Spis treści: „The Data Journalism Handbook”

Spis treści publikacji pt. „The Data Journalism Handbook”.

Tytuł: „The Data Journalism Handbook”
Autorzy: Jonathan Gray, Lucy Chambers, Liliana Bounegru
Wydawca: O’Reilly Media
Data wydania: 2012-07-12
Język: Angielski
Liczba stron: 242

Spis treści

Spis treści / Table of contents

Your Skills Set / 7

A Remedy for Information Asymmetry / 7

An Answer to Data-Driven PR / 7

Providing Independent Interpretations of Official Information / 8

Dealing with the Data Deluge / 8

Our Lives Are Data / 9

A Way to Save Time / 9

An Essential Part of the Journalists’ Toolkit / 9

Adapting to Changes in Our Information Environment / 10

A Way to See Things You Might Not Otherwise See / 10

A Way To Tell Richer Stories / 11

Some Favorite Examples / 11

Do No Harm in the Las Vegas Sun / 11

Government Employee Salary Database / 12

Full-Text Visualization of the Iraqi War Logs, Associated Press / 13

Murder Mysteries / 15

Message Machine /16

Chartball / 16

Data Journalism in Perspective / 17

Computer-Assisted Reporting and Precision Journalism / 18

Data Journalism and Computer-Assisted Reporting / 21

Data Journalism Is About Mass Data Literacy / 21

2. In The Newsroom / 23

The ABC’s Data Journalism Play / 24

Our Team / 26

Where Did We Get the Data From? / 27

What Did We Learn? / 27

The Big Picture: Some Ideas / 27

Data Journalism at the BBC / 28

Make It Personal / 29

Simple Tools / 29

Mining The Data / 30

Understanding An Issue / 31

Team Overview / 31

How the News Apps Team at the Chicago Tribune Works / 32

Behind the Scenes at the Guardian Datablog / 34

Data Journalism at the Zeit Online / 37

How to Hire a Hacker / 41

Harnessing External Expertise Through Hackathons / 44

Following the Money: Data Journalism and Cross-Border Collaboration / 48

Our Stories Come As Code / 51

Kaas & Mulvad: Semi-Finished Content for Stakeholder Groups / 54

Processes: Innovative IT Plus Analysis / 56

Value Created: Personal and Firm Brands and Revenue / 57

Key Insights of This Example / 57

Business Models for Data Journalism / 58

3. Case Studies / 61

The Opportunity Gap / 62

A Nine Month Investigation into European Structural Funds / 64

1. Identify who keeps the data and how it is kept / 65
2. Download and prepare the data / 65
3. Create a database / 65
4. Double-checking and analysis / 66

The Eurozone Meltdown / 66

Covering the Public Purse with OpenSpending.org / 71

Finnish Parliamentary Elections and Campaign Funding / 75

1. Find data and developers / 76
2. Brainstorm for ideas / 76
3. Implement the idea on paper and on the Web / 77
4. Publish the data 77

Electoral Hack in Realtime (Hacks/Hackers Buenos Aires) / 78

What Data Did We Use? / 78

How Was It Developed? / 79

Pros / 79

Cons / 80

Implications / 80

Data in the News: WikiLeaks / 80

Mapa76 Hackathon / 83

The Guardian Datablog’s Coverage of the UK Riots / 85

Phase One: The Riots As They Happened / 87

Phase Two: Reading the Riots / 87

Illinois School Report Cards / 88

Hospital Billing / 90

Care Home Crisis / 92

The Tell-All Telephone / 93

Which Car Model? MOT Failure Rates / 95

Bus Subsidies in Argentina / 96

Who Worked on the Project? / 100

What Tools Did We Use? / 100

Citizen Data Reporters / 100

The Big Board for Election Results / 104

Crowdsourcing the Price of Water / 106

4. Getting Data / 109

A Five Minute Field Guide / 110

Streamlining Your Search / 110

Browse Data Sites and Services / 111

Ask a Forum / 113

Ask a Mailing List / 113

Join Hacks/Hackers / 114

Ask an Expert / 114

Learn About Government IT / 114

Search Again / 115

Write an FOI Request / 115

Your Right to Data / 116

Wobbing Works. Use It! / 121

Case Study 1: Farm Subsidy / 121

Case Study 2: Side Effects / 122

Case Study 3: Smuggling Death / 123

Getting Data from the Web / 124

What Is Machine-Readable Data? / 125

Scraping Websites: What For? / 125

What You Can and Cannot Scrape / 126

Tools That Help You Scrape / 126

How Does a Web Scraper Work? / 127

The Anatomy of a Web Page / 127

An Example: Scraping Nuclear Incidents with Python / 128

The Web as a Data Source / 132

Web Tools / 132

Web Pages, Images, and Videos / 134

Emails / 136

Trends / 136

Crowdsourcing Data at the Guardian Datablog / 137

How the Datablog Used Crowdsourcing to Cover Olympic Ticketing / 139

Using and Sharing Data: the Black Letter, the Fine Print, and Reality / 142

5. Understanding Data / 147

Become Data Literate in Three Simple Steps / 148

1. How was the data collected? / 149
2. What’s in there to learn? / 150
3. How reliable is the information? / 151

Tips for Working with Numbers in the News / 151

Basic Steps in Working with Data / 153

Know the Questions You Want to Answer / 153

Cleaning Messy Data / 154

Data May Have Undocumented Features / 155

The £32 Loaf of Bread / 157

Start With the Data, Finish With a Story / 158

Data Stories / 159

Data Journalists Discuss Their Tools of Choice / 161

Using Data Visualization to Find Insights in Data / 165

Using Visualization to Discover Insights / 165

Which Tools to Use / 170

An Example: Making Sense of US Election Contribution Data / 170

What To Learn From This / 176

Get the Source Code / 176

6. Delivering Data / 177

Presenting Data to the Public / 178

To Visualize or Not to Visualize? / 178

Using Motion Graphics / 178

Telling the World / 178

Publishing the Data / 179

Opening Up Your Data / 179

Starting an Open Data Platform / 180

Making Data Human / 180

Open Data, Open Source, Open News / 181

Add A Download Link / 181

Know Your Scope / 182

How to Build a News App / 182

Who Is My Audience and What Are Their Needs? / 183

How Much Time Should I Spend on This? / 184

How Can I Take Things to the Next Level? / 184

Wrapping Up / 185

News Apps at ProPublica / 185

Visualization as the Workhorse of Data Journalism / 186

Tip 1: Use small multiples to quickly orient yourself in a large dataset / 187

Tip 2: Look at your data upside down and sideways / 189

Tip 3: Don’t assume / 189

Tip 4: Avoid obsessing over precision / 190

Tip 5: Create chronologies of cases and events / 190

Tip 6: Meet with your graphics department early and often / 190

Tips For Publication / 190

Using Visualizations to Tell Stories / 191

Seeing the Familiar in a New Way / 192

Showing Change Over Time / 193

Comparing Values / 194

Showing Connections and Flows / 195

Designing With Data / 196

Showing Hierarchy / 197

Browsing Large Databases / 198

Envisioning Alternate Outcomes / 199

When Not To Use Data Visualization / 200

Different Charts Tell Different Tales / 201

Data Visualization DIY: Our Top Tools / 206

Google Fusion Tables / 207

Tableau Public / 208

Google Spreadsheet Charts / 210

Datamarket / 210

Many Eyes / 211

Color Brewer / 212

And Some More / 212

How We Serve Data at Verdens Gang / 212

Numbers / 212

Networks / 213

Maps / 213

Text Mining / 215

Concluding Notes / 215

Public Data Goes Social / 216

Engaging People Around Your Data / 218

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