This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users’ decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly.

Visualizing with Text uncovers the rich palette of text elements usable in visualizations from simple labels through to documents. Using a multidisciplinary research effort spanning across fields including visualization, typography, and cartography, it builds a solid foundation for the design space of text in visualization. The book illustrates many new kinds of visualizations, including microtext lines, skim formatting, and typographic sets that solve some of the shortcomings of well-known visualization techniques. Key features: More than 240 illustrations to aid inspiration of new visualizations Eight new approaches to data visualization leveraging text Quick reference guide for visualization with text Builds a solid foundation extending current visualization theory Bridges between visualization, typography, text analytics, and natural language processing The author website, including teaching exercises and interactive demos and code, can be found here. Designers, developers, and academics can use this book as a reference and inspiration for new approaches to visualization in any application that uses text.

Background: The technology of visualization is developed at a fast pace as it is highlydemanded in this big data era. However, current visual analytic tools largely focus on providingvisualization tools to target for specific data types and often overlook the processes and activitiesinvolved in visual analytics. Visual analytics often involve high-level cognitive activities, and theresults of visual analytics can be directly influenced by the cognition of analysts in data analysis.One of important cognitive factors in visual analytics is cognitive bias, which may mislead theanalysis in various ways.Research Questions: How do cognitive bias affect the results of visual analytics?Research Method and Result: The research will use a qualitative method to analyze thesolutions submitted to the 2017 IEEE VAST Challenge. By comparing some solutions with theground truth of the challenge, we identify the gaps between the correct answers and thesubmitted answers and from the perspective of cognitive biases explore the causes of thesedifferences. Our analysis focuses on two common cognitive biases: anchoring bias andavailability bias.Conclusion: Our research confirms the existence of cognitive biases in data analysissupported by visual analytics tools. We also provide some guidance to mitigate the impacts ofcognitive biases in visual analytics. Our work can help analysts understand the potential biasesthey may have in their work, and help the designers of visual analytics systems understand whatcan be done to improve their systems.

A leading data visualization expert explores the negative—and positive—influences that charts have on our perception of truth. We’ve all heard that a picture is worth a thousand words, but what if we don’t understand what we’re looking at? Social media has made charts, infographics, and diagrams ubiquitous—and easier to share than ever. We associate charts with science and reason; the flashy visuals are both appealing and persuasive. Pie charts, maps, bar and line graphs, and scatter plots (to name a few) can better inform us, revealing patterns and trends hidden behind the numbers we encounter in our lives. In short, good charts make us smarter—if we know how to read them. However, they can also lead us astray. Charts lie in a variety of ways—displaying incomplete or inaccurate data, suggesting misleading patterns, and concealing uncertainty—or are frequently misunderstood, such as the confusing cone of uncertainty maps shown on TV every hurricane season. To make matters worse, many of us are ill-equipped to interpret the visuals that politicians, journalists, advertisers, and even our employers present each day, enabling bad actors to easily manipulate them to promote their own agendas. In How Charts Lie, data visualization expert Alberto Cairo teaches us to not only spot the lies in deceptive visuals, but also to take advantage of good ones to understand complex stories. Public conversations are increasingly propelled by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie demystifies an essential new literacy, one that will make us better equipped to navigate our data-driven world.

"This is a book about what the science of perception can tell us about visualization. There is a gold mine of information about how we see to be found in more than a century of work by vision researchers. The purpose of this book is to extract from that large body of research literature those design principles that apply to displaying information effectively"--

Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization for All takes you step-by-step through tutorials, real-world examples, and online resources. This hands-on resource is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others