Better data visualizations : a guide for scholars, researchers, and wonks / Jonathan Schwabish.
Material type:
TextPublication details: New York, New York : Columbia University Press, 2021.Description: xi, 464 pages : illustrations ; 23 cmISBN: - 9780231193115
- 001.4 SCH
| Item type | Current library | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|
| Standard Loan | Moylish Library Main Collection | 001.4 SCH (Browse shelf(Opens below)) | Available | 39002100603951 |
Enhanced descriptions from Syndetics:
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.
Includes bibliographical references and index.
Table of contents provided by Syndetics
- Introduction (p. 1)
- Part 1 Principles of Data Visualization
- 1 Visual processing and perceptual rankings (p. 13)
- Anscombe's Quartet (p. 20)
- Gestalt Principles of Visual Perception (p. 22)
- Preattentive Processing (p. 25)
- 2 Five guidelines for better data visualizations (p. 29)
- Guideline 1 Show the Data (p. 29)
- Guideline 2 Reduce the Clutter (p. 31)
- Guideline 3 Integrate the Graphics and Text (p. 33)
- Guideline 4 Avoid the Spaghetti Chart (p. 41)
- Guideline 5 Start with Gray (p. 43)
- 3 Form and function: let your audience's needs drive your data visualization choices (p. 53)
- Changing How We Interact with Data (p. 61)
- Let's Get Started (p. 62)
- Part 2 Chart Types
- 4 Comparing categories (p. 67)
- Bar Charts (p. 68)
- Paired Bar (p. 84)
- Stacked Bar (p. 87)
- Diverging Bar (p. 92)
- Dot Plot (p. 97)
- Marimekko and Mosaic Charts (p. 102)
- Unit, Isotype, and Waffle Charts (p. 106)
- Heatmap (p. 112)
- Gauge and Bullet Charts (p. 118)
- Bubble Comparison and Nested Bubbles (p. 121)
- Sankey Diagram (p. 126)
- Waterfall Chart (p. 129)
- Conclusion (p. 130)
- 5 Time (p. 133)
- Line Chart (p. 133)
- Circular Like Chart (p. 149)
- Slope Chart (p. 150)
- Sparklines (p. 152)
- Bump Chart (p. 153)
- Cycle Chart (p. 155)
- Area Chart (p. 157)
- Stacked Area Chart (p. 159)
- Streamgraph (p. 162)
- Horizon Chart (p. 164)
- Gantt Chart (p. 166)
- Flow Charts and Timeliness (p. 170)
- Connected Scatterplot (p. 175)
- Conclusion (p. 177)
- 6 Distribution (p. 179)
- Histogram (p. 179)
- Pyramid Chart (p. 185)
- Visualizing Statistical Uncertainty with Charts (p. 187)
- Box-and-Whisker Plot (p. 196)
- Candlestick Chart (p. 199)
- Violin Chart (p. 200)
- Ridgeline Plot (p. 201)
- Visualizing Uncertainty by Showing the Data (p. 204)
- Stem-and-Leaf Plot (p. 214)
- Conclusion (p. 215)
- 7 Geospatial (p. 217)
- Choropleth Map (p. 220)
- Cartogram (p. 233)
- Proportional Symbol and Dot Density Maps (p. 243)
- Flow Map (p. 245)
- Conclusion (p. 248)
- 8 Relationship (p. 249)
- Scatterplot (p. 249)
- Parallel Coordinates Plot (p. 263)
- Radar Charts (p. 267)
- Chord Diagram (p. 269)
- Arc Chart (p. 272)
- Correlation Matrix (p. 275)
- Network Diagrams (p. 277)
- Tree Diagrams (p. 284)
- Conclusion (p. 287)
- 9 Part-to-whole (p. 289)
- Pie Charts (p. 289)
- Treemap (p. 297)
- Sunburst Diagram (p. 299)
- Nightingale Chart (p. 300)
- Voronoi Diagram (p. 304)
- Conclusion (p. 309)
- 10 Qualitative (p. 311)
- Icons (p. 311)
- Word Clouds and Specific Words (p. 312)
- Word Trees (p. 316)
- Specific Words (p. 318)
- Quotes (p. 319)
- Coloring Phrases (p. 321)
- Matrices and Lists (p. 324)
- Conclusion (p. 325)
- 11 Tables (p. 327)
- The Ten Guidelines of Better Tables (p. 329)
- Demonstration: A Basic Data Table Redesign (p. 338)
- Demonstration: A Regression Table Redesign (p. 341)
- Conclusion (p. 344)
- Part 3 Designing and redesigning your visual
- 12 Developing a data visualization style guide (p. 349)
- The Anatomy of a Graph (p. 352)
- Color Palettes (p. 358)
- Defining Fonts for the Style Guide (p. 362)
- Guidance for Specific Graph Types (p. 364)
- Exporting Images (p. 365)
- Accessibility, Diversity, and Inclusion (p. 366)
- Putting it All Together (p. 368)
- 13 Redesigns (p. 369)
- Paired Bar Chart: Acreage for Major Field Crops (p. 369)
- Stacked Bar Chart: Service Delivery (p. 372)
- Line Chart The Social Security Trustees (p. 374)
- Choropleth Map: Alabama Slavery and Senate Elections (p. 378)
- Dot Plot: The National School Lunch Program (p. 380)
- Dot Plot: GDP Growth in the United States (p. 382)
- Line Chart: Net Government Borrowing (p. 385)
- Table: Firm Engagement (p. 387)
- Conclusion (p. 389)
- Conclusion (p. 391)
- Appendix 1 Data visualization tools (p. 397)
- Appendix 2 Further reading and resources (p. 403)
- General Data Visualization Books (p. 403)
- Historical Data Visualization Books (p. 405)
- Books on Data Visualization Tools (p. 405)
- Data Visualization Libraries (p. 406)
- Where to Practice (p. 407)
- Acknowledgments (p. 409)
- References (p. 413)
- Index (p. 431)