
Data Journalism Handbook
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Content
- Intro
- Table of Contents
- Preface
- For the Great Unnamed
- Contributors
- What This Book Is (And What It Isn't)
- Conventions Used in This Book
- Safari® Books Online
- How to Contact Us
- Chapter 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
- Chapter 2. In The Newsroom
- The ABC's 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
- Chapter 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 Datablog's 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
- Chapter 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
- Chapter 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. What's 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
- Chapter 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: Don't 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
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