Cover image for The Data Journalism Handbook

Book description

When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field.

Table of Contents

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