Better data visualizations :: a guide for scholars, researchers, and wonks /
"Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Columbia University Press,
[2021]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message"-- |
| Beschreibung: | 1 online resource (xi, 449 pages) : illustrations (chiefly color), maps (chiefly color) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780231550154 0231550154 |
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| 505 | 0 | |a Introduction -- Part One: Principles of data visualization. 1. Visual processing and perceptual rankings -- 2. Five guidelines for better data visualizations -- 3. Form and function : let your audience's needs drive your data visualization choices -- Part Two: Chart types. 4. Comparing categories -- 5. Time -- 6. Distribution -- 7. Geospatial -- 8. Relationship -- 9. Part-to-whole -- 10. Qualitative -- 11. Tables -- Part Three: Designing and redesigning your visual. 12. Developing a data visualization style guide -- 13. Redesigns -- Conclusion -- Appendix 1: Data visualization tools -- Appendix 2: Further reading and resources. | |
| 520 | |a "Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message"-- |c Provided by publisher. | ||
| 545 | 0 | |a Jonathan Schwabish is an economist and writer, teacher, and creator of policy-relevant data visualizations. | |
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| contents | Introduction -- Part One: Principles of data visualization. 1. Visual processing and perceptual rankings -- 2. Five guidelines for better data visualizations -- 3. Form and function : let your audience's needs drive your data visualization choices -- Part Two: Chart types. 4. Comparing categories -- 5. Time -- 6. Distribution -- 7. Geospatial -- 8. Relationship -- 9. Part-to-whole -- 10. Qualitative -- 11. Tables -- Part Three: Designing and redesigning your visual. 12. Developing a data visualization style guide -- 13. Redesigns -- Conclusion -- Appendix 1: Data visualization tools -- Appendix 2: Further reading and resources. |
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| discipline | Allgemeines Informatik |
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| spelling | Schwabish, Jonathan A., author. http://id.loc.gov/authorities/names/n2002090315 Better data visualizations : a guide for scholars, researchers, and wonks / Jonathan Schwabish. New York : Columbia University Press, [2021] ©2021 1 online resource (xi, 449 pages) : illustrations (chiefly color), maps (chiefly color) text txt rdacontent still image sti rdacontent cartographic image crt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Introduction -- Part One: Principles of data visualization. 1. Visual processing and perceptual rankings -- 2. Five guidelines for better data visualizations -- 3. Form and function : let your audience's needs drive your data visualization choices -- Part Two: Chart types. 4. Comparing categories -- 5. Time -- 6. Distribution -- 7. Geospatial -- 8. Relationship -- 9. Part-to-whole -- 10. Qualitative -- 11. Tables -- Part Three: Designing and redesigning your visual. 12. Developing a data visualization style guide -- 13. Redesigns -- Conclusion -- Appendix 1: Data visualization tools -- Appendix 2: Further reading and resources. "Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message"-- Provided by publisher. Jonathan Schwabish is an economist and writer, teacher, and creator of policy-relevant data visualizations. Print version record. Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Visual analytics. http://id.loc.gov/authorities/subjects/sh2007004134 Visualisation de l'information. Analyse visuelle. COMPUTERS / Data Visualization. bisacsh Information visualization fast Visual analytics fast has work: Better data visualizations (Text) https://id.oclc.org/worldcat/entity/E39PCFqMvgDqjrryYTm4QCHYMX https://id.oclc.org/worldcat/ontology/hasWork Print version: Schwabish, Jonathan A. Better data visualizations New York : Columbia University Press, [2021] 9780231193108 (DLC) 2020017814 (OCoLC)1153011198 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2453484 Volltext |
| spellingShingle | Schwabish, Jonathan A. Better data visualizations : a guide for scholars, researchers, and wonks / Introduction -- Part One: Principles of data visualization. 1. Visual processing and perceptual rankings -- 2. Five guidelines for better data visualizations -- 3. Form and function : let your audience's needs drive your data visualization choices -- Part Two: Chart types. 4. Comparing categories -- 5. Time -- 6. Distribution -- 7. Geospatial -- 8. Relationship -- 9. Part-to-whole -- 10. Qualitative -- 11. Tables -- Part Three: Designing and redesigning your visual. 12. Developing a data visualization style guide -- 13. Redesigns -- Conclusion -- Appendix 1: Data visualization tools -- Appendix 2: Further reading and resources. Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Visual analytics. http://id.loc.gov/authorities/subjects/sh2007004134 Visualisation de l'information. Analyse visuelle. COMPUTERS / Data Visualization. bisacsh Information visualization fast Visual analytics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002000243 http://id.loc.gov/authorities/subjects/sh2007004134 |
| title | Better data visualizations : a guide for scholars, researchers, and wonks / |
| title_auth | Better data visualizations : a guide for scholars, researchers, and wonks / |
| title_exact_search | Better data visualizations : a guide for scholars, researchers, and wonks / |
| title_full | Better data visualizations : a guide for scholars, researchers, and wonks / Jonathan Schwabish. |
| title_fullStr | Better data visualizations : a guide for scholars, researchers, and wonks / Jonathan Schwabish. |
| title_full_unstemmed | Better data visualizations : a guide for scholars, researchers, and wonks / Jonathan Schwabish. |
| title_short | Better data visualizations : |
| title_sort | better data visualizations a guide for scholars researchers and wonks |
| title_sub | a guide for scholars, researchers, and wonks / |
| topic | Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Visual analytics. http://id.loc.gov/authorities/subjects/sh2007004134 Visualisation de l'information. Analyse visuelle. COMPUTERS / Data Visualization. bisacsh Information visualization fast Visual analytics fast |
| topic_facet | Information visualization. Visual analytics. Visualisation de l'information. Analyse visuelle. COMPUTERS / Data Visualization. Information visualization Visual analytics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2453484 |
| work_keys_str_mv | AT schwabishjonathana betterdatavisualizationsaguideforscholarsresearchersandwonks |