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Data-Driven Storytelling [Pehme köide]

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  • Formaat: Paperback / softback, 296 pages, kõrgus x laius: 234x156 mm, kaal: 538 g, 10 Tables, black and white; 15 Line drawings, color; 1 Line drawings, black and white; 41 Halftones, color; 3 Halftones, black and white; 56 Illustrations, color; 14 Illustrations, black and white
  • Sari: AK Peters Visualization Series
  • Ilmumisaeg: 29-Mar-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138197106
  • ISBN-13: 9781138197107
Teised raamatud teemal:
Data-Driven StorytellingSuurem pilt
  • Formaat: Paperback / softback, 296 pages, kõrgus x laius: 234x156 mm, kaal: 538 g, 10 Tables, black and white; 15 Line drawings, color; 1 Line drawings, black and white; 41 Halftones, color; 3 Halftones, black and white; 56 Illustrations, color; 14 Illustrations, black and white
  • Sari: AK Peters Visualization Series
  • Ilmumisaeg: 29-Mar-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138197106
  • ISBN-13: 9781138197107
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138197107.html
Märksõnad:
This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.

Arvustused

Trumpets please! This lucidly written report on data-driven storytelling lays out the compelling benefits and substantial challenges of this potent journalistic innovation. The strong team of authors offer fresh thinking and thoughtful guidance on exploration, explanation, engagement, ethics, and evaluation. Packed with examples for practitioners and references for researchers, this book opens up fresh possibilities that extend information visualization into decision-making, policy-shifting, and mind-changing applications.

--Ben Shneiderman, University of Maryland

Data-Driven Storytelling presents an accessible and informative introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, Data-Driven Storytelling offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. The newest addition to the simply outstanding CRC Press 'A K Peters Visualization Series', Data-Driven Storytelling is unreservedly recommended for professional, corporate, college, and university library Computer Science & Engineering collections and supplemental studies reading lists.

--Midwest Book Review, May 2018

Data-Driven Storytelling promotes an efficient way of data-driven storytelling in professionalism and its profound implications. This approach involves visualizations, explorations, explanations, curated visuals, narrative design patterns, process from analysis to communication, impressing the audience, ethical guidelines and evaluation methods on successful the data driven storytelling.

--Ramalingam Shanmugam, Journal of Statistical Computation and Simulation

This bible of data-driven storytelling covers everything you need to know about the topic, from storytelling techniques (including scrollytelling) and narrative design patterns to evaluation and ethics in storytelling, providing readers with explanations and examples of the concepts described as well as extensive reference material. All of this is expressed in an accurate, concise, and clear way.

--Lorenzo Amabili, University of Groningen, Book Review in Nightingale Trumpets please! This lucidly written report on data-driven storytelling lays out the compelling benefits and substantial challenges of this potent journalistic innovation. The strong team of authors offer fresh thinking and thoughtful guidance on exploration, explanation, engagement, ethics, and evaluation. Packed with examples for practitioners and references for researchers, this book opens up fresh possibilities that extend information visualization into decision-making, policy-shifting, and mind-changing applications.

--Ben Shneiderman, University of Maryland

Data-Driven Storytelling presents an accessible and informative introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, Data-Driven Storytelling offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners. The newest addition to the simply outstanding CRC Press 'A K Peters Visualization Series', Data-Driven Storytelling is unreservedly recommended for professional, corporate, college, and university library Computer Science & Engineering collections and supplemental studies reading lists.

--Midwest Book Review, May 2018

Data-Driven Storytelling promotes an efficient way of data-driven storytelling in professionalism and its profound implications. This approach involves visualizations, explorations, explanations, curated visuals, narrative design patterns, process from analysis to communication, impressing the audience, ethical guidelines and evaluation methods on successful the data driven storytelling.

--Ramalingam Shanmugam, Journal of Statistical Computation and Simulation

This bible of data-driven storytelling covers everything you need to know about the topic, from storytelling techniques (including scrollytelling) and narrative design patterns to evaluation and ethics in storytelling, providing readers with explanations and examples of the concepts described as well as extensive reference material. All of this is expressed in an accurate, concise, and clear way.

--Lorenzo Amabili, University of Groningen, Book Review in Nightingale

Acknowledgment vii
Editors ix
Contributors xi
Chapter 1 Introduction 1(16)
Nathalie Henry Riche
Christophe Hurter
Nicholas Diakopoulos
Sheelagh Carpendale
Research in Data Visualization: From Understanding to Exploration to Data Storytelling
3(1)
Practice in Data Journalism: From Communication to Data Evidence to Data Storytelling
4(1)
Forging New Interdisciplinary Perspectives
5(2)
Notes on Terminology
7(2)
Audience of This Book
9(1)
Book Structure
10(4)
Chapter 2: Storytelling in the Wild: Implications for Data Storytelling
10(1)
Chapter 3: Exploration and Explanation in Data-Driven Storytelling
10(1)
Chapter 4: Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Visual Stories
11(1)
Chapter 5: Narrative Design Patterns for Data-Driven Storytelling
11(1)
Chapter 6: Watches to Augmented Reality: Devices and Gadgets for Data-Driven Storytelling
11(1)
Chapter 7: From Analysis to Communication: Supporting the Lifecycle of a Story
12(1)
Chapter 8: Organizing the Work of Data-Driven Visual Storytelling
12(1)
Chapter 9: Communicating Data to an Audience
13(1)
Chapter 10: Ethics in Data-Driven Visual Storytelling
13(1)
Chapter 11: Evaluating Data-Driven Stories and Storytelling Tools
13(1)
Future Directions
14(1)
References
15(2)
Chapter 2 Storytelling in the Wild Implications for Data Storytelling 17(42)
Barbara Tversky
Preview
19(1)
Perceiving and Understanding Events
20(3)
Spontaneous Retellings of Events
23(1)
Kinds of Discourse
23(3)
Description
26(3)
Explanation
26(1)
Stories
27(1)
Argument
28(1)
Journalism
28(1)
Conversation
28(1)
Graphic Descriptions, Explanations, and Storytelling
29(8)
Ancient Graphics
30(1)
Modern Graphics
31(4)
Contemporary Graphics
35(2)
How Graphics Work
37(3)
Space
37(1)
Marks
38(1)
Meaningful Schematic Marks
39(1)
Inferences from Visualizations
39(1)
Designing Effective Graphic Displays
40(3)
Two General Design Principles
40(1)
How to Find Cognitive Design Principles: The Three P's
41(2)
Looking Forward: Insights from Comix
43(6)
Boxes/Frames
44(1)
Segmenting
45(1)
Connecting: Visual Anaphora
45(1)
Metaphors
46(1)
Visual Jokes
46(1)
Perspective
46(2)
Words, Symbols, and Pictures
48(16)
Simultaneous Parallel Stories
48(1)
Double/Triple Meanings
48(1)
Words
48(1)
A Caveat on Culture and Language
49(1)
Returning to Data
49(1)
Design of the World: Spraction
50(1)
Pulling Things Together
50(1)
Acknowledgments
50(1)
References
51(8)
Chapter 3 Exploration and Explanation in Data-Driven Storytelling 59(26)
Alice Thudt
Jagoda Walny
Theresia Gschwandtner
Jason Dykes
John Stasko
Introduction
60(4)
Characterizing Exploration and Explanation in Visual Data-Driven Stories
64(4)
Characteristics of Exploration
64(2)
Characteristics of Explanation
66(2)
Dimensions of Data-Driven Visual Stories
68(10)
Flexibility
69(4)
Flexibility in the View
69(3)
Flexibility in Choosing the Focus
72(1)
Flexibility in Choosing the Sequence
72(1)
Interpretation
73(4)
Interpretation through the View
74(1)
Interpretation through the Focus
75(1)
Interpretation through the Sequence
75(2)
Summary
77(1)
Benefits of Exploration and Explanation
78(2)
Benefits of Exploration
78(1)
Drawbacks of Exploration
79(1)
Benefits of Explanation
79(1)
Drawbacks of Explanation
79(1)
Summary
80(2)
References
82(3)
Chapter 4 Data-Driven Storytelling Techniques Analysis of a Curated Collection of Visual Stories 85(22)
Charles D. Stolper
Bongshin Lee
Nathalie Henry Riche
John Stasko
Introduction
86(1)
Analysis Method and Process
87(2)
Visualization-Driven Storytelling Techniques
89(9)
Communicating Narrative and Explaining Data
90(2)
Linking Separated Story Elements
92(3)
Enhancing Structure and Navigation
95(2)
Providing Controlled Exploration
97(1)
Discussion and Future Work
98(4)
Composing Multiple Techniques
98(1)
Opportunities for Authoring Tools
99(1)
Controlled Reader Interaction and Experience
100(1)
Smart, Dynamic, Data-Driven Annotations
100(1)
Navigation through Scrolling
101(1)
Effectiveness-Informed Authoring
101(1)
Conclusion
102(1)
References
102(1)
Stories Analyzed
103(4)
Chapter 5 Narrative Design Patterns for Data-Driven Storytelling 107(28)
Benjamin Bach
Moritz Stefaner
Jeremy Boy
Steven Drucker
Lyn Bartram
Joe Wood
Paolo Ciuccarelli
Yuri Engelhardt
Ulrike Koppen
Bayerischer Rundfunk
Barbara Tversky
Introduction
108(3)
Narrative Patterns
111(11)
Patterns for Argumentation
112(3)
Patterns for Flow
115(1)
Patterns for Framing the Narrative
116(1)
Patterns for Empathy and Emotion
117(2)
Patterns for Engagement
119(3)
Use Cases
122(4)
Case 1: U.S. Debt Visualized
122(3)
Case 2: Can You Live on the Minimum Wage?
125(1)
Case 3: What's Really Warming the World?
125(1)
Discussion
126(3)
Using Narrative Patterns
126(1)
Storytelling Techniques
127(1)
Presentation Medium
127(1)
Different Notions of Time
128(1)
Audience and General Intention
129(1)
Conclusions
129(1)
References
130(5)
Chapter 6 Watches to Augmented Reality Devices and Gadgets for Data-Driven Storytelling 135(16)
Bongshin Lee
Tim Dwyer
Dominikus Baur
Xaquin Gonzalez Veira
Introduction
136(1)
Characteristics of Different Devices
137(4)
Examples of Practices by Devices
141(4)
Opportunities and Challenges
145(2)
Conclusion
147(1)
References
147(4)
Chapter 7 From Analysis to Communication: Supporting the Lifecyle of a Story 151(34)
Fanny Chevalier
Melanie Tory
Bongshin Lee
Jarke Van Wijk
Guiseppe Santucci
Marian Dork
Jessical Hullman
Introduction
152(1)
Understanding Current Practices: Interviews with Data Storytellers
153(16)
Methodology
153(1)
Storytelling Process
154(13)
Origin of the Story
167(1)
Roles
167(1)
Storytelling Constraints
168(1)
Tool Landscape
169(8)
Directions for Research and Design
177(4)
Conclusion
181(1)
References
182(3)
Chapter 8 Organizing the Work of Data-Driven Visual Storytelling 185(26)
Christina Elmer
Jonathan Schwabish
Benjamin Wiederkehr
Introduction
186(2)
Design Studios
188(5)
Organizational Structure
189(1)
Skill Sets
190(1)
Tools and Technologies
191(1)
Process and Project Selection
192(1)
Reflections and Lessons Learned
192(1)
Media Organizations
193(5)
Structure and Organization
194(1)
Skill Sets
195(1)
Tools and Technologies
196(1)
Process and Project Selection
196(1)
Reflections and Lessons Learned
197(1)
NGOs and Nonprofits
198(4)
Structure and Organization
199(1)
Skill Sets
200(1)
Tools and Technologies
200(1)
Process and Project Selection
201(1)
Reflections and Lessons Learned
202(1)
Conclusion
202(2)
Appendices
204(1)
Glossary and Definitions
204(1)
Tools
205(2)
Interview Questionnaire
207(1)
Interviewees
208(3)
Chapter 9 Communicating Data to an Audience 211(22)
Steven Drucker
Samuel Huron
Robert Kosara
Jonathan Schwabish
Nicholas Diakopoulos
Introduction
212(1)
What Does the Audience Know?
213(7)
Data and Visualization Literacy: The Annotation Layer
214(2)
Background Knowledge and Expertise
216(3)
Design Expectations
219(1)
What Does the Audience Want?
220(4)
Media Wants and Needs
222(1)
Tailoring to the Audience without Knowing It
223(1)
Directly Engaging the Audience
224(1)
Specific Design Contexts
224(5)
The Reality of the Newsroom
226(1)
Visualization for Television
227(2)
Conclusions
229(1)
References
230(3)
Chapter 10 Ethics in Data-Driven Visual Storytelling 233(16)
Nicholas Diakopoulos
Data Acquisition
236(2)
Provenance
236(1)
Quantification
237(1)
Data Transformation
238(4)
Normalization
238(1)
Aggregation
239(1)
Algorithmic Derivation
240(1)
Filtering
241(1)
Anonymization
241(1)
Conveying and Connecting Insights
242(5)
Visual Mapping and Representation
242(1)
Implied Relationships
243(2)
Context and Annotation
245(1)
Interactivity
246(1)
Summary
247(1)
Acknowledgments
247(1)
References
247(2)
Chapter 11 Evaluating Data-Driven Stories and Storytelling Tools 249(38)
Fereshteh Amini
Matthew Brehmer
Gordon Bolduan
Christina Elmer
Benjamin Wiederkehr
Introduction
252(2)
Outline
252(2)
Goals and Perspectives
254(11)
Author/Storyteller Goals
254(5)
To Be the First to Break a Story
255(1)
To Own a Story or Topic
255(1)
To Communicate, Inform, and Educate
255(1)
To Indoctrinate and to Change Opinion
256(1)
To Persuade to Action or Change Behavior
256(1)
To Facilitate Change in Policy and Governance
257(1)
To Be Validated, Recognized, and Acknowledged by Peers
257(1)
To Appear as Being Aligned with Journalistic Values
258(1)
To Appear as Being Independent from Corporate Interests
258(1)
Publisher Goals
259(3)
To Increase Page Views and Time Spent on Pages
259(1)
To Increase Visibility on Social Media Channels
260(1)
To Make a Business Sustainable
260(1)
To Increase Leadership
261(1)
To Increase Engagement
262(1)
Data-Driven Storytelling Tool/Technique Developer Goals
262(1)
Audience Goals
263(2)
Evaluation Criteria
265(5)
Criteria for Evaluating Data-Driven Stories
265(3)
Comprehension
265(1)
Memorability
266(1)
Engagement
266(1)
Dissemination
266(1)
Increased Knowledge
267(1)
Impact
267(1)
Credibility and Trust
267(1)
Criteria for Evaluating Data-Driven Storytelling Tools
268(2)
Expressiveness
268(1)
Efficiency
268(1)
Usability
269(1)
Learnability
269(1)
Integration
269(1)
Extensibility
269(1)
Collaboration
270(1)
Evaluation Methods
270(5)
Methods for Evaluating Data-Driven Stories
270(3)
Collecting Performance Statistics
271(1)
Recall and Recognition Tests
271(1)
Questionnaires and Interviews
271(1)
Physiological Sensing
272(1)
Methods for Evaluating Data-Driven Storytelling Tools
273(2)
Usability Studies
273(1)
First-Use Studies
274(1)
A/B Testing
274(1)
Case Studies
275(1)
Evaluation Metrics
275(5)
Quantitative Metrics for Stories
275(2)
Page Views
276(1)
Time Spent on a Page
276(1)
Number of Users
276(1)
Number of Likes, Comments, and Replies
276(1)
Number of Shares/Retweets
277(1)
Awards/Recognition
277(1)
Physiological Responses
277(1)
Categorical Metrics for Stories
277(2)
Traffic Sources
278(1)
Devices
278(1)
Outgoing Traffic Destination
279(1)
Qualitative Metrics for Stories
279(1)
Challenges and Constraints
280(1)
Human Resources and Expertise
281(1)
Time/Deadlines
281(1)
Budget
281(1)
External Validity
281(1)
Conclusion
281(1)
References
282(5)
Index 287
Dr. Nathalie Henry Riche is a researcher at Microsoft Research since December 2008. She holds a Ph.D. in computer science from the University of Paris XI and INRIA, France, as well as from the University of Sydney, Australia. Her research focuses on human-computer interaction and information visualization.

Dr. Christophe Hurter is a professor at the Interactive Data Visualization group ( part of the DEVI team) of the French Civil Aviation University (ENAC) in Toulouse, France.

Dr. Nicholas Diakopoulos is an Assistant Professor at the Northwestern University School of Communication where he directs the Computational Journalism Lab

Dr. Sheelagh Carpendale is a professor in the Department of Computer Science at the University of Calgary where she holds a Canada Research Chair in Information Visualization, the NSERC/AITF/SMART Technologies Industrial Research Chair in Interactive Technologies and leads the Innovations in Visualization (InnoVis) research group.