The Data Journalism Handbook

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• The Data Journalism Handbook
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• Preface
  • For the Great Unnamed
  • Contributors
  • What This Book Is (And What It Isnt)
  • Conventions Used in This Book
  • Safari Books Online
  • How to Contact Us
• 1. Introduction
  • What Is Data Journalism?
  • Why Journalists Should Use Data
  • Why Is Data Journalism Important?
    • Filtering the Flow of Data
    • New Approaches to Storytelling
    • Like Photo Journalism with a Laptop
    • Data Journalism Is the Future
    • Number-Crunching Meets Word-Smithing
    • Updating Your Skills Set
    • A Remedy for Information Asymmetry
    • An Answer to Data-Driven PR
    • Providing Independent Interpretations of Official Information
    • Dealing with the Data Deluge
    • Our Lives Are Data
    • A Way to Save Time
    • An Essential Part of the Journalists Toolkit
    • Adapting to Changes in Our Information Environment
    • A Way to See Things You Might Not Otherwise See
    • A Way To Tell Richer Stories
  • Some Favorite Examples
    • Do No Harm in the Las Vegas Sun
    • Government Employee Salary Database
    • Full-Text Visualization of the Iraqi War Logs, Associated Press
    • Murder Mysteries
    • Message Machine
    • Chartball
  • Data Journalism in Perspective
    • Computer-Assisted Reporting and Precision Journalism
    • Data Journalism and Computer-Assisted Reporting
    • Data Journalism Is About Mass Data Literacy
• 2. In The Newsroom
  • The ABCs Data Journalism Play
    • Our Team
    • Where Did We Get the Data From?
    • What Did We Learn?
    • The Big Picture: Some Ideas
  • Data Journalism at the BBC
    • Make It Personal
    • Simple Tools
    • Mining The Data
    • Understanding An Issue
    • Team Overview
  • How the News Apps Team at the Chicago Tribune Works
  • Behind the Scenes at the Guardian Datablog
  • Data Journalism at the Zeit Online
  • How to Hire a Hacker
  • Harnessing External Expertise Through Hackathons
  • Following the Money: Data Journalism and Cross-Border Collaboration
  • Our Stories Come As Code
  • Kaas & Mulvad: Semi-Finished Content for Stakeholder Groups
    • Processes: Innovative IT Plus Analysis
    • Value Created: Personal and Firm Brands and Revenue
    • Key Insights of This Example
  • Business Models for Data Journalism
• 3. Case Studies
  • The Opportunity Gap
  • A Nine Month Investigation into European Structural Funds
    • 1. Identify who keeps the data and how it is kept
    • 2. Download and prepare the data
    • 3. Create a database
    • 4. Double-checking and analysis
  • The Eurozone Meltdown
  • Covering the Public Purse with OpenSpending.org
  • Finnish Parliamentary Elections and Campaign Funding
    • 1. Find data and developers
    • 2. Brainstorm for ideas
    • 3. Implement the idea on paper and on the Web
    • 4. Publish the data
  • Electoral Hack in Realtime (Hacks/Hackers Buenos Aires)
    • What Data Did We Use?
    • How Was It Developed?
    • Pros
    • Cons
    • Implications
  • Data in the News: WikiLeaks
  • Mapa76 Hackathon
  • The Guardian Datablogs Coverage of the UK Riots
    • Phase One: The Riots As They Happened
    • Phase Two: Reading the Riots
  • Illinois School Report Cards
  • Hospital Billing
  • Care Home Crisis
  • The Tell-All Telephone
  • Which Car Model? MOT Failure Rates
  • Bus Subsidies in Argentina
    • Who Worked on the Project?
    • What Tools Did We Use?
  • Citizen Data Reporters
  • The Big Board for Election Results
  • Crowdsourcing the Price of Water
• 4. Getting Data
  • A Five Minute Field Guide
    • Streamlining Your Search
    • Browse Data Sites and Services
    • Ask a Forum
    • Ask a Mailing List
    • Join Hacks/Hackers
    • Ask an Expert
    • Learn About Government IT
    • Search Again
    • Write an FOI Request
  • Your Right to Data
  • Wobbing Works. Use It!
    • Case Study 1: Farm Subsidy
    • Case Study 2: Side Effects
    • Case Study 3: Smuggling Death
  • Getting Data from the Web
    • What Is Machine-Readable Data?
    • Scraping Websites: What For?
    • What You Can and Cannot Scrape
    • Tools That Help You Scrape
    • How Does a Web Scraper Work?
    • The Anatomy of a Web Page
    • An Example: Scraping Nuclear Incidents with Python
  • The Web as a Data Source
    • Web Tools
    • Web Pages, Images, and Videos
    • Emails
    • Trends
  • Crowdsourcing Data at the Guardian Datablog
  • How the Datablog Used Crowdsourcing to Cover Olympic Ticketing
  • Using and Sharing Data: the Black Letter, the Fine Print, and Reality
• 5. Understanding Data
  • Become Data Literate in Three Simple Steps
    • 1. How was the data collected?
      • Amazing GDP growth
      • Crime is always on the rise
      • What you can do
    • 2. Whats in there to learn?
      • Risk of Multiple Sclerosis doubles when working at night
      • On average, 1 in every 15 Europeans totally illiterate
      • What you can do
    • 3. How reliable is the information?
      • The sample size problem
      • Drinking tea lowers the risk of stroke
      • What you can do
  • Tips for Working with Numbers in the News
  • Basic Steps in Working with Data
    • Know the Questions You Want to Answer
    • Cleaning Messy Data
    • Data May Have Undocumented Features
  • The 32 Loaf of Bread
  • Start With the Data, Finish With a Story
  • Data Stories
  • Data Journalists Discuss Their Tools of Choice
  • Using Data Visualization to Find Insights in Data
    • Using Visualization to Discover Insights
      • Learn how to visualize data
      • Analyze and interpret what you see
      • Document your insights and steps
      • Transform data
    • Which Tools to Use
    • An Example: Making Sense of US Election Contribution Data
    • What To Learn From This
    • Get the Source Code
• 6. Delivering Data
  • Presenting Data to the Public
    • To Visualize or Not to Visualize?
    • Using Motion Graphics
    • Telling the World
    • Publishing the Data
    • Opening Up Your Data
    • Starting an Open Data Platform
    • Making Data Human
    • Open Data, Open Source, Open News
    • Add A Download Link
    • Know Your Scope
  • How to Build a News App
    • Who Is My Audience and What Are Their Needs?
    • How Much Time Should I Spend on This?
    • How Can I Take Things to the Next Level?
    • Wrapping Up
  • News Apps at ProPublica
  • Visualization as the Workhorse of Data Journalism
    • Tip 1: Use small multiples to quickly orient yourself in a large dataset
    • Tip 2: Look at your data upside down and sideways
    • Tip 3: Dont assume
    • Tip 4: Avoid obsessing over precision
    • Tip 5: Create chronologies of cases and events
    • Tip 6: Meet with your graphics department early and often
    • Tips For Publication
  • Using Visualizations to Tell Stories
    • Seeing the Familiar in a New Way
    • Showing Change Over Time
    • Comparing Values
    • Showing Connections and Flows
    • Designing With Data
    • Showing Hierarchy
    • Browsing Large Databases
    • Envisioning Alternate Outcomes
    • When Not To Use Data Visualization
  • Different Charts Tell Different Tales
  • Data Visualization DIY: Our Top Tools
    • Google Fusion Tables
    • Tableau Public
    • Google Spreadsheet Charts
    • Datamarket
    • Many Eyes
    • Color Brewer
    • And Some More
  • How We Serve Data at Verdens Gang
    • Numbers
    • Networks
    • Maps
    • Text Mining
    • Concluding Notes
  • Public Data Goes Social
  • Engaging People Around Your Data
• About the Authors
• Colophon
• SPECIAL OFFER: Upgrade this ebook with OReilly
• Copyright

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