tasks
    • Slotting the Images
    • Passing the Image: Communication Among the Three Tasks
    • Getting the Picture: Image Download and Processing
    • Image Compression
    • Downlink, or, Its All Downhill from Here
    • Conclusion
  • 4. Cloud Storage Design in a PNUTShell
    • Introduction
    • Updating Data
      • The Challenge
      • Our Approach
        • More on mastership
        • Supporting ordered data
        • Trading off consistency for availability
    • Complex Queries
      • The Challenge
      • Our Approach
    • Comparison with Other Systems
      • Google's BigTable
      • Amazon's Dynamo
      • Microsoft Azure SDS
      • Other Related Systems
      • Other Systems at Yahoo!
    • Conclusion
    • Acknowledgments
    • References
  • 5. Information Platforms and the Rise of the Data Scientist
    • Libraries and Brains
    • Facebook Becomes Self-Aware
    • A Business Intelligence System
    • The Death and Rebirth of a Data Warehouse
    • Beyond the Data Warehouse
    • The Cheetah and the Elephant
    • The Unreasonable Effectiveness of Data
    • New Tools and Applied Research
    • MAD Skills and Cosmos
    • Information Platforms As Dataspaces
    • The Data Scientist
    • Conclusion
  • 6. The Geographic Beauty of a Photographic Archive
    • Beauty in Data: Geograph
    • Visualization, Beauty, and Treemaps
      • What Is Beauty in Visual Data Exploration?
      • Making Treemaps Beautiful: A Geographic Perspective
    • A Geographic Perspective on Geograph Term Use
      • Representing the Term Hierarchy
      • Representing Absolute Location with Color
      • Representing Relative Location with Spatial Treemaps
      • Representing Location Displacement
    • Beauty in Discovery
    • Reflection and Conclusion
    • Acknowledgments
    • References
  • 7. Data Finds Data
    • Introduction
    • The Benefits of Just-in-Time Discovery
    • Corruption at the Roulette Wheel
    • Enterprise Discoverability
    • Federated Search Ain't All That
    • Directories: Priceless
    • Relevance: What Matters and to Whom?
    • Components and Special Considerations
      • The Existence of, and Availability of, Observations
      • The Ability to Extract and Classify Features from the Observations
      • The Ability to Efficiently Discover Related Historical Context
      • The Ability to Make Assertions (Same or Related) About New Observations
      • The Ability to Recognize When New Observations Reverse Earlier Assertions
      • The Ability to Accumulate and Persist This Asserted Context
      • The Ability to Recognize the Formation of Relevance/Insight
      • The Ability to Notify the Appropriate Entity of Such Insight
    • Privacy Considerations
    • Conclusion
  • 8. Portable Data in Real Time
    • Introduction
    • The State of the Art
      • Transport
        • XMPP
        • BitTorrent
        • Proprietary/P2P
      • Formats
      • APIs
      • Polling
        • Rate limiting
        • Getting it right
        • Zero miles per gallon efficiency
      • Events
        • HTML 5 events
      • WAN Scale Events
    • Social Data Normalization
      • Business Value of Data
        • Public versus private
    • Conclusion: Mediation via Gnip
  • 9. Surfacing the Deep Web
    • What Is the Deep Web?
    • Alternatives to Offering Deep-Web Access
      • Basics of HTML Form Processing
      • Queries and Query Templates
      • Selecting Input Combinations
        • Quality of query templates
        • Informativeness test
        • Searching for informative query templates
      • Predicting Input Values
        • Generic text inputs
        • Typed text inputs
    • Conclusion and Future Work
    • References
  • 10. Building Radiohead's House of Cards
    • How It All Started
    • The Data Capture Equipment
      • Velodyne Lidar
      • Geometric Informatics
    • The Advantages of Two Data Capture Systems
    • The Data
    • Capturing the Data, aka "The Shoot"
      • The Outdoor Lidar Shoot
      • The Indoor Lidar Shoot
      • The Indoor GeoVideo Shoot
    • Processing the Data
    • Post-Processing the Data
    • Launching the Video
    • Conclusion
  • 11. Visualizing Urban Data
    • Introduction
    • Background
    • Cracking the Nut
    • Making It Public
    • Revisiting
    • Conclusion
  • 12. The Design of Sense.us
    • Visualization and Social Data Analysis
    • Data
    • Visualization
      • Design Considerations
        • Foster personal relevance
        • Provide effective visual encodings
        • Make each display distinct
        • Support intuitive exploration
        • Be engaging and playful
      • Visualization Designs
        • Job Voyager
        • Birthplace Voyager
        • U.S. census state map and scatterplot
        • Population pyramid
        • Implementation details
    • Collaboration
      • View Sharing
      • Doubly Linked Discussion
      • Pointing via Graphical Annotation
      • Collecting and Linking Views
      • Awareness and Social Navigation
      • Unobtrusive Collaboration
    • Voyagers and Voyeurs
      • Hunting for Patterns
      • Making Sense of It All
      • Crowd Surfing
    • Conclusion
    • References
  • 13. What Data Doesn't Do
    • When Doesn't Data Drive?
      • 1. More Data Isn't Always Better
      • 2. More Data Isn't Always Easy
      • 3. Data Alone Doesn't Explain
      • 4. Data Isn't Good for a Single Answer
      • 5. Data Doesn't Predict
      • 6. Probability Isn't Intuitive
      • 7. Probabilities Aren't Intuitive
      • 8. The Real World Doesn't Create Random Variables
      • 9. Data Doesn't Stand Alone
      • 10. Data Isn't Free from the Eye of the Beholder
    • Conclusion
    • References
  • 14. Natural Language Corpus Data
    • Word Segmentation
    • Secret Codes
    • Spelling Correction
    • Other Tasks
      • Language Identification
      • Spam Detection and Other Classification Tasks
      • Author Identification (Stylometry)
      • Document Unshredding and DNA Sequencing
      • Machine Translation
    • Discussion and Conclusion
    • Acknowledgments
  • 15. Life in Data: The Story of DNA
    • DNA As a Data Store
      • DNA Makes RNA Makes Proteins
      • Hacking Your DNA Data Store with Drugs
      • Cancer
      • Replication
      • Cracking the Code
      • DNA As Digital Storage
      • Evolution As an Algorithm
    • DNA As a Data Source
      • A Quantum Leap
      • "My God, It's Full of Bases"
    • Fighting the Data Deluge
      • The Sanger Institute's Sequencing Platform
        • Project management
      • Flexible Data Capture
      • Instrument and Data Management
    • The Future of DNA
      • How to Become a Genetic Hacker
      • Next Next-Gen
      • The Era of Big Data
    • Acknowledgments
  • 16. Beautifying Data in the Real World
    • The Problem with Real Data
    • Providing the Raw Data Back to the Notebook
    • Validating Crowdsourced Data
    • Representing the Data Online
      • Unique Identifiers for Chemical Entities
      • Open Data and Accessible Services Enable a Wide Range of Visualization and Analysis Options
      • Integrating Data with a Central Aggregation Service
      • Enabling Data Integration via Unique Identifiers and Self-Describing Data Formats
    • Closing the Loop: Visualizations to Suggest New Experiments
    • Building a Data Web from Open Data and Free Services
    • Acknowledgments
    • References
  • 17. Superficial Data Analysis: Exploring Millions of Social Stereotypes
    • Introduction
    • Preprocessing the Data
    • Exploring the Data
    • Age, Attractiveness, and Gender
    • Looking at Tags
    • Which Words Are Gendered?
    • Clustering
    • Conclusion
    • Acknowledgments
    • References
  • 18. Bay Area Blues: The Effect of the Housing Crisis
    • Introduction
    • How Did We Get the Data?
    • Geocoding
    • Data Checking
    • Analysis
    • The Influence of Inflation
    • The Rich Get Richer and the Poor Get Poorer
    • Geographic Differences
    • Census Information
    • Exploring San Francisco
    • Conclusion
    • References
  • 19. Beautiful Political Data
    • Example 1: Redistricting and Partisan Bias
    • Example 2: Time Series of Estimates
    • Example 3: Age and Voting
    • Example 4: Public Opinion and Senate Voting on Supreme Court Nominees
    • Example 5: Localized Partisanship in Pennsylvania
    • Conclusion
    • References
  • 20. Connecting Data
    • What Public Data Is There, Really?
    • The Possibilities of Connected Data
    • Within Companies
    • Impediments to Connecting Data
      • The Representation Problem
      • Shared Nouns and Shared Verbs
      • The Same Thing with Different Names
      • Different Things with the Same Name
    • Possible Solutions
      • Matching on Multiple Fields
      • Collective Reconciliation
    • Conclusion
  • A. Contributors
  • Index
  • About the Authors
  • COLOPHON
  • SPECIAL OFFER: Upgrade this ebook with OReilly

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