Chapter 1 Introduction: Why Data Visualization?
In this book, you’ll learn how to create true and meaningful data visualizations through chapters that blend design principles and step-by-step tutorials, in order to make your information-based analysis and arguments more insightful and compelling. Just as sentences become more persuasive with supporting evidence and source notes, your data-driven writing becomes more powerful when paired with appropriate tables, charts, or maps. Words tell us stories, but visualizations show us data stories by transforming quantitative, relational, or spatial patterns into images. When visualizations are well-designed, they draw our attention to what is most important in the data in ways that would be difficult to communicate through text alone.
Our book features a growing number of free and easy-to-learn digital tools for creating data visualizations. We broadly define this term primarily as charts, which encode data as images, and maps which add a spatial dimension. While tables do not illustrate data in the same way, we include them in this book because of our pragmatic need to navigate new learners through a decision-making process that often results in building one of these three products. Furthermore, in this digital era we define data visualizations as images that can be easily re-used by modifying the underlying information, typically stored in a data file, in contrast to infographics that are generally designed as single-use artwork.1
As educators, we designed Hands-On Data Visualization to introduce key concepts and provide step-by-step tutorials for new learners. You can teach yourself, or use the book to teach others. Also, unlike many technical books that focus solely on one tool, our book guides you on how to choose among over twenty free and easy-to-use visualization tools that we recommend. Finally, while some other books only focus on static visualizations that can be distributed only on paper or PDF documents, we demonstrate how to design interactive tables, charts, and maps, and embed them on the web. Interactive visualizations engage wider audiences on the internet by inviting them to interact with the data, explore patterns that interest them, download files if desired, and easily share your work on social media.
Data visualizations have spread widely across on the internet over the last decade. Today in our web browsers we encounter more digital charts and maps than we previously saw in the print-only past. But rapid growth also raises serious problems. The “information age” now overlaps with the “age of disinformation.” Now that nearly anyone can post online, how do you make wise decisions about whom to trust? When presented with conflicting data stories about divisive policy issues such as social inequality or climate change, which one do you believe? In the next section, we’ll delve into this thorny topic by exploring what types of evidence persuades you, and why. And we’ll share this dirty little secret about data visualization: it illuminates our path in pursuit of the truth, but it also empowers us to deceive and lie.
Note that other data visualization books may use these terms differently. For example, all visualizations are defined as “charts” in Alberto Cairo, How Charts Lie: Getting Smarter About Visual Information (W. W. Norton & Company, 2019), https://www.google.com/books/edition/How_Charts_Lie_Getting_Smarter_about_Vis/qP2KDwAAQBAJ, p. 23.↩︎