Information Design for the Common Good: Human-Centric Approaches to Contemporary Design Challenges 9781350117297, 9781350117259, 9781350117266

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Acknowledgments Thank you to the many contributors I spoke with and the talented people who worked with them on their respective projects. The process of writing this book was deeply inspired by the work of these professionals that are shaping the landscape of how meaningful datadriven, human-centric experiences should look and feel. Thank you to the Design Incubation community for their support, encouragement, and guidance in pursuing this “passion project” of mine. Thank you to Aaris Sherin, Dan Wong, Robin Landa, Maggie Taft, and all of the brilliant Design Incubation fellows and Haddon Writing Institute attendees for being some of the most inspiring people I have had the pleasure of being around. Thank you to Pattie Belle Hastings, my faculty colleague, mentor, and dear friend, for her unwavering support and confidence in me from start to finish. Her insight and feedback have been invaluable at every step of this process, and her ability to “talk me down” in moments of panic or doubt is second to none (and deeply appreciated).

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Thank you to the Bloomsbury team for their professionalism and patience in guiding me through my first book publication. Specifically, thank you to Louise Baird-Smith for believing in the idea, my editor Leafy Cummins, James Tupper, and the Bloomsbury production team, Molly Montanaro and the talented team at Lachina, and all the other individuals that helped bring the final product together. Most importantly, thank you to my husband Phil for his patience and willingness to take extra childcare duties for our two young sons, Paxton and Calvin. My goal as a designer and educator is to help make the world better for my children and future generations, and I couldn’t (and wouldn’t) do this without their love and support.

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Introduction This book is for designers and non-designers alike, students and teachers of all disciplines, and professionals looking to improve how they communicate information to stakeholders and the general public. I am not a statistician or a data scientist; I am presenting this book from my perspective as a designer and educator, deeply invested in creating experiences that keep my audience at the center of everything - presentations, workshops, assignments, projects, and other explanations. We have seen the destructive power of misinformation and disinformation, but we also have great potential to illuminate truth and visually explain complexities. There has never been a greater need to nurture the relationship between education, data literacy, information design, and storytelling to impact society positively. Chapter 1 begins by pulling from the evolution of data visualization over history as inspiration for applying and evolving successful practices. Then, the history of social design and empathy is explored in Chapter 2 and used to understand the importance of humanizing data in practice. Data visualization and information design are inherently interdisciplinary, so knowing various design processes (as presented in Chapter 3) and keeping an empathetic mindset helps understand the audience and understand collaborators of varying disciplines.

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Chapter 4 introduces data literacy and methods of honest data presentation in charts, graphs, and maps. Ways of finding and cleaning data sets are also explained alongside a starter list of reliable data sources. Chapters 5 and 6 will feel like familiar territory to designers, looking at how to piece together meaningful and memorable stories while allowing data to drive the narrative. First, we look at narrative structures and goals, followed by understanding and applying helpful visual cues like marks, type, colors, and icon systems. The book concludes with thoughts and techniques on measuring the impact of our designs. Dr. Laura Willis helps present evaluation methods and materials, and common mistakes. In looking to make a positive societal impact with design and data visualization, we must better understand audience comprehension and action. Throughout the book, a variety of case studies help to illustrate and inspire. In each example, I have summarized conversations with the primary designer or part of the team that created the project. The work represents everything from MFA and personal projects to government agencies and scientific publications; and mediums of print, interactive web applications, social media, and multidimensional installations. My goal

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Introduction

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is that you find that each example offers a unique perspective on storytelling with data that make a difference. Let this book be a starting point for your own interdisciplinary collaborations and data explorations. Use your creative power to help others understand, learn, and make meaningful steps towards improving our world for future generations – whether that goal be to reduce carbon emissions, better communicate the spread of global disease, eliminate gender and racial inequities, and many more. Together, let’s better design information that serves a common good.

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The roots of datadriven design

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Information design vs. data visualization

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Information design vs. data visualization The role of graphic design has evolved and broadened so significantly that it is hard to describe what it is that we do. Traditionally, graphic design primarily promotes commerce, but today it is much more than that. As visual communicators, designers must carefully consider the content of a message, efficiency of their delivery, technology used for its implementation and the social impact that it has (Frascara 2006). Effective designs now turn to well-developed principles of information design, social interaction, user experience and semantics to inform how an audience may interpret the visual message (Margolin 2016). The evolving role of graphic design has also been influenced by “the seismic and paradigm shifts of the past decade in technology, in our environment and local cultures, in world economics and global politics that have profoundly affected and challenged both traditional design education and professional design practice. Simply put, change is needed” (Resnick 2016). That change involves evaluating how we view our role as visual communicators in contemporary society, how we collaborate with others to solve complex problems efficiently and accurately and how deeply we empathize with and create for specific audiences from small to global communities. The same seismic shift has also contributed to the

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increasing amount of data available in our world and the growing expectation for immediacy. With seemingly endless information at our fingertips, there is more need than ever to be able to process and make sense of the world’s data quickly and efficiently. Information design has become an essential and in-demand area to which graphic designers can contribute. Whether referred to as information design, information graphics, infographics, data visualization or other similar terms, they all refer to a visual representation of information or data. For our purposes, information design refers to the visual display of non-numerical information, such as a lifecycle diagram or flowchart. Data visualization, as described by Michael Friendly (2007), “is the science of visual representation of ‘data’ defined as information which has been abstracted in some schematic form, including attributes or variables for the units of information.” A few common examples include statistical graphs for financial data and thematic cartography like weather maps. It has become critical today to engage in the process that puts abstract data into forms we can see and understand quickly. For designers, this means an increased need for embracing both quantitative and qualitative research through strategic thinking, systemic thinking

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and critical thinking at a time when designers routinely tackle complex social challenges, wicked problems of great consequence to our future generations (Resnick 2016). Data do not often reveal interesting patterns in its raw form, and we typically collect or at least have access to far more data than we can make sense of. The strength of information design is that compared to text, it offers more immediate visibility to information and offers the ability to visualize complex data so that the user can grasp a message at first glance (Weber 2017). The weakness of information design is that it has the potential to be misleading while being trusted by its audience. Since many forms of information design serve as models and do not mirror concrete objects, they are always subject to the interpretation of their creator. The designer of the graphic is responsible for choosing how the data are represented in terms of line, color, shape, space, form and arrangement, even though the graphic originates in facts and figures (Weber, 2017). These aesthetic choices should draw the viewers’ attention to the substance of the data with minimal distraction and must acknowledge the context of how the graphic will affect the attitudes and behaviors of the audience (Frascara 2006). In his influential work, Semiology of Graphics, Jacques Bertin (1983) defines seven variables for encoding data visually, including position, size, value, texture, color orientation and shape. All of these variables are distinctly visual skills that designers train to address,

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just not necessarily concerning a quantitative context. While designers are skilled in understanding audiences and using aesthetics to speak to those audiences, they also “must be professionally, culturally and socially responsible for the impact their design has on the citizenry”. (Resnick 2016). Thus, it would be irresponsible for designers to assume without proper training that they are statisticians, and likewise irresponsible for statisticians to assume that they have the artistic skills necessary to engage an audience with their data. Instead, information design and data visualization often necessitate an interdisciplinary team to most confidently collect, analyze and display information of value. In an interconnected and immediate world, immediacy requires efficiency and efficiency is the product of a well-assembled team of experts from necessary diverse disciplines. Graphic design’s broad skills applied to the realm of today’s need for information design is just beginning. While the general population often misunderstands graphic design as a purely aesthetic craft, design practices and processes applied to information design and data visualization can be a powerful communicative force. In helping to translate the vast information available today, designers can “change, improve, renew, inspire, involve, disrupt and help solve the ‘wicked’ problems of the world” (Resnick 2016). To best understand the possibilities of information design today, it is helpful to reflect on graphic milestones from the past.

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Historical overview

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Historical overview Statistical graphics and data visualization are often misconstrued as relatively modern developments in the display of quantitative information. However, the rich history spans back to the earliest map making and later thematic cartography, statistics and statistical graphics, medicine and other fields (Friendly, 2007). Over the years, developments in technologies, mathematical theory and practice, and empirical observation and recording allowed for broader usage and new advances in form and content (Friendly 2007). The convergence of these conditions resulted in periods of information inflation driven by new technology responsible for more memory “to ever more durable, compact, and portable” objects such as Mesopotamian writing, Greek libraries and European movable type (Andrews, 2019). At the same time, the costs associated with producing and caring for these objects limited the rate of production. Today, with the latest digital information technology, data have become independent from physical objects, making its physical output less expensive. However, we again exist in a world that has more data than we know how to manage (Andrews 2019). Much of the following historical overview is based on Michael Friendly’s work in creating the Milestone Project. The Milestone Project is arguably the most comprehensive catalog of essential developments representing a wide range of areas and fields in

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data visualization, from medieval to modern times. The project is one of the only accounts that covers the entire development of visual thinking and data visualization while also considering the contributions from a variety of disciplines (Friendly 2007). It is essential to acknowledge the development of information design in all of its uses, with practitioners coming from varying specialized fields, generally unaware of parallel events and history in areas outside of their realm. Friendly notes, “many of the milestone graphics have a story to be told: what motivated this development? What was the communication goal? How does it relate to other developments – what were the precursors? How has this idea been used or re-invented today?” (Friendly 2007) The following historical developments were selected from the Milestone Project to help show the evolution of form, content and purpose in information design. They also demonstrate how information design has long included multiple disciplines and been used to support the pursuit of understanding the world and making it a better place – for the common good. Measurements of people and places While we can trace the roots of visualization back to early geometric diagrams and tables of star positions, the seventeenth century saw significant

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growth in the ability to conduct physical measurements of “time, distance, and space, for astronomy, surveying, map making, navigation, and territorial expansion” (Friendly 2007). Other developments in data visuals were more focused on practical application such as analytical geometry and coordinate systems (Descartes and Fermat), theories of errors of measurement and estimation (used by Galileo in the analysis of observations on Tyco Brahe’s star of 1572), the first use of probability theory and the onset of demographic statistics and “political arithmetic.” Political arithmetic eventually evolved into what we know as government Census data, but beginning in the 1660s focused on population, land taxes and the value of goods. The wealth of the state was understood through the systematic collection of this information (Friendly 2007). In 1686, the first known weather map was created by Edmond Halley, showing trade winds and monsoons on a world map (Figure 1.1). By the end of the seventeenth century, data of significance, sense-making theory, and initial visual representations had surfaced in need of better ways

representing them visually. Initial work on political arithmetic and weather maps made clear that there were interests and need for understanding the environment, the world and people within communities. Fourcroy and Playfair The interest in data combined with the theoretical and visual groundwork laid out by the seventeenth century led to a wave of need-based graphic advances. Map makers began to add new data features in the form of isolines and contours to their geographic maps – a technique to connect equal values and identify patterns. This addition sparked the development of thematic mapping of physical qualities, including geologic, economic and medical data (Friendly 2007). The visual representations are evidence of attempts to share a universal understanding of societal conditions and the world. As data were more widely collected, new visual techniques emerged for the comparison of quantitative information. Charles de Fourcroy (1972) and August F.W. Crome (1785) used geometric figures and cartograms to compare

Figure 1.1 The first known weather map shows global trade winds and monsoons. Edmond Halley, 1686.

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areas of demographic quantities, inspiring today’s tree charts (Figure 1.2). Their work applies principles of scale, shape and space that are common to traditional design practices to compare the areas of European states. One of the most influential people in information design, William Playfair (1759–1823) developed some of the most widely used forms of data graphics during this time. These forms included the line graph, bar chart, pie chart, and circle graph (Friendly 2007). A graph by Playfair from 1821 exemplifies the best of his contributions to these graphic forms (Figure 1.3). The chart included three parallel time series and contained data representing the price of wheat, weekly wages and reigning monarch over an approximate 250-year span from 1565 to 1820 (Friendly 2007). The visual

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evidence argued that working conditions had become better off in recent years. Playfair preferred graphics over tables for their ability to show the shape of data from a comparative perspective (Tufte 2001). The power of design very clearly brought meaning to otherwise dense data, giving an understanding of context and offering a conclusion or data-based takeaway that raw data could not.

Figure 1.2 (right) Tableau Poleometrique compared the demographics of major European cities and inspired the modern day treechart. Charles de Fourcroy, 1782. Figure 1.3 (below) Chart showing the price of wheat compared to weekly wages, from 1565 to 1821. William Playfair, 1822.

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In part because of Playfair’s contributions, data visuals were no longer dependent on direct analogy to the physical world (Tufte 2001). Since any variable quantity could be placed in relation to any other variable amount, data graphics became relevant to all quantitative inquiry. The creativity in visual developments of the eighteenth century was limited by the collection of data, which was still fragmentary and often not publicly available. The limited data availability would soon change in the 1800s. Comprehensive atlases and first disease maps The nineteenth century began with the creation of statistical graphics and thematic mapping at a rate unmatched until modern times (Tufte 2001). Thematic cartography saw mapping evolve from single maps to comprehensive atlases featuring representative data from a wide range of topics, including economic, social, moral, medical and physical information (Friendly 2007). The advances in mapping introduced a variety of forms of symbolism and also saw an analysis of weather, tides and other natural phenomena begin to appear in scientific publications regularly. Later in this chapter, you will see how National Geographic uses mapping and naturally occurring patterns to understand how plastics travel in world waterways. Another early example of using information design for good came from Andre-Michel Guerry’s work on visualizing the moral statistics of France, which established the foundation of

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modern social science in 1833. The series of maps were based on data on literacy, suicides, donations to the poor and other “moral” variables (Friendly, 2007). Guerry (1864) went on to later carry out a comparative visual study of crime in England and France. The visuals helped in making connections apparent and quickly for a broad audience to understand that might not have otherwise explored the numerical data. It also represented a clear link between design and social science to help better understand the world. Another example that shows early power in visualizing data was tracking the spread of cholera in London. The first known disease map of cholera was from Dr. Robert Baker in 1833, which simply showed the districts of Leeds affected by cholera during the particularly severe 1832 outbreak. In his submission to the Leeds Board of Health, based on his map Baker and other medical professionals believed that “the streets in which malignant cholera prevailed most severely were those in which the drainage was most imperfect” (Figure 1.4). However, in 1855, Dr. John Snow made a landmark graphic discovery by creating a dot map that clearly showed the deaths due to cholera clustered around the Broad Street pump in London (Figure 1.5). By using a geographical grid to plot deaths caused by the outbreak and researching each case to determine if the pump water had been accessed, Snow seemingly confirmed that the pump was the source of the epidemic. In addition to those who lived near the pump, Snow tracked hundreds of cases of cholera to nearby schools, restaurants, businesses

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Figure 1.4 A map of Leeds used for the “Report on the Sanitary Conditions of the Labouring Population of Great Britain” studied the correlation between poverty and disease. Rober Baker, 1842.

Figure 1.5 This map shows cases of cholera in London with a cluster pattern around the Broad Street water pump. Dr. Jogn Snow, 1854.

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Case Study: Harry Stevens, Washington Post Background and Purpose: The Washington Post is a top awardwinning American daily newspaper best known for its coverage of politics and government. The 2020 global pandemic COVID-19, like any other widespread sickness, was widely covered by news sources around the world. As the virus led to business closures and social distancing in the United States around the end of March, the focus of the coverage was mostly on case numbers, deaths and the idea of “flattening the curve.” Process: In the spring of 2020, COVID-19 rapidly spread from China to numerous countries around the world. As with any new virus, scientists and doctors worked diligently to understand how to contain the spread best and treat patients. It was evident early on that symptoms vary greatly in those infected from no signs at all to a terrible respiratory infection, fever and possible death. In many of the people infected, symptoms were mild enough to require no medical treatment at all, even if they did feel slightly sick for a period. The variance in symptoms coupled with lack of adequate testing made it impossible to track all cases, and therefore difficult to stop the spread. People carrying the virus could feel wholly healthy but still pass the virus to someone more vulnerable like someone older or immuno-compromised. Because of this, the public needed to understand the importance of social distancing – limiting face-to-face exposure with others. Without social distancing, the virus continued to spread exponentially and put a strain on the healthcare system. It can be assumed that baseline cases were being undercounted, representing

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approximately only one in every five cases. Additionally, it was assumed that everybody who goes to the doctor to get tested was getting tested, which was not at all the case. These factors contributed to the issue of attempting to produce precise representations of numbers that are not precise at all. Even trying to compare the case trajectories of different states or different countries is hard because there are confounding variables like different countries testing methods and availability. Even if you assume you know what the case number is, the forecast into the future is also tricky because there is still so much unknown about the virus, like transmission rates and lethality rates. Also not taken into consideration for forecasting was what different policies the government and state governors might adopt. With so much unknown, Stevens instead treated his graphic as if it were a fake infectious disease within a fake society. In the various models, Stevens crudely represents different policies and different social behaviors just to give people a sense of how the results of different behavioral changes can affect the outcome of what was occurring with COVID. For the visuals, the bouncing balls originated from experimenting with dancing circles in Stevens’ browser about a year before their use in the COVID story. With most of the code already written for making the circles realistically bounce off of each other, he then focused on how to make certain circles represent being sick, not sick or recovered. From there, the visual would start with one ill person, and if it touches another person, they also get infected. As time passes, a time-lapse of infections progress above the simulation so you

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can see how the interaction of the circles mimics the idea of “flattening the curve” while also being interesting to watch. Stevens thought that if he could draw the reader in with something interesting to look at, then that would also make it more engaging, effective and even more personal. Because, for example, you might end up just watching one dot that gets sick and then recovers or when they stop moving, you can recognize that that could be you. If you could stop moving it would make everybody else better. You can start to see yourself in the interaction of the dots. It is not just the data but also has a personal connection. By taking a step away from the uncertainty of case numbers and instead focusing on the cause of the spreading, it helps the audience understand how the virus travels. It also helps them understand why their actions are so necessary if we are to “flatten the curve” and eventually get past COVID-19. What is brilliant about Stevens’ approach is that he created a timeless explanation for how viruses spread. The visuals don’t expire or become dated based on case counts, and since it shows the effect of an individual, it is also very identifiable as a viewer. While Stevens may not have been directly influenced by Snow’s cholera map, visualization techniques as simple as interacting circles are a timeless approach for understanding an issue as complex as infectious disease.

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Figure 1.6 This simulation begins as a town of 200 people (blue dots) and one sick person (orange dot) with everyone starting in a random position and moving at a random angle. Each time the sick person (orange dot) collides with blue dot, that dot also becomes sick (turns orange). In this particular moment, all people in the town have already become sick (as shown in the “change over time” graph), with 193 of them having already recovered. The rapid rise and widespread infection rate is indicative of an environment with no disease mitigation efforts and represents a “free-forall” mentality.

Figure 1.7 To show social distancing, this simulation begins the same as the first. The only change is that one out of every eight people (dots) moves. As shown in the “change over time graph,” this strategy is quite effective in limiting any upward curve of infection. This model represents “extensive distancing.”

Figure 1.8 The Washington Post feature explores four different scenarios for flattening the curve, each with varied results but demonstrate the effectiveness of social distancing for slowing and greatly limiting the spread of COVID-19. The completely randomized nature of the simulations produces slightly different, yet consistent, results each time run.

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and pubs. Snow also investigated groups of people unaffected by the cholera outbreak and traced whether or not they drank pump water. That information was critical in helping Snow rule out other possible sources of the epidemic besides pump water. The more thorough collection and depiction of data done by Snow (perhaps purely by the increased availability of such data) allowed his visual to help society understand patterns surrounding the source of the outbreak and how it spread. The significance of information design in public health and communication is undeniably in contemporary culture. While Dr. Snow effectively used dots to track the spread of cholera, the following case study shows how dots can account for moving people during the global pandemic of COVID-19. Taking a universal approach to how viruses spread explained with simple shapes, graphics editor Harry Stevens created what is now the most viewed story in Washington Post history. The golden age of statistical graphics The mid-1800s had all the components necessary for rapid growth in data visualization, and official state statistical offices were established throughout Europe. These offices acknowledged the growing importance of numerical information for social planning, industrialization, commerce and transportation (Friendly 2007). The unparalleled beauty and graphic innovations defined the period. Michael Friendly notes that these developments

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were so varied that it is difficult to be comprehensive, but a few themes stand out. One theme was graphical innovation, particularly the development of graphical forms for new areas of inquiry, especially the social realm. Minard developed the use of divided circle diagrams as well as flow maps, his most widely admired work being his map following multiple variables of Napoleon’s retreat. The chart seemingly effortlessly integrates six variables: the size of Napoleon’s army, their location on a two-dimensional surface, the direction of the army’s movement, and temperature on various dates during the retreat from Moscow (Figure 1.9). Florence Nightingale also showcased social and political uses of graphics through an innovative polar area chart (also known as a “rose diagram” or “coxcombs”). She used the new graphic form to bring awareness to poor sanitary conditions in battlefield treatment of soldiers and argue for improvements (Figure 1.10). Both of these examples are inspiring for contemporary design because of how accessible they make multiple variables at once – a persistent condition of working with today’s prolific data. Francis Galton contributed nonstatistical graphic innovations, most notably the “anti-cyclonic” (counterclockwise) pattern of winds around low-pressure systems, combined with clockwise rotations of high-pressure areas (Friendly 2007). Galton’s work on weather patterns culminated in 1863’s Meteorographica, containing over 600 total illustrations (Figure 1.11).

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Figure 1.9 This figure describes the successive losses of men in the French Army during Napoleon’s Russian campaign from 1812-1813. As one of the most widely recognized data visuals, the graphic shows six types of data: the number of Napoleon’s troops, distance, temperature, latitude and longitude, dorection of travel, and location relative to specific dates. Edward Tufte and others have called this graphic the best statistical graphic ever drawn. Charles Joseph Minard, 1869.

Figure 1.10 This “rose diagram” was used to explain how nutrition, ventilation, and shelter could help control epidemic disease. At the time, epidemic disease was responsible for more British deaths than Crimean War battle wounds. The graphic is called “Diagram of the Causes of Mortality in the Army of the East” that was also later published in A Contribution to the Sanitary History of the British Army During the Late War with Russia. Florence Nightingale, 1858.

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Figure 1.11 “Meteorographica, or Methods of Mapping the Weather (Referring to the Weather of a Large Part of Europe during the Month of December 1861)” displays temperature, wind, rain, and barometer at morning, afternoon, and evening each day of December 1861. The efforts for recording weather later led to the first weather maps in newspapers. Francis Galton, 1863.

Another theme was the rise of statistical atlases used in most European countries from about 1825 to 1870 (Friendly 2007). These state-sponsored artifacts contained official government statistics on population, trade and commerce, along with social, moral and political issues. Arguably the most beautiful example is the Albums de Statistique Graphique published under the direction of Emile Cheysson by the French ministry of public works from 1879–1897 (Figure 1.12). The albums represent an excellent overview of the graphical methods available at the time and how they applied to demographic content. However, by the end of the 1800s, statistical atlases were primarily discontinued due to high production costs. Instead, as in the case of the US Census, they were sometimes “routinized productions, largely devoid of color and graphic imagination” (Friendly 2007).

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Figure 1.12 During the late 1880’s in France, a set of annual data visualization publications called the Album de Statistique Graphique was released. This particular graphic shows the percentage of various categories of agricultural workers by department in 1882. Inspired by the work of Charles Joseph Minard, the albums were created for the French Ministry of Public Works. Emile Cheysson, 1887.

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Historical overview

The Bauhaus’ effect and universal functionality

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The early to mid-1900s were marked by the undeniable influence of the German design school, the Bauhaus, founded in 1919. The school strived for a functional and rational ideal of design that bridged art and industry. The Industrial Revolution encouraged a machine-friendly aesthetic reduced to essential parts that was embraced by the Bauhaus philosophy. Russian designer El Lissitzky believed in the artist’s ability to be an agent for change, and his work and teachings greatly influenced ideas of visual unity and functional design. He believed that a well-structured page could achieve clarity and understanding in its viewers. While Lissitzky helped influence the Bauhaus, the Bauhaus in turn influenced information design pioneer Ladislav Sutnar.

A key figure directly from the Bauhaus was Herbert Bayer, who studied under Wassily Kandinsky. Kandinsky’s constructivist abstractions appealed to Bayer in application to social problems and realities (Anker 2007). One visitor during his time at the Bauhaus was Otto Neurath who spoke about his “International System of Typographic Picture Education” or “Isotype” for short (more on Isotype in Chapter 6). Neurath’s method of graphic representation become important to Bayer’s work in cartography, where he emphasized a more visual language “to tell the story in the simplest terms” to give the reader “immediate comprehension” (Anker 2007). Bayer utilized simplified graphics to improve the human relationship with the world, and a universal and objective science-based approach defined his work after his final examination at the Bauhaus in 1925.

Sutnar’s work was most often of commercial content in the form of catalogs (including those of F.W. Dodge’s Sweet’s Catalog Service), but his methods of organizing massive amounts of information for his audience is clearly rooted in Bauhaus fundamentals. He did this through strong grid structures and implementing simple typography, iconography and symbols to aid the navigation and comprehension of information. Perhaps lesser known (but of arguably greater societal importance), Sutnar is also the uncredited designer that implemented parentheses around American area codes for Bell Systems. This simple, now information standard allowed for easier access and distinction between normal and emergency services (Heller 1995).

Bayer’s most ambitious project was a five-year creative endeavor to create the World Geo-Graphic Atlas. The completed book was 368 pages, including 120 full-page maps, 1,200 smaller maps and 4,000–5,000 finished drawings (Figure 1.13). Designed to draw attention to conservation of energy and material sources, the final pages of the book are dedicated to a call to action to solve the problem (Anker 2007). Bayer foresaw contemporary environmental concerns including “limited availability of land, the abuse of forests, the restricted reserves of minerals, the problem of social erosion, and the vast abuse of energy” (Anker 2007). Bayer’s use of a modernist graphic language forever changed atlases, which borrow extensively from

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his integration of color, graphics and symbol to better understand the world. Around the time of the Bauhaus’ closing in 1933, Harry Beck developed the London Underground Diagram, which defined and followed an innovative set of new fundamental rules to produce functional design for urban transportation. The map

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was designed with the notion that passengers of the tube system had a primary goal of getting from one station to another, with no pressing need for geographical accuracy. Replacing geographic standards where scale is critical, Beck drew inspiration from electrical schematics with stations that were situated more or less equally

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Figure 1.13 This spread from the World Geo-Graphic Atlas examines global economy and production. The influence of Neurath’s Isotype in the icons and the modern aesthetic of Bauhaus are both evident. The graphics show areas of production for various goods, import and export levels, and how the items were most frequently used. Herbert Bayer, 1953.

in these disciplines for several decades. However, in 1962 John Tukey released the landmark paper, The Future of Data Analytics in the United States. The article called for the recognition of data analysis as a separate branch of statistics distinct from mathematical statistics (Friendly 2007). It also introduced a new set of simple graphic forms, including stem-leaf plots, box plots, hanging rootograms and twoway table displays, among others, under the label of Exploratory Data Analysis (EDA).

spaced. This map structure has since been widely adapted by cities with underground systems around the world. While the influence of the Bauhaus’ designers and others is clear, the 1900s also saw the rise of formal statistics and data for social sciences. Focused on the precision of numbers, visual representation largely took a backseat

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Another important publication was Jacques Bertin’s Semiologie Graphique (1964) in France. The book organizes the visual and perceptual elements of graphics according to the features and relations in data. A final notable development was the ability to perform computer processing of statistical data with the creation of Fortran, the first advanced computing language. Soon after, public university computers were capable of creating old and new graphic forms by computer programs. By the mid to late 1970s, significant collaborations would begin, including further development in computer science, software tools and data analysis. Some of these advances included C language, UNIX, input technologies like pen plotters and digitizer tablets, and modern GIS systems (Friendly 2007). This exciting period set the stage for incredible growth in visualization possibilities.

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Evolution of form During the last quarter of the twentieth century and into the twenty-first century, data visualization has evolved into a thriving, though still developing, multi-disciplinary practice. This practice is primarily due to the full range of visualization methods, available tools and a variety of widely available data sets. While identifying critical developments from the past that have contributed to where we are today is apparent, it is much more difficult to detail the more recent events in information design “because they are so varied, have occurred at an accelerated pace, and across a wider range of disciplines” (Friendly 2007). Some of the most prevalent themes are the development of highly interactive statistical computing systems, new ways to manipulate data directly and increased computer processing speeds and streaming capabilities. With the rise of such heavy-handed technology, we must not lose sight of the fact that the data we are processing with machines correlates with living touchpoints. The case studies in this book represent a wide range of creators, backgrounds, audiences, team structures and visualization techniques. Despite their differences, each of the examples shares the common goal of sharing complex information, educating its audience and trying to make the world a better place.

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While information design and data visualization have statistical implications, today only about 6 per cent of US workers are in science, technology, engineering or mathematics occupations. Information design created for the common good acknowledges that there is a story to be told, born out of the ordinary everyday experiences that we all share (Andrews 2019). Creating data graphics has a magical allure to it in arranging values into visual forms. However, that notion overlooks the fact that all data originate in the physical world, affecting real people in their real, everyday life (Andrews 2019). There is no denying the power of today’s data technology in sharing information. However, the best data stories often result when we acknowledge the origins of the data and how our mind recognizes and interacts with our physical environment (Andrews 2019).

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Learning from the past, comprehending the present and predicting the future are vital themes in many fields with broad applications in business, science, politics and humanities (Tominski 2017). This topic is present in the case studies of this book. The business world has traditionally approached design with caution, “in part because designers tend to stress the creative element of what they do more than the problemsolving business-focused element” (Boag 2017). Another reason for tension between design and business interpretations of data visuals is that design is too often not taken seriously and misunderstood as a purely artistic craft. Design in the business world has meant providing styling for artefacts at the end of a development cycle, which is often the first thing to be sacrificed if time or money becomes a constraint. One of the results of this is the use of poorly designed graphics created by non-designers in business tools like PowerPoint and other templated options. These programs can give a false sense of confidence to the creator that they are making meaningful graphics that communicate clearly. Today and in the foreseeable future, designers are involved from the onset of a project for not only their creative input but their inherently trans-disciplinary skills in research and ability to design for and with the audience (Bennett 2006). Graphic designers have many necessary skills for twenty-first-century communication. We must evolve in our practices and processes to take advantage of new resources such as boundless data to inform our work and help make sense of our world for the common good of all who inhabit it.

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The next case study looks at how multiple forms of information design in National Geographic give a comprehensive explanation of the story of world plastics. The visuals center around a study about Henderson Island, a remote desert island in the Pacific Ocean where plastics from various waterways have washed up. The challenge was to tell the story of the island by explaining what plastics are, how they are made, how they get into the ocean and finally, how they end up piling onto Henderson Island. As many contemporary information designers need to consider, the story is told as a print article (June 2018), a web feature on nationalgeographic.com and via social media channels. Maps, charts, graphs and other information graphics are all thoughtfully integrated to encourage action against plastics.

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Case Study: Jason Treat, National Geographic, World Plastics Background and Purpose: National Geographic is a global general interest publication. They do not make any assumptions about what their audience may know or not know about any given topic and walk through stories to explain things that may or may not be evident to different audience members. Exploring plastics had already been an ongoing topic at National Geographic, and Graphics Editor Jason Treat was asked to create a visual feature story using the information that had been gathered and reported. Treat and his “story team” of text editor, author, photo editor and graphics editor worked together to determine how they wanted to tell the story, what they want people to know and how they needed to distribute the work to gather the necessary components. The purpose of information design at National Geographic is often to fill in content that would otherwise be boring, take too much text to explain or show something that photos could not. In situations where data can be summarized in one quick line, a graphic can quickly engage the audience to illustrate complex content in a broader context. Process: To develop a compelling narrative that translates to print and web formats, Treat’s team considers differences in user behavior between different mediums but the same general-interest audience. For the print version of the story, they know that the first thing their readers do is flip through the pages, look at all the pictures, read some captions, then graphics and headlines, and dive into what interests them. For the web version of the same story, they want to share the same

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complete narrative without the benefit of high-resolution print. It has to be just as exciting and engaging but cannot fit the same amount of information in each graphic as print is inherently able to do with higher resolution. As a result, they need to have a clear idea of narrative sequencing to guide the user through information in the story. About ten specific informational items were identified by the story team to be constructed into a narrative – first for print due to production timeline requirements but also storyboarded for web development. To narrow down the wealth of available information, Treat went to the research experts to ask what the main takeaways of their work were from their perspective. Those experts shared their data that were then processed by Treat’s team and clarified follow-up queries about the information. One set of data was able to be used to create the featured world map of waterways, first in ArcGIS, to focus on geographical features, then styled in Illustrator and Photoshop. For the other charts in the story, Treat mainly used RawGraphs and Illustrator. For the digital version, D3 and Javascript allowed the information to take on a more interactive form. To share still graphics via social media, they reformatted the items to the appropriate dimensions. Stylistically, the intent was to make plastics seem otherworldly and artificial. To make the plastic data points feel out of place, Treat created a subtle glow on a dark background. The unusually dark palette for National Geographic largely stemmed from the need for the neon plastics to stand out more than landmasses to draw focus to the

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movement of plastics through world waterways. With the map being the most critical feature, the colors used in it carry to the other graphics explaining the essential components to the story of plastics in our waterways from start to finish. Although the overall structure is inherently different between the print and digital formats of the story of the plastic, National Geographic does an incredible job of translating the high-resolution gatefold map into a more sequential web feature. The printed form is more exploratory than the web format, with the high resolution showing several data sets simultaneously. The graphic also serves as a practical summary of a detailed feature article that also includes photography and information graphics. There is, however, a defined order that they choose to show the key in, indicative of plastics’ journey: mismanaged plastic waste on land, plastics from large river basins, observed plastic in the open ocean, and missing plastic. The movement through the evident ocean currents is a remarkable evolution of Galton’s work in Meteorographica. While the visual hierarchy invites the viewer to identify patterns and the most concentrated areas for each symbol on the map, it is not until the viewer is acquainted with the information contained in the key that they can piece together a full story. In the web format, the story is told in a more digestible sequential format that addresses a specific objective: “What Happens to the Plastic We Throw Out: How a piece of trash can travel from land to Henderson Island, an uninhabited, remote island in the middle of the South Pacific Ocean.” Aware that users tend to spend less time reading content on screen than they may in magazine format, the text is used much more conservatively to mainly help guide the viewer through the visuals. The sequential order begins with

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showing shocking video footage from Henderson Island before identifying the location on a map. Like the printed piece, it follows plastics from mismanaged waste on land through waterways to the oceans. Given the limited and unpredictable width of browser windows, each world map that shows data can drag left and right to see the whole world, though the default is focused on the Pacific Ocean and Asia since that is where the most plastics are. For the second phase of plastics’ journey, waste in waterways breaks into two graphics – first showing a mapped overview, but then a bubble chart that accommodates the limitations of a lower resolution screen by focusing on the most polluted rivers in the world. By honoring the restraints of the medium and pace of a user interacting with a story, it is helpful to show the blown-up detail of the most polluted rivers for easier-to-identify comparisons. Following the two data visuals, video footage of the trash-filled Pasig River Tributary helps give a realness to more sterile graphics. It offers a memorable visual of how severe the plastic issue is. Finally, a time-lapsed map shows how plastic waste moves when it reaches the ocean. In print form, plastic movement is addressed as a map with directional arrows to show normal tide and current patterns. In contrast, the web version is particularly useful in its ability to show the movement of waste, making it easier to understand how and why Henderson Island has become a mountain of plastic. The story finishes with video footage of a manta ray consuming trash falling from the surface of the water and a call to action linking to take the Planet or Plastic pledge. Ultimately, the narrative of plastics is handled differently across web and print to acknowledge the different behaviors of the users and play to the advantages of each medium. Both effectively communicate

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Figure 1.14 The print spread (with gatefold) of “Drowning in Plastic” invites the viewer to explore in an order of their choosing. While a loose order is suggested working left to right along the bottom, a narrative can be understood no matter where you enter the graphic. Blues on land represent plastic waste, yellow radial symbols indicate river basins, and pinks in the oceans represent plastic. Other components of the spread include how to account for “missing” broken down plastic and small annotations of key stories.

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the main objective, which is to show plastics’ journey from land to Henderson Island – the chosen geographic symbol of the more significant world issue. The story of the proliferation of plastic is complex, yet the way the information is broken down and explained through a consistent visual language makes the topic easily accessible.

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National Geographic is an industry leader because of how well they can connect with their audience. In essence, they do that through not only compelling storytelling that helps people understand complex topics but also because of their ability to humanize data and stories. Balancing out visualized numerical data with high-quality video footage, photos

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and expert text brings their features to life in meaningful, memorable ways. It can be easy to get sucked into the data analysis part of the plastics issue in the world, but reference points that we can all identify with help us understand the story more. Maps, photographs, personal stories and simple icons serve as thoughtful visual cues that bridge the data with the human experience.

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References Andrews, R. J. Info We Trust: How to Inspire the World with Data. Hoboken, NJ: Wiley, 2019. Anker, P. “Graphic Language: Herbert Bayer’s Environmental Design.” Environmental History 12, no. 2 (2007): 254–79. https://doi. org/10.1093/envhis/12.2.254. Bennett, Audrey. “The Rise of Research in Graphic Design.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 14–23. New York: Princeton Architectural Press, 2006. Bertin, Jacques. Semiology of Graphics: Diagrams, Networks, Maps. Redlands, CA: ESRI Press, 2011. Boag, Andrew. “Information Design and Value.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 619–634. London, UK: Routledge, 2017. Frascara, Jorge. “Graphic Design: Fine Art or Social Science?” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 26–35. New York: Princeton Architectural Press, 2006. Friendly, Michael. “A Brief History of Data Visualization.” In Handbook of Computational Statistics: Data Visualization, C. Chen, W. Härdle, A. Unwin (Ed.), Heidelberg: Springer-Verlag, Ch. 1, pp. 1–34, 2007. Harry Stevens (Washington Post) in discussion with the author, April 2020.

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Heller, Steven. “1995 AIGA Medalist: Ladislav Sutnar,” September 9, 1995. https://www. aiga.org/medalist-ladislavsutnar/. Jason Treat (National Geographic) in discussion with the author, June 2019. Margolin, Victor. “Graphic Design Education and the Challenge of Social Transformation.” In Developing Citizen Designers, by Elizabeth Resnick, 14–15. New York, NY: Bloomsbury Academic, 2016. Resnick, Elizabeth. “Introduction.” In Developing Citizen Designers, by Elizabeth Resnick, 185–197. New York, NY: Bloomsbury Academic, 2016. Resnick, Elizabeth. “What Is Design Citizenship?” In Developing Citizen Designers, by Elizabeth Resnick, 12–13. New York, NY: Bloomsbury Academic, 2016. Tominski, Christian, Wolfgand Aigner, Silvia Miksch, and Heidrun Schumann. “Images of Time.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 23–42. London, UK: Routledge, 2017. Tufte, Edward R. The Visual Display of Quantitative Information. Cheshire, CT: Graphic Press, 2001. Weber, Wibke. “Interactive Information Graphics.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 243–256. London, UK: Routledge, 2017.

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Social good and empathy

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Social good Today, an increasing number of designers are eager to lend their skills to projects that align with their values and ethics. There are many terms for this drive – design for social change, design for social impact, design for the greater good and others, all with the clear objective of being more responsible local and global citizens (Shea 2016). Design inherently offers ways of thinking about the world that is significant for addressing many of the problems that humans face in contemporary culture (Buchanan 2006). Designers have the professional and technical skills that can effect meaningful change, including aspects of society, leadership and policy at the local, national and international level. The fact that the interest in solving social problems has grown beyond the boundaries of traditional social sciences and into design is a clear indicator of a broader acknowledgment of the increasingly essential issues experienced by today’s society (Janzer and Weinstein 2016). With their training in the tools of persuasion, designers “can tell the tale of the other side of the ‘underdog,’ of the endangered species” (Ilyin 2016). They can garner attention for those who struggle to get the care that they need and can catalyze social innovation. Social design is not a new discipline but has experienced dramatic growth in recent years in tandem with the growing interconnectedness

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and access to data, people and other readily available information. Graphic designers exhibit their social responsibility by creating work with a concern for the impact all visual communication has on physical and virtual environments, as well as how the content influences people. This showing of social responsibility is especially true when displaying data or sophisticated information design (explored further in Chapter 4). It is, therefore, critical that designers ensure that communications related to the safety and well-being of a community be implemented as accurately and consistently as possible (Frascara 2006). This communication includes knowing exactly what messages you are sending and who you are trying to reach. However, do not assume that all designers have a clear understanding of their ethics. Design educator John Kolko highlighted this at the 2013 LEAP symposium in saying, “while designers are typically well-intentioned, many lack the ethical framework to guide their practice” (Shea 2016). To be able to focus creative skills on social issues, it is essential first to grasp what it is that you believe personally and work on projects of social “good” that are good enough for you to dedicate your time and effort. Those projects might be related to personal experiences, politics, public health or a wide range of causes that motivate the designer to

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explore collaborating with like-minded people or organizations. Andrew Shea describes these partnerships as being focused “on human-centered processes where designers build empathy with the intended audience or around the issue at hand and engage with them to understand their needs, personalities and preferences better” (Shea 2016). The designer and client co-design as partners, with each providing their expert insight into the problem at hand. In Chapter 3, the importance of collaboration in contemporary socially data-driven design is further examined. Whereas expertise in customer persuasion to purchase products has become highly developed, belief must now apply to promote positive social behavior, “such as ethnic and racial tolerance, energy conservation and overall environmental citizenship” (Margolin 2016). Designers cannot achieve social action through datadriven work alone; it requires a diverse set of expertise from community members and disciplinary experts. Promoting behavioral change has become a critical task of the contemporary graphic designer and the design process has had to evolve to accommodate this task.

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Behavioral change requires designers to be more conscious than ever of their connection to and impact on society from research to creation and dissemination. Positive, long-term change means that the audience needs to be involved in the process. Social design “works with people: it does not alienate its audience by claiming moral superiority” (Ilyin 2016). We are at a point individually, and as a professional field, when social design pedagogy has made its way into most design curriculums through a combination of design thinking and social science strategies. However, information design and data visualization are still relatively early in their integration to design curriculum. To develop a pipeline of social designers that contribute to the understanding of complex issues, design education must address the diverse global context that we now work. We must attempt to clarify and sort the multiple communication needs between people of differing cultures, nationalities and languages (Margolin 2016). Design is constantly evolving in terms of its definition, but social design has revealed a new and necessary shift from designing for users to designing with users.

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History of social design Social design has consistently resurfaced in response to historical events in society. Many regard the roots of the social design movement as the 1964 First Things First manifesto by Ken Garland and signed by twenty additional designers and artists. The declaration came in response to the society of 1960s Britain and widespread consumer culture at the time. It encouraged designers to prioritize humanist aspects of design, including education, public services and promoting the betterment of society rather than with “commerce-driven projects” (Garland 1964). Seven years later, Victor Papanek published Design for the Real World, which further (and more aggressively) urged designers to be more proactive in effecting tangible change to make life better for others. Papanek declared advertising as the “phoniest field in existence today” because it is “persuading people to buy things they don’t need, with money they don’t have, to impress people who don’t care” (Shea 2016). The book was translated into twenty-three languages and remains one of the most widely read design books to date. Throughout his career, Papanek used his social design principles with organizations, including UNESCO and the World Health Organization (WHO) (papanek.org). In 2000, the First Things First manifesto was revised and republished to reflect graphic designers’ renewed

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commitment to social responsibility. Thirty-three international designers signed the declaration and released it simultaneously in AdBusters (Canada), Emigre, AIGA Journal of Graphic Design (United States), Eye magazine, Blueprint (Britain) and Items (Netherlands). In it, it argues that “there are pursuits more worthy of our problem-solving skills. Unprecedented environmental, social and cultural crises demand our attention” (First Things First 2000). Substantial technological advances marked the early 2000s with the birth of Google and surge of digital design, then the “flattening” of our increasingly interconnected world, the development of sophisticated phones, tablets and watches and other technology; modern social media platforms and unprecedented speed in accessing information from around the world. The September 11, 2001, terrorist attack on the World Trade Center in New York City only further emphasized a wave of introspective examination in design and culture. A focus on including people in the design process, especially in understanding how to design interactive media, may be what prompted designers to adopt research methods instead of relying primarily on intuition (Bennett 2006). The pace of change and innovation has only intensified since the early 2000s. Along with continued growth in population, there have come increased

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issues of climate change, urbanization, wealth disparities and healthcare, among other topics. Each of these topics comes with a widely available plethora of data yet not always easily understood. The numbers are vast, confusing and layered, and it is easy to manipulate data visuals to seem in favor of any agenda that you want – intentionally or unintentionally misleading audiences of varying scales. The tools needed to manage data and design today are easily accessible but not necessarily used responsibly. Content can be passed through users with little filtering, fact-checking or editorial evaluation (Allcott, Hunt and Gentzkow 2017). The 2016 United States presidential election raised alarming concerns about the ability for false stories, or fake news, to circulate on social media and actually be trusted by its viewers. According to Allcott, Hunt and Gentzkow (2017), the concerns stemmed from the findings that “62 percent of US adults get news on social media; the most popular fake news stories were more widely shared on Facebook than the most popular mainstream news stories; and many people who see fake news stories report that they believe them.” Of course, fake news and misinformation campaigns are not a new phenomenon; we have seen it in various forms of conspiracy theories throughout history. However, the rate of growth in fake news can be attributed to low barriers to entry in media, with easy website creation and easy monetization of content through advertising. As the “social” in social media and social design implies, the designer’s relationship with the audience has evolved to

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be more participatory rather than carefully curating visual information for them (Triggs 2016). With the flood of information and social media usage today, it is not surprising that we see another peak in social design today, especially in application to data visualization. Data visuals are an incredibly efficient way of communicating, much easier to digest and make sense of quickly than longform text-based articles. Data visuals are powerful, and now more than ever with great power comes great responsibility. We must be rooted in personal ethics with an eye towards using our creative power to by socially engaged and help make sense of our complicated world. The following case study exhibits a participatory project in which designers explore their ethics and what social design means to them. The resulting data create unique data portraits of design ethics. Most disciplines that can cause damage to human lives are required to follow a code of ethics, including law, medicine, architecture and engineering. All of these professionals have a regulated and standardized practice, while designers do not. Designers’ ethical conversations should include an in-depth self-reflective process related to the decisions they are making, the audience, the purpose of their visualand the social and environmental impact behind the artifacts they are creating. A designer who is aware of her ethical standards and understands the importance of ethics can make informed decisions about her practice (Londono 2018).

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Empathy Creating compelling data and information design with a specific social agenda means connecting with a particular audience. That connection is made through empathy. Empathy is widely understood as the human ability to understand the mental and emotional lives of others (Lanzoni 2018). Historically, the past one hundred years have shown us that empathy is complicated and conveys both views of “fusion, identity, and similarity as well as

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projection, separation, and difference” (Lanzoni 2018). We need a level of consciousness and self-reflection to be able to identify and experience something through someone else. Empathy pushes us beyond the borders of our comfortable habits to reach out towards another person, thing or environment to engage in experience from another perspective. In turn, we can make more meaningful, informed decisions throughout the creative process.

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Case Study: Juanita Londoño, Impact Over Form Purpose: This project was created as part of a thesis in the Savannah College of Art and Design (SCAD) Graphic Design MFA program. The purpose of the thesis was to explore ways in which the design discipline could push for more ethical practices. The data collected through the process was an opportunity to ignite a dialogue about the discipline’s understanding of what ethics are and help in setting an ethical baseline at the core of design practice. It was essential to define ethics as a community and to include multiple disciplines since that is how designers collaborate today. A workshop was designed to explore beliefs and establish a baseline definition. The artifacts created in the workshops were then analyzed and shared as an exhibit experience. Process: To explore the meaning of design ethics, Londoño collected data from designers as well as practitioners from other disciplines, including artists, engineers and business administrators. Participants explored which areas of ethics they felt were most important to their belief systems through a reflective, interactive process of creating representative charm bracelets. The bracelet activity sparked conversations among the participants about their priorities in ethics. The starting point to define the list of ethical statements was the responses collected through an online survey. Based on the responses, thirty statements became options for the participants to choose from to answer the question, “How do you define or understand ethics in design and being an ethical designer?”

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This list was further consolidated into sixteen statements to reduce redundancy and create more definite statements. The instructions for creating the bracelets were: “I want to invite you to take 30 minutes of your time to create an Ethical Bracelet. You can use any materials and resources that you find at home, from paper and pencils to flowers, cardboard, and magazine cut-outs. Whatever inspires and helps you express what each statement means to you. You will be provided with sixteen statements describing what being an ethical designer means. These statements were selected from the responses of forty different designers. You must pick a minimum of five of them that you feel are most important to your everyday practice to create your bracelet, but feel free to add more if desired. For each of the statements you select, please create a charm or element that identifies it. Once you are done, take a picture of your bracelet and make a sheet explaining which statements each charm represents and why you chose it.” Each bracelet is an extension of the participant and their beliefs surrounding ethics in design practice. The bracelets showcase what is important to each of them through materials, shapes, colors and their selection of statements (Figures 2.1 and 2.2). Through a series of three workshops of varying participants, it was possible to collect a total ofthirty-three bracelets that featured participants from 21 to 75 years old in more than fifteen disciplines and nine different countries.

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Figure 2.1 Instructions and supplies provided by Londoño at one of her workshops.

Figure 2.2 A sample of bracelets created at two separate workshops, showing what participants identified as most significant to them for being an ethical designer.

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Multiple graphics and data visualization tools were studied to start processing the data collected. From all possibilities, the final graph was a chord diagram. Rather than create a single visualization summarizing the data, Londoño felt that the data had to be presented in individual layers to adequately appreciate it as unique belief priorities. The choices made by participants in creating the charm bracelets were then digitally visualized as unique shapes that were screen-printed on translucent sheets (Figure 2.3). Londoño explains that “viewers walked through these data creations for an immersive and interactive experience. The main translucent pieces complemented hung posters that showcased the abstract, some of the bracelets designed by the participants, all the unique shapes compiled, and a comparison of the data sets collected” (Londoño). The final visual outcome consisted of an installation made of thirty-three transparent sheets that featured each of the participant’s shapes representing the charms of their top ethical beliefs. The forms were hung from the ceiling to allow the viewer to walk around them (Figure 2.4). Additional graphics complemented the installation showing the data grouped by categories: gender, age and deeper levels of the ethical statements. For example, to further understand what one of the most popular comments, “designing for good,” meant to the participants, an additional visualization was designed (Figure 2.5). It is clear from this project that designers today should not only be crafters of artifacts, experiences and visuals. More importantly, they should be

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meaning-makers, with purpose, intention and a clear understanding of where their ethics stand. While making bracelets was beneficial to the creators, an additional level of connection and meaning is created when the data is visualized and shown in comparison to the other data sets.

Figure 2.3 Based on the numbered statements that were selected, Londoño created a circular “data portrait” of each participant connected their choices. The handmade screen prints with a color assigned to each person, is a tactile human experience mirroring the bracelet making exercise.

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Figure 2.4 The final exhibit displays the bracelets and the data portraits produced from each and posters with layers of data portraits from participants of a similar demographic. Hanging from the ceiling, the data portraits are screen printed on transparent sheets to allow viewers to be fully immersed in the experience and views of others. Londoño, Juanita. (May, 2019) Impact Over Form: An exploration on how to redeem the Ethical Designer. M.F.A: Graphic Design Visual Experience Thesis at The Savannah College of Art and Design. Savannah, GA.

Figure 2.5 Londoño’s final set of prints are presented as one large group of responses (top left), individually (top right), and by a variety of demographic segments (samples shown in second row).

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History of empathy Susan Lanzoni’s book, Empathy, comprehensively looks at the history and evolution of the term. In it, she examines how social scientists, psychologists, medical practitioners and art theorists over time have looked at imagined, projected and extended versions of the self into others and the world (S8, P2). The diverse, empathetic methods that have touched a diverse range of fields demonstrate that extending the self to take on the experience of others has been practiced for a long time. So much so that by 1936, the psychoanalyst Theodor Reik noted that “the conception of empathy has become so rich in meanings that it is beginning to mean nothing at all” (Lanzoni 2018). Throughout the history of empathy, even before being named as such, it has never really established a “clear, compelling, organizational framework.” The idea of immersing oneself into another’s experience surfaced in the mainstream public after World War II, with explosive growth in the culture of psychological experts. It was in these post-war years when empathy gained widespread respect by offering new possibilities for connection, identification and understanding with promise for improving social relations (Lanzoni 2018). Social scientists committed to community improvement worked on fostering empathy in intercultural

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educational projects to help eliminate prejudice and increase tolerance (Lanzoni 2018). The social psychologist Gordon Allport, who was well-versed in German literature, connected the study of aesthetics, form and personality to an ability to show tolerance toward other ethnic or racial groups (Lanzoni 2018). These social sciences had similar goals as social designers in their pursuit of a better human condition, just through different methods. In the arts, empathy was used as a method of cultivating oneness with a subject or object, developing warmth and enthusiasm and as “a force for projecting one’s consciousness into others” (Lanzoni 2018). The art historian Mark Jarzombek (in Lanzoni 2018) noted that “art pedagogy was one of the principal carriers of the empathy message” in its celebration of authentic seeing. One educator identified empathy as the counterpart of science education, a “sympathetic identification, in imagination, with the unique qualities of the form, by which the content is intuited” (Lanzoni 2018). Empathy could enhance an artist’s understanding of form, which in turn could enrich the knowledge of other people in everyday life (Lanzoni 2018). In this way, we can begin to see a connection to contemporary ethnographic practices and personas common to user-experience design.

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History of empathy

As the Cold War settled in, empathy was an attempt to refine understanding by stepping into a person’s social and cultural world (Lanzoni 2018). The political climate cultivated a persistent fear of “surprise nuclear attack” that encouraged social scientists and psychologists to develop their communication skills, especially empathy (Lanzoni 2018). In 1947, psychotherapist Carl Rogers envisioned that empathetic practices would be widely beneficial to many fields and to society as a whole. He wrote to the dean of social sciences at the University of Chicago that he intended not only to “educate and develop professional leaders in the field of psychotherapy” but to develop knowledge for “significant social usefulness. Time was ‘running out for society with such awful speed,’ given the recent use of the atom bomb, and client-centered skills needed to be applied to political science, education, industry, and religion” (Lanzoni 2018) Social psychologist Kenneth Clark crafted a new definition of empathy as, “the essential aspect of empathy is the capacity of an individual to feel into the needs, the aspirations, the frustrations, the joys, the sorrows, the anxieties, the hurt, indeed the hunger of others as if it were their own” (Lanzoni 2018). He further noted that the powerful in society lacked this ability, or they would not structure and support the kinds of brutal inequalities that exist in society. A tension existed between empathy and the biological survival instinct and egocentric needs. Consequently, Clark

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concluded that empathy was uniquely human, requiring abstract thought from the most recently evolved portion of the brain (anterior frontal lobes) potentially varying levels of understanding from person to person based on differences in structure (Lanzoni 2018). Some of this tension relays back to the First Things First manifesto’s conflict between consumer culture versus societal impact or the intentional sharing of false data versus sharing full context. Cognitive psychologist J.D. Trout uses insights from social policy, economics and neuroscience to point to the presence of an “empathy gap” or an inability to use emotional understanding to grasp others’ lives that are culturally or temporally distant from us. The result of this gap is failures of active social policy – perhaps in part because studies have shown that more powerful figures often feel less empathy for those lacking power (Lanzoni 2018). To be able to show empathy means for one to see the difference between their distinct condition and someone else’s condition and have a willingness to build a bridge between the two. We may first experience empathy as seeing similarities, but we must also recognize differences. Cultural critic Jeremy Rifkin noted in 2009 that empathy is a social value that will remake our businesses, our neighborhoods, even our biosphere (Lanzoni 2018). I would argue that empathy is undoubtedly the catalyst by which humanity can solve some of its most significant problems and discrepancies.

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Lanzoni’s book acknowledges that we don’t fully understand the mechanisms or scope of empathy, but it is very clearly part of our everyday language and interactions with others. Empathy speaks to our relationship with people and things but is also challenging to measure or systematize. Rooted in the arts, it is not surprising that empathy is more often associated with subjectivity than objectivity (Lanzoni 2018). More recently, there has been a surge of interest in emotion and its contribution to rational thoughts among social neuroscientists. Undoubtedly, this will become increasingly important as we evaluate the effectiveness of data visuals and information design in being both understood and actionized. There has also been growing recognition across academia of the importance of emotional understanding for a variety of social, political and economic efforts. For designers, this calls for honing their ability to listen to and interpret the needs of people in other fields and combining the intuitive skills to express concepts visually, along with a rational capacity for analysis and synthesis. To achieve this, designers also need to have enough flexibility of mind and resources to produce visually sophisticated and efficient communications (Frascara 2006). Having an understanding of empathy and the empathetic concern of others is vital in creating effective information design. Cognitive psychologists have studied how we read charts and point out that our prior knowledge and expectations play a crucial role (Cairo 2020). Our reactions to various graphics suggest that our brain stores ideal “mental models” as points of reference

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to which we compare graphics that we see. The influence of family, education, health and social network, as well as access to services, where someone grew up and even sometimes just chance encounters on an individual’s journey all contribute to forming these mental models. Psychologist Stephen Rosslyn came up with a “principle of appropriate knowledge” which, if applied to information design, means that effective communication between a designer and an audience requires a shared understanding of what the chart is about and of the data’s visual representation in the chart. This human visual connection will be explored further in Chapter 5. The emotion of great design causes us to reconsider what the experience of life can be. We need emotions. Pleasing, or at very least interesting experiences, make us curious or feel good, causing us to linger and desire more similar content (Andrews 2019). The aesthetic quality of data visuals and information design is essential, but so is accessibility and usability by the audience. People not only want to look at something interesting, beautiful and appealing; they also want to learn something, complete a task and achieve outcomes (Heller 2018). Good information design is approachable; it must be readable, intelligible and understandable; and it must allow people to act appropriately (Heller 2018). Successful design reflects all levels of the activity – the emotional and rational, the communicative, the technological and the awareness of social context (Frascara 2006). The following case study exhibits all of those characteristics as it tackles varying water issues around the world.

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Case Study: Jan Willem Tulp (TULP interactive), World Water Atlas Purpose: Leading up to World Water Day, the Dutch government wanted to promote stories of people living with varying water conditions, particularly too much, too little or too dirty of water. The World Water Atlas describes how, in the summer of 2016, Prime Minister Rutte of The Kingdom of The Netherlands met with the Sherpas of the UN and World Bank’s High-Level Panel on Water (HLPW). In this meeting, he confirmed the urgency and need for action to understand better and address these complex risks when he said: “The World Water Atlas is directed by the HLPW Action Plan, presented on September 21st, 2016. The Action Plan recognizes the need for ‘a platform where states can share and exchange lessons and good practices for addressing waterrelated disasters and translate them into solutions to promote globally.’” The World Water Atlas is an interactive platform that marks water risk “hotspots” where water challenges and opportunities collide. The Atlas uses narratives from reliable opensource data to help explain and address the complex issues related to water. Process: For this project, Jan Willem Tulp of TULP interactive collaborated with front-end developers from De Voorhoede to create a data visualization prototype for a website that could share stories and data about water from across the globe. Focusing on the storytelling aspect of people living with varying conditions of water, Tulp identified some custom data sets specifically for a globe visualization that would introduce the three themes of water stories – too much, too little and too dirty. At that time, Tulp imagined that the globe he created would be used as an engaging, interesting visual

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on the opening screen to draw viewers into the site to explore further (Figure 2.6). When Tulp shared the globe visual, it was so well-received that they requested making the graphic a navigation tool that could allow the user to click on areas to reveal specific stories. New challenges came with the new purpose for the globe since now it was going to be a fully functional, central rotating component of the website rather than more of a supporting graphic. Questions came up, such as: how will we see more detail? What happens if there are too many markers on the globe? How do we best integrate this into the broader website? To address some of these issues, Tulp generated a grid for the globe using the free tool QGIS. The grid was then exported to use in Tulp’s own custom source code combined with the water data. Each block of the grid has a color value representing to what degree the area experiences water that is too little, too much or too dirty. As you toggle through the three water options, a beautiful animation sweeps the globe to transition to the appropriate color scheme and values (blues, yellows, or purples).

Figure 2.6 Initial globe projections and experimenting.

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Simultaneously, when the user selects a water category, the associated icon is highlighted around the globe, representing a full story focused on that geographic location. The stories slide in from the left and immediately make the data more human by presenting real stories and photos that give life to the data. Allowing users to zoom in and out on (and rotate) the globe itself addressed the issue of having potentially too many markers on the globe. When zoomed out, geographically close stories are grouped with a number on the icon to show how many stories are available from that area. As you zoom in, those stories increasingly separate to the individual level, the most sophisticated example being a grouping

of forty-three stories in Europe – from far away, you simply cannot show them all. Still, as you get closer, they all begin to fall into their respective countries (Figures 2.7 and 2.8). The World Water Atlas is an excellent example of how data visualization, user experience and design can all contribute to a seamless educational experience that everyone can appreciate. The gridded globe alone could have featured the data on the website; Instead, the site resonates with the user through integrating the humanizing element of stories, photos and an area to submit your personal experiences with water. It invites you to be part of the story while also sharing the stories of others.

Figure 2.7 The landing page of the World Water Atlas website features Tulp’s globe with a series categorized data points. The user can filter stories from around the world by theme (too dirty, too much, too little), topic (biodiversity, coastal flooding, wetlands, sanitation, and several other options), or influence.

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Figure 2.8 Sorting by too much (top), too dirty (middle), or too little (bottom) allows the user to read through stories specific to the selected region. A series of icons at the top of each story indicate the topics and influences that are also tied to the area.

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Applied empathy + humanization Design is not just cultural and commercial decoration. It is a practical and powerful discipline that requires responsible action for elevating the values of a country or culture into reality. Design allows us to transform abstract ideas into management, tangible forms (Buchanan 2006). Critical to cultural life, design is how we create all of the artifacts and visual communications that serve human beings, intended to meet their needs and desires, facilitate the exchange of information and deliver education to society (Buchanan 2006). Thoughtful, well-executed design projects can make a difference. We just need adequate data to support them. Data humanist and Pentagram partner Giorgia Lupi (2018) notes that “when working with data, we often focus only on the hard numbers that are readily available to us without realizing they become more meaningful if we use more nuanced and expressive data along with them (The Times).” As the case studies in this chapter have shown, data can and should have a humanness about it – whether it is an analog creation process or a digital process that relates directly to specific human stories and conditions.

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In Koskinen’s book Empathetic Design, design practitioners discuss the role of empathy in design as one that helps move “from rational and practical issues to personal experiences and private contexts” (Kouprie and Sleeswijk Visser 2009). When creating information narratives, not only must designers research and understand the topic, they must also work to understand how the issue affects a broader environment, society and people. Beyond general knowledge, empathy necessitates a relating to others and developing an emotional connection. Empathy is an intuitive ability that varies from person to person based on how well that can “identify with other people’s thoughts and feelings – their motivations, emotional and mental models, values, priorities, preferences, and inner conflicts” (McDonagh 2006). Although developing empathy is unique to individuals and requires a willingness to learn, discussing as a team can enhance and reinforce the connection, leading to potentially more connections and greater understanding.

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Applied empathy + humanization

It is valuable to distinguish between two components of empathy, affective and cognitive, to understand better how exactly it works. Psychologists describe the affective as an immediate emotional response, whereas cognitive is the observer’s understanding of the other person’s feelings. Therefore, to create an empathetic response, one must have an emotional response (affective) to another’s emotional state and reflect on that through perspective taking (cognitive). Several psychological studies (by Stein, Reik and Rogers) generally agree that empathy can be described as phases that involve transitioning into and out of an empathee’s life. Based on this notion, Kouprie and Sleeswijk Visser developed a framework to apply empathy to design practice, considering affective and cognitive reasoning and ability. The following chart is adapted from their framework to explain the proposed four steps of empathy in design (Figure 2.9). Creating something for someone else’s lived experience or needs requires us to be students of their condition and careful listeners to their stories (Heller 2018). While we may not be able to convince anyone of anything, effective design can help them convince themselves. One way of doing this is to create information designs and data visualizations that allow for their exploration, personal discovery and empowerment. Cornell professor Josephine Strode used empathy to help fine-tune interviewing techniques to understand an audience better. She

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described empathy as producing “a strange state of double consciousness: one simultaneously felt the other’s situation as well as one’s own. It was very much like an actor playing a role.” She also advised social workers to develop empathy through observation of people to help reveal underlying emotions. Students would practice these skills by participating in community activities, trying out different jobs and reading history, biography and novels. Empathy forces us to acknowledge our humanness. It is critical to empathize with the audience of any project. Creating a personal bond with community members or organizations is the first step towards a successful partnership and project and requires listening above all else (Benson 2016). Building a relationship through listening should result in the designer becoming a valued new member of that community as well as help put the community interests above the designers. However, designers should not go into a community uninvited or without prior research on the issues and deliver the solutions that they believe to be needed (Benson 2016). Honest and humble discussions are necessary from a variety of community perspectives to create a knowledge base and build trust. For design-based social change to be effective and lost-lasting, it must be rooted in the empowerment of the audience by inviting them to engage, learn and decide for themselves that they want to be part of the change.

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Figure 2.9

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Methods

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Methods There are several methods designers can use to practice empathy while creating information design for social good, as well as several methods that borrow from or collaborate with other social sciences such as anthropology and sociology. There are numerous unique opportunities to make social change, but we must carefully prepare and select the appropriate tools to research users’ situations before designing anything. For designers who are focused on the success of a project, as defined by the users’ benefit, the appropriate methodology will be apparent (Janzer and Weinstein 2016).

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If not used appropriately, any of these tools can take you down the wrong path, much like lying with statistics or using non-representative figures (Pruitt and Grudin 2006). Likewise, it is essential to acknowledge biases and frequently visit and revisit assumptions (Janzer and Weinstein 2016). The following are a few necessary tools that are useful in creating information design projects that benefit large audiences. You may notice that several of them are familiar to user experience design, as information design requires similar interactive qualities and concerns of usability.

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Method

Strengths

Weaknesses

Content

Empathy Mapping

– Presents data gathered from users in a condensed, easily digestible visual form

– Should not replace other research artifacts such as journey mapping or scenarios

– Output is a fictional character that embodies research on a specific audience segment

– Can be inaccurate if not based on reliable data

– Design off of what the audience thinks, does, feels and says

– Is an overall simplistic model that needs to be used as a companion to other tools

– Can also include sections on pain and gain

– Run the risk of not being believable (if not related to data)

– Highest priority audience segments contain user research that includes field studies, focus groups, interviews and comparative analysis

– Can point out gaps in user research, highlighting what further insights need to be explored – Effective tool for communicating key information about the audience to the rest of a team and developing a shared vision – Can even be filled out by users themselves! Use as part of inperson survey or interview. – Cost effective and easy way to organize information Personas Fictional characters based on actual data that depict target user populations (Pruitt and Grudin 2006).

– Provide focus on specific target audiences to establish who is (and consequently who is not) being designed for – Can be used alone to aid design, but can be more potent if used in conjunction with a full range of qualitative and quantitative methods – Can amplify the effectiveness of other methods – Provide a shared basis for communication across all participants in a design project – Easy to explain and justify design decisions when based on personas (S6, P311) – They are generative, quickly allowing for projection into new situations

– Sometimes not communicated well, mainly if printed largescale and hung in an inconvenient place – Sometimes difficult to understand how to use the characters, especially if not designed to speak to all disciplines or all stages of the development cycle – Sometimes little or no high-level support, and therefore depend on more of a grassroots design effort

– Should consist of existing qualitative data, quantitative data or both, as well as observational anecdotes (S6, P317) – Clear links between persona characteristics and supporting data – Specific ages, genders, occupations, hobbies, families and other various demographic and sociological components

– Can be overused, replacing other usercentered methods, data collection or evaluation

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Method

Strengths

Weaknesses

Content

Ethnography A research method based on observing people in their natural environment rather than in a formal research setting.

– Hands-on, on-the-scene learning—and it is relevant wherever people are concerned (Princeton)

– Must be conducted in a locally appropriate manner (strength and weakness)

– Collection of field notes, audio/video recordings, transcripts, participant observation, interviews

– Serves as a way to thoroughly understand the audience and create empathy for their point of view

– Very time-consuming (several hours per person, potentially over time)

– Systematic collection of diverse types of data through observation, conversation and textual study

– Expensive due to time and travel possibly involved

– Study unpredictable situations and relationships that are too complex or difficult for quantitative methods, such as surveys and statistical analysis of numerical data – Provides more realistic picture – Allows real needs versus wants to surface Human-Centered Design A process designed to get you to learn directly from people, open yourself to a breadth of possibilities and then zero in on what is most desirable, feasible and viable for the people you’re designing for (IDEO). The process involves diverging and converging a few times, with each time coming closer to the solution.

– The process believes that all problems, no matter how dire, are solvable by working with the people who face those problems every day – Offers all problem solvers a chance to be part of understanding people and designing for and with a community – Build deep empathy through interviews – Working in teams maintains creativity and energy surrounding a project

– Users may reject an idea as something they would never use, but upon seeing the completed product in person, they may realize that they need it.

– IDEO has an established process of Inspiration, Ideation, Implementation as outlined in the 57 methods of their Field Guide

– Users may come up with an idea of what they think they want, but in reality, they would have no use for it

– Tinker and test, fail early and often and spend a surprising amount of time not knowing the answer to the problem – Succeeds when things are observable, knowable, tangible and measurable

Interviews and Focus Groups

– Can confirm insights collected though other research methodologies

– Moderators bias is hard to prevent or control

– The number of questions should be reasonable (less than 10, if possible)

– Can hear audience responses in person and observe and body cues

– Focus groups can present disproportionate speaking time

– Questions need to be clear to avoid having to discuss the question itself

– Flexibility to dive deeper into issues that come up organically – Uncover ideas and issues that you may not have previously considered – Provides insight on best way to describe certain terms and processes – Can be used to understand surveys at a deeper level

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– May be hard to recruit participants – Participants may be influenced by either the moderator or others in a focus group – Artificial environment

– Do not ask questions that can be answered with yes or no. Why and how will help get more insight – Include probe questions (introductory), follow-up questions (deeper into topic and opinions) and exit questions (check that you didn’t miss anything)

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References Allcott, Hunt and Matthew Gentzkow. “Social Media and Fake News in the 2016 Election.” Journal of Economic Perspectives 31, no. 2 (2017): 211–36. https://doi.org/10.3386/ w23089. Andrews, R. J. Info We Trust: How to Inspire the World with Data. Hoboken, NJ: Wiley, 2019. Bennett, Audrey. “The Rise of Research in Graphic Design.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 14–23. New York: Princeton Architectural Press, 2006. Benson, Eric. “Designing Sustainable and Equitable Relationships with Communities.” In Developing Citizen Designers, by Elizabeth Resnick, 270–271. New York, NY: Bloomsbury Academic, 2016. Buchanan, Richard. “Human Dignity and Human Rights: Thoughts on Principles of Human-Centered Design.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 300-305. New York: Princeton Architectural Press, 2006. Cairo, Alberto. How Charts Lie: Getting Smarter about Visual Information. New York, NY: W W Norton, 2020. “First Things First 2000,” 2000. http:// www.eyemagazine.com/feature/article/ first-things-first-manifesto-2000. Frascara, Jorge. “Graphic Design: Fine Art or Social Science?” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 26–35. New York: Princeton Architectural Press, 2006. Garland, Ken. “First Things First Manifesto,” 1964. http://www.designishistory.com/1960/ first-things-first/. Heller, Steven and Veronique Vienne. Citizen Designer: Perspectives on Design Responsibility (Second Edition). 2nd ed. New York, NY: Skyhorse Publishing Company, Incorporated, 2018. “IDEO Design Thinking.” Accessed September 15, 2020. https://designthinking.ideo.com/.

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Ilyin, Natalia. “What Design Activism is and is Not: A Primer for Students.” In Developing Citizen Designers, by Elizabeth Resnick, 64–65. New York, NY: Bloomsbury Academic, 2016. Jan Willem Tulp (TULP interactive) in discussion with the author, July 2018. Janzer, Cinnamon and Lauren Weinstein. “The Citizen Designer: A Cautionary Note.” In Developing Citizen Designers, by Elizabeth Resnick, 287–289. New York, NY: Bloomsbury Academic, 2016. Juanita Londono (Impact Over Form) in discussion with the author, June 2018. Kouprie, Merlijn and Froukje Sleeswijk Visser. “A Framework for Empathy in Design: Stepping into and out of the User’s Life.” Journal of Engineering Design 20, no. 5 (2009): 437–48. https://doi. org/10.1080/09544820902875033. Lanzoni, Susan. Empathy: a History. New Haven, CT: Yale University Press, 2018. Margolin, Victor. “Graphic Design Education and the Challenge of Social Transformation.” In Developing Citizen Designers, by Elizabeth Resnick, 14–15. New York, NY: Bloomsbury Academic, 2016. NYT Open Team, The. “5 Questions with Giorgia Lupi.” The New York Times Open. The New York Times, November 8, 2018. https://open.nytimes.com/5-questions-withgiorgia-lupi-23e2ba972fd8. Pruitt, John and Jonathan Grudin. “Personas: Practice and Theory.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 311–332. New York: Princeton Architectural Press, 2006. Shea, Andrew. “Anatomy of the Socially Responsible Designer.” In Developing Citizen Designers, by Elizabeth Resnick, 20–21. New York, NY: Bloomsbury Academic, 2016. Triggs, Teal. “Collaborative Learning: The Social in Social Design.” In Developing Citizen Designers, by Elizabeth Resnick, 140–141. New York, NY: Bloomsbury Academic, 2016.

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Collaboration and the process of data-driven design

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The evolution of design processes and data exploration Graphic design is both a rational and an artistic process in its goal to induce action, to educate or to create an experience. At every step of the way, the decision-making process jumps between considering objective information and creative intuition. Every designer has a personal mental library of experiences that they reference subconsciously and are difficult to quantify or rationalize. These references are used to interpret information and ultimately communicate, based on data, perspective, analysis and judgment (Tyler 2006). The rational and artistic decisions involved in creating a successful information design often evolve from following a combination of design processes. In this chapter, we will take a look at some of the influential figures and methods common to the information design process, focusing on those that have helped in developing interdisciplinary practices relevant to working with data and visual design. John Tukey and exploratory data analysis The term “exploratory data analysis” (EDA) was introduced in 1977 by American mathematician John Tukey. EDA was considered a way to use simple graphical and quantitative techniques to encourage open-minded data exploration. Exploratory data

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analysis can help to improve the results of statistical hypotheses, and most of the methods developed by Tukey are graphical (Figures 3.1 and 3.2). Graphics give the information designer the unparalleled opportunity to explore, allowing the data to reveal their underlying insights freely. While we all possess the ability to recognize patterns on a more general level, Tukey says that “the greatest value of a picture is when it forces us to notice what we never expected to see” (Tukey 1977). The graphical techniques of EDA are quite simple, and consist of: 1. Plotting raw data (using methods such as data traces, histograms, probability plots, lag plots, block plots and Louden plots). 2. Plotting simple statistics (through mean plots, standard deviation plots and box plots) 3. Positioning plots to maximize natural pattern-recognition (for example, by using multiple plots per page) Creating data sketches is a fast way of allowing yourself to see the data and let you proceed to react to those sketches to produce deeper meaning (Andrews 2019). Tukey summarized data exploration as a “creative act” wherein “good data sketches deliver answers, and great data sketches inspire more questions” (Andrews 2019).

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Figure 3.1

Figure 3.2

Sketches can also serve as a simple unifying method across disciplines for exploration, as Tukey sees the importance and complementary nature of sciences, mathematics and the arts. Regardless of background, he says that the three things that are judged essential by most are “intellectual content, organization into an understandable form and reliance upon the test of experience as the ultimate

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standard of validity.” And further, that “we need to stress flexibility of attack, willingness to iterate, and willingness to study things as they are, rather than as they, hopefully, should be” (Tukey 1962). The practices presented by mathematician John Tukey were highly influential to the progression of data visualization while also having clear parallels to ideation techniques common to graphic designers.

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Edward Tufte By the mid-1970s, Tukey joined forces with American statistician Edward Tufte to develop joint seminars at Princeton University. The workshops were designed to teach statistics to journalists and became the foundation for Tufte’s landmark book, The Visual Display of Quantitative Information, published in 1982. Widely recognized as a pioneer in data visualization, Tufte’s simplistic approach to telling stories with data made clear the value of communicating visually to a variety of disciplines. He coined the term “chartjunk” about useless, noninformative or information-obscuring elements of information displays. He also introduced the concepts of what he calls the “lie factor,” the data-ink ratio, and the data density of a graphic. Tufte also popularized the technique of sparklines for delivering complex miniature data sets, commonly used in newspapers for sports statistics and tracking the stock market. Mostly focused on journalistic usage of graphics, Tufte argues that “the contempt for graphics and their audience, along with the lack of quantitative skills among illustrators, has deadly consequences for graphical work: over-decorated and simplistic designs, tiny data sets, and big lies” (Tufte 81). He goes on to say that the best graphics require three very different skills: substantive, statistical and artistic. That is to say that the highest potential for effective and meaningful information design occurs with interdisciplinary teams that can expertly represent each of those

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distinct skills. While data analysts have a specific set of expertise, designers have the artistic training to bring that analysis to light. David Sless and an information design process Whereas Tufte was more focused on information design output, by 1994, David Sless and his colleagues at the Australian Communications Research Institute (CRI) argued the importance of process. Sless’ methodology included: defining the problem, involving all stakeholders, observing and measuring the current state, development and testing of prototypes, interactive development and testing of prototypes and implementation and monitoring (Boag 2017). He further suggests that practical information design needs a strong understanding of users and user research, careful craft, specialist teams and measured performance (Sless 1994, 9; Figure 3.3). Even in the mid90s, Sless offered a glimpse of what would become the popular concept of human-centered design. The challenge for information designers is to manage the relationship between design and the user individually when other expertise is also needed in the process. Instead, collaboration should exist among a team of individuals to tackle different aspects of the project. Among Sless’s most significant contributions to developing a creative process is arguably his insistence on measuring improvement through information design. His message was a rallying call: “to be good, information design

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Figure 3.3

requires demonstrable improvement, and therefore measured performance” (Boag 2017). In 1994, having a measure on the original design was relatively novel and is still not done nearly enough for a variety of reasons. While measuring the impact of design inevitably presents challenges, some methods can give a better sense of an information design’s effectiveness, as explored in Chapter 7. Sless argues that a designer’s responsibility extends beyond the design itself by monitoring and managing the work over time (Sless 2007). Design thinking . . . and rethinking The idea of having a formalized process to design progressed from Sless and became popularized by the Stanford d.school and IDEO CEO Tim Brown as “design thinking” in the early 2000s. Brown defines design thinking as “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success”

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(Kimbell 2011). The formal shift toward human-centered design represented a time when a shift from designing objects to designing situations and experiences was necessary for United States culture. You may recall from Chapter 2 that the early 2000s were marked by the revised First Things First manifesto, the rise of the internet and the September 11th terrorist attacks. A wave of recession swept the globe, and by late 2007, the United States was in the midst of a financial crisis that cast big businesses and banks in a negative light. The rise of design thinking was needed in the business sphere to demonstrate a marked focus on humans and rebuild trust. Brown’s 2008 article “Design Thinking” for the Harvard Business Review marked a critical point for business and design (Boag 2017). In it, he stressed that great design solutions had been the result of hard work and a three-phase design process. That process includes Inspiration (observation, reasoning, understanding), Ideation (distilling collected data, prototyping, testing, customer journeys) and Implementation (executing, engineering and measuring

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Figure 3.4

impact) (Boag 2017; Figure 3.4). At the heart of the process leading to successful innovation is a need for the designer to show empathetic concern for matching an audience’s needs with something that is both technically feasible and commercially viable. Without empathy, services cannot do their job and without commercial viability, the business will fail regardless of how popular the product is (Boag 2017). In theory, with a focus on empathy in human-centered design, designers can dismiss any preconceived ideas they may have and keep the audience at the center of their efforts. While design thinking makes sense for things used or consumed by humans, Janzer and Weinstein (2016) argue that social projects are often more complex and nuanced than can be understood through a “human-centered” lens. They claim that although human-centered design can generate transformative social change through creativity and borrowed practices from the social

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sciences, the research is not thorough enough. This could in part be because of the emphasis on “rapid prototyping” and design sprints – the idea that somehow massive problems in our world can be solved within a few concentrated allhands-on-deck days and weeks. Toolkits like IDEO’s Human-Centered Design Toolkit, An Ethnographic Primer by AIGA and the Collective Action Toolkit, to name a few, preach the efficacy of design ethnography – rapid research to gather quick insights (Janzer and Weinstein 2016). The problem with this is that it diminishes the value of accurate ethnographic research needed to produce meaningful results – research that is carefully crafted and works with specific people over a particular time. Without full emersion with an audience and their environmental context, it is challenging to tout design thinking as the best solution within the social realm, at least on its own. As Janzer and Weinstein explain, significant

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knowledge from research science, social theory, developmental history and understanding of the user’s local situation from their perspective are all critical in developing a complete picture (2016). For what it lacks in detailed research, the prominence of design thinking has helped in distinguishing the craft skills of designers from their creative way of approaching problems. By defining steps to a process, other disciplines can more readily understand and appreciate designers’ work and become active complementary participants in the process. UK-based designer and educator Lucy Kimbell further explores this participatory process in her papers on Rethinking Design Thinking (Parts 1 and 2). Kimbell states that sources from anthropology, science and technology studies have entered design research methods and theory, drawing attention to the broader contexts in which designers work and users respond (Kimbell 2012). She also discusses participatory design as an approach focused on processes and procedures where all stakeholders from beginning to end are involved in the design process as co-designers. Involving all stakeholders is increasingly important in contemporary society and is critical to meaningful information design because the content is widely variable and the audience is often broad. Sometimes called “cooperative design” or co-design, the nature of participatory design allows the creation of environments and artifacts that are “appropriate to their citizenry’s cultural, emotional, spiritual, and practical needs” (Kimbell 2012). Designers and users working together through a design process have the best potential for improving quality of life.

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Terry Irwin and transition design To better think about what including all stakeholders in the creative process might look like in catalyzing societal change, we can pull from Transition Design. Introduced by Terry Irwin in 2018, Transition Design is a newer area of design research that argues that design and designers have a crucial leadership role in systems-level changes. To address twenty-first-century issues such as climate change, loss of biodiversity, wealth disparities and water security, we need new approaches to problem solving and change at all levels of society (Irwin 2019). Transition Design acknowledges that we are living in complicated “transitional times” in need of moving towards more sustainable futures. In her 2018 paper “The Emerging Transition Design Approach,” Irwin (2019) suggests the need “to engage all stakeholders affected by the problem to create a shared problem definition and understanding of the oppositions and alignments among them.” To place stakeholders and collaboration at the heart of the process, Transition Design draws on approaches from the social sciences to understand the roots of complex issues. In this context, “stakeholder” refers to anyone with an interest in the topic or is affected by the subject (Irwin 2019). Irwin argues that systems-level problems are severe because they straddle disciplinary boundaries and are hard to define with multiple stakeholders of conflicting agendas. The issues exist at numerous interdependent and interconnected levels. A more holistic approach is needed to address problems that will take dozens of years or longer to resolve. The Transition Design Framework brings together evolving practices that can:

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Visualize and “map” complex problems and their interconnections and interdependencies Situate them within broad, spatiotemporal contexts Identify and bridge stakeholder conflicts and leverage alignments Facilitate stakeholders in the co-creation of visions of desirable futures Identify leverage points in the extensive problem system in which to situate design interventions (Irwin 2019)

These practices occur within three phases of Irwin’s process: Re-Framing the Present and Future, Designing Interventions and Waiting and Observing (Figure 3.5). Rather than a defined process, these phases offer suggestions for the types of action (or inaction) that should be considered when designing for systems-level change (Irwin 2019). The stages highlight the importance of co-designing a shared future, identifying a problem’s root causes at multiple levels of scale to design interventions, and having patience and restraint while observing any changes. In using a design-led approach, Transition Design provides several

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benefits to the creation of useful information design. It enables stakeholders to develop a shared definition of the problem and an understanding of its complexities and interdependencies in an empathic and holistic way. By including all stakeholders, it fosters a collective intelligence that considers all members’ concerns, relations, expectations and beliefs in framing the problem and its potential solutions. Interdisciplinary teams also benefit from a broad palette of tools and methodologies for co-creating a vision for the future, as well as catalyzing social change (Irwin 2019). In information design, how a topic or problem is framed and visualized is critical in shaping how it will be understood and create action. Therefore, the value of a team’s shared vision cannot be understated. In developing this vision together, individuals within the group discover parts of the problem that they were unaware of, challenge any of their preconceived notions and build empathy for one another’s views. Creating data visuals is possible as a solo designer, but the results are not as efficient or thorough as it could be as a team.

Figure 3.5

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Janzer and Weinstein’s social design action matrix Working within (and across) each of the processes mentioned in this chapter are many research tools and practices. One size certainly does not fit all in terms of approach, and data visuals are only one piece of the complex puzzle of addressing social issues. For designers to lead visual communication efforts for societal change, we must be able to identify the most suitable practices for a specific project. These practices are commonly a mix of multiple relevant methods (often borrowed from social sciences) and take into consideration the differences in scope and capability of each technique (Janzer and Weinstein 2019). Choosing to use a mix of practices should also consider the focus of the social design project and the context of use. Cinnamon Janzer and Lauren Weinstein developed a Social Design Action Matrix for evaluating research needs to help “differentiate between various spheres of operation, which can then inform content-specific decisions around relevant and appropriate research method selection” (Janzer and Weinstein 2019). The y-axis of the matrix represents the spectrum of design intervention, ranging from situations to objects, tangible to intangible. The x-axis represents the spectrum of the designer’s solidarity with the community they seek to work with, where inside perspectives mark a high level of trust from the community. The quadrants of the matrix can be summarized as follows: ●

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Quadrant 1: transformative social change; designing a situation with an inside perspective Quadrant 2: traditional humancentered design processes; creating an object with an inside view for an end-user

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Quadrant 3: traditional design; designing an object with an outside perspective

This quadrant aligns with strategic consultant David Berman’s message in Do Good: How Designers Can Change the World. He says “but rather than sharing our cycle of style, consumption, and chemical addictions, designers can use their professional power, persuasive skills, and wisdom to help distribute ideas that the world needs: health information, conflict resolution, tolerance, technology, freedom of the press, freedom of speech, human rights, and democracy”(Berman 2009). ●

Quadrant 4: design neocolonialism; designing a situation with an outside perspective (Janzer and Weinstein 2019)

The purpose of the matrix is to be a guide for consistent implementation approaches (Figure 3.6). Having a shared framework for theory and practice is increasingly important in social design and fruitful collaborations. This chapter has only described a few of the numerous ways of describing the overarching processes in which designers create visuals. Still, they are the ones that are most useful in exploring information design and data visualization. In looking at the processes and methods, a few key themes emerge that can help designers be successful collaborators. Good collaborations have rational processes. The evolution and establishment of a defined process (as variable as it may sometimes be), makes it more comfortable for professionals from other disciplines to work with designers. A designer’s

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Figure 3.6

tools and templates for action offer a recognizably similar but different pattern that is adaptable by others. That familiarity provides the opportunity to build bridges between disciplines and meet in the middle to create a shared knowledge (Zender, Brinkman and Widdice 2017). It can be complicated to jump into interdisciplinary projects as a designer because of the learning curve and language barrier that some topics present. Still, designers are trained to quickly assimilate to new information and essential features to become valued partners. Artifacts like personas, frequent sketching and comps can help in providing a reference point for all collaborators to assure a shared understanding of audience goals. Another characteristic of a good collaboration is holistic thinking. Designers engage as the midpoint between audience and client, involving people from both sides of the equation in the process. They empathize with

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the needs of both parties while helping prioritize the type of information presented and the form it will take (Zender, Brinkman and Widdice 2017). Clients have their own set of professional terminology and technicalities, and designers analyze that content to evaluate what will be of value and understood by the audience. Designers produce interdisciplinary knowledge and are not just visual translators of a client’s expertise (Bennett 2006). Information design, in particular, is inherently interdisciplinary, and one of the most common places designers can lend their skills is in the medical community. The following case study looks at the process and collaboration required for creating the incredible feature, “Katie’s New Face,” for National Geographic. Bryan Christie Design took on the visuals for explaining the complex, traumatic and hopeful story of a successful face transplant and a new chance at life.

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Case Study: Bryan Christie Design, National Geographic Purpose: The feature, “Katie’s New Face” for National Geographic, explains the incredible medical miracle of a successful face transplant. At twenty-one years old, Katie Stubblefield was the youngest person in the United States to receive a face transplant and the fortieth known in the world. Three years prior, feeling bullied at her new school and deeply hurt by a recent breakup, she impulsively attempted suicide by placing a hunting rifle under her chin and pulling the trigger. The 31-hour experimental surgery that she received and her road to recovery is a story of hope and resilience – one that needed to be portrayed visually with human compassion. The goal of Christie’s studio was to treat something tragic and violent in a way that is not about the violence – it is about the transformation.

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Process: Christie used several assets and references from National Geographic Design Editor, Jason Treat, that he acquired from the doctors that had worked with Katie. Among those assets were the digital files of scans done of Katie’s face progressing from the moment she entered the hospital until what she is like now. The designers researched the medical terminology to familiarize themselves with the procedure. Using the hospital scans, Christie was able to use software to digitally isolate different densities, allowing him to highlight flesh, skin, bone and neurology circulation, and from there, export those areas in a format to render in their 3D software (Figure 3.7). Once the scans were broken down into a linear progression that made sense, Christie talked back and forth with Treat (who continued to communicate with the medical staff) to eventually get down to about seven and a half steps in the transplant process. In the early stages, doctors were looking at something aesthetically rough and could miss certain things that they then saw in a different light when the final renders were complete. With this project, the designers first needed to determine the perspective to look at the face in a way that could consistently show all necessary pieces.

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With the face in the right perspective, they roughed in the necessary elements, getting increasingly specific with the three-dimensional aspects of the model. During this process, there was significant back-and-forth between the designers, Treat at National Geographic and the doctors to confirm the accuracy of details. Medical illustrations, and particularly threedimensional ones, have the potential to be very technical, highly saturated and unnatural. Christie’s design studio is known for stripping down color to be minimal and used almost exclusively to indicate information. This distinct style is the result of Christie’s desire to bring elegance to three-dimensional design. He looked to the past of scientific illustrators ranging from Galileo to diagrams in the 1950s and 1960s issues of Scientific American, trying to translate that simple aesthetic into

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three-dimensional work. One of the critical parts of the translation was stripping down the color to focus on the information that color could provide (rather than serving as a realistic translation of the color). Using 3D software, Joe Lertola built various components, including the titanium mesh, reconstructed skull and musculature. Christie’s studio was in charge of the coloration of all of the elements, and since there were so many parts, it took extensive discussion. Shapes and facial structures were sometimes simplified as to focus on specific facts and realities rather than distracting with excessive detail. The sign-off process included confirmation of information accuracy by doctors and aesthetic style verification by National Geographic. Within the three-dimensional software, Christie was able to do most of the coloration,

Figure 3.7 Bryan Christie Design used numerous real scans of Katie’s progress. The timeline (top, left to right) and collection of images representing different procedural layers helped to inform the final design’s strategic color usage and callouts.

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Figure 3.8 The final print spread of “Rebuilding Katie’s New Face” (with gatefold) in National Geographic September 2018 issue.

camera angles and lighting. From there, final color alterations and contrast were addressed in Photoshop. Christie’s task is to direct the reader through the illustrations by using a strong visual hierarchy and streamlining the most critical information as much as possible (Figure 3.8). Christie believes that there is a very fine line between absolute discipline and absolute freedom, especially in the realm of information design, where

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something is often either right or wrong. For an information design to be the most successful and productive, there needs to be a clear system in place. For example, Christie suggests having a clear sense of how many rounds and early pencil stages there are going to be for a project. Knowing this helps plan the project and know what to focus on with the doctors in each round. Having Treat as an intermediary between designers and doctors helped limit and

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filter the number of back-and-forth emails, which was helpful in not squelching any creative momentum and experimentation. What makes “Katie’s New Face” successful is that without needing to read a long scientific article, you can immediately gather and begin to understand the stages of rebuilding her face. Viewers need to work a little to understand but are driven by curiosity and interest since there is a level of beauty even though the subject is

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intense. The simple accuracy of it invites viewers in and allows them to want to know more about the story without feeling overly intimidated. To Christie, his job is to “clear away all the wreckage of excess information and let the truth of what is supposed to be coming out speak.” For this feature, in particular, he was focused on if the piece generates some sort of emotional reaction or feeling rather than traditional metrics.

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Having shared goals Like “Katie’s New Face,” many information design projects have a general goal of creating awareness and explaining a complicated procedure in a way that makes it more accessible. One of the most exciting challenges for designers and their partners is to show how their work affects people, organizations, communities or causes in a measurable way (explored more in Chapter 7) (Shea 2016). As it applies to process and collaboration, it is essential to work with stakeholders from the beginning to define what success would look like for a project. Success can be increased awareness, taking a particular action on a website, specific behavioral change, more revenue or some other project-defined goal (Shea 2016). Based on that shared goal, designers will craft methodologies that help record how conditions changed because of their design. The conditions may include broad categories like economic, social, environmental, cultural, health-related or something else. The designers will also try to account for whether changes occurred as a direct or indirect result of the design (Shea 2016). This type of assessment, although preached at least since Sless in the mid-90s, is too often an afterthought or just not addressed at all. Instead, it should be bookending the creative process at the beginning and end, and even somewhat guiding it. Essential data exist not only in the final artifact that the designer creates but also in the process itself by asking, “How will you know if the social change happened? What would this look like? What impact might the project have on social, economic, environmental, and/or

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political conditions? How long will the change take? What is the magnitude of the change that we can expect? How can you rule out other potential stimuli? How can you capture representational data?” (Buck-Coleman 2016) Design is notoriously tricky to measure, often brushed off as a subjective practice. One way to look at assessment is deciding when in the process to research, who will produce the data and what will they evaluate. The data collection is therefore influenced by the purpose of the evaluation and availability of resources at different stages of the design process (Dyson 2017). Whereas diagnostic testing pinpoints specific problems in the design, heuristic evaluations are typically formative and part of the process. Heuristic evaluations generally are expert opinions that employ principles and guidelines for overall usability. Frameworks analyze design outcomes by applying the same tool or process to a sample artifact repeatedly (Dyson 2017). As design becomes increasingly interdisciplinary and shared widely online, it will become more important to understand methods of assessment and set measurable goals. These goals are not just a measure of the value of design but are a responsibility in assuring information is communicated clearly and effectively. The next case study looks at the Pacific Salmon Explorer created by Periscopic for the Pacific Salmon Foundation in British Columbia, Canada. Through collaboration, a thoughtful design process and a shared vision, the partnership produced a successful webbased resource.

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Case Study: Dino Citraro of Periscopic, Pacific Salmon Explorer Purpose: The Pacific Salmon Foundation in British Columbia, Canada, wanted to be the definitive source of health and habitat data for different salmon populations, delivered in an online format. Process: Citraro says that the aspirational phrase they visit at the start of any project is, “what am I looking at, and why should I care,” making sure to be able to answer both from the audience’s perspective so they’re not confused on either one of those. They first worked with the Pacific Salmon Foundation to understand how different people interact with the vast data that they have available, and how to best visualize that for them. They spent time acclimating to all the different types of data that needed to be communicated and began a conversation about potential ways to visualize the information. While Periscopic relies on the client to be the domain expert, they try their best to become quasi experts to best analyze and explain the content to a more scientific audience. Reliable communication is paramount to a successful collaboration. It is essential to pay attention to detail and make sure that everyone understands what everyone else is saying. It is easy to feel like you know what someone is saying without entirely doing that, and you find weeks later that you went down the wrong path. One way to avoid this is to document things very clearly and get people to acknowledge that yes, that is what we talked about, and we understand the next steps.

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In collaborations, it is also vital to acknowledge that clients are humans, and everyone on the creative side of things is human, so every experience is going to be unique. It can sometimes be hard to be efficient in creating data stories because it is a blend of art and science. You have to recognize that art is messy and emotional, and things evolve in unexpected ways, whereas science is something intended to be reproducible. The audience scope evolved throughout this project. Initially, there were four audience segments: 1) policymakers who decide where development can take place and how clean water should be; 2) river managers and scientists monitoring farmed fish in the waterways; 3) the indigenous First Nations, who live off of the salmon and need a voice in how the salmons’ habitat is treated; and 4) the general public. Needing to communicate to the general public as the lowest common denominator was creating something distilled down to a very generic experience. Acknowledging that the data tools were more focused on analysis than communication allowed the team to eliminate the general public as a primary audience segment and let the presentation take a more scientific and technical turn. There are a lot of data available and you do have to care about it to a certain degree to make sense of it and make the appropriate connections. Since there are so much data available, Citraro and his team decided on a single page with an at-a-glance overview of

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information to allow the audience to make a general assessment of salmon populations. Even with minimal time, a user could scroll through the sections and still gather some insights from the landing page of the website. If the user has time, or the desire to dig deeper, they can further explore specific geographic areas that they are working on or particular populations. The decision to contain a narrative centered on a single overview page was the most accessible option to reach the widest audience, while also not being prohibitive of future additional content. After a few rounds of revisions, the team built the data tool in a modular format to allow for easy addition of new data sets (for example, the skeena region represents a module. Figures 3.9 through 3.12). They then made sure that all of the map tiles were correct and developed a tool for the Pacific Salmon Foundation to easily edit all the text on their own, like a custom Content Management System. Any design to spread information has a marketing component to consider, which

will ultimately contribute to the success of a piece. Marketing or a communications strategy for disseminating the information design itself is often overlooked, or at least an afterthought, maybe partially because of cost considerations. The Pacific Salmon Explorer wanted to measure success by being seen as a data warehouse that was credible and that they could use the data to influence different audiences. That successful outcome was achieved – the tool is now the face of the organization. They do other things, but this is ultimately their conversation starter. As a result of this, they started a partnership with the Canadian government to help inform decisions. With many data tools living in online environments, it is important to have maintenance as part of the process by revisiting components and consider rewriting them to be more friendly with new browser specifications or to improve performance. Online technology changes so quickly that when you have a complex system it needs to be maintained.

Figure 3.9 A visual overview of salmon regions in British Columbia.

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Figure 3.10 This page view gives an overview of pressures on spawning and rearing habitats. The risk of habitat degradation from select individual pressures and cumulative risk posed by all habitat pressures. This particular screenshot shows spawning habitat with “Insect and Disease Defoliation” selected (red is high risk, yellow is moderate, and green is low risk).

Figure 3.11 A number of metrics can be selected to help understand salmon spawning. Those metrics include spawner surveys, smolt surveys, spawner abundance, run timing, catch and run size, recruits-per-spaner, trends in spawner abundance, and biologal status - each with a unique way of being displays (shown on left). This screenshot shows average abundance (red/orange line) and catch and run size (yellow-blue bars).

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Figure 3.12 As an enlarged view of one of the graphs from Fig. 3.11, this shows specifics regarding year, Canadian catch, US catch, exploitation rate, as well as a map of spawning habitat.

Figure 3.13 The page of the website shows the biological status of each Conservation Unit based on stock recruitment (colored square on left) and historic spawners (colored square on right). The colors mean poor (red), fair (yellow), green (good), or grey for no assessment due to data deficiencies. The indicators are intended to guide management decisions alongside other habitat information, ecology, and socioeconomic factors.

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Collaborative opportunity starts early The Pacific Salmon Explorer is a fantastic example of how data and design-driven decisions can be creatively explored. In finding an appropriate balance between information depth and visual cues, information chunks give a clear understanding of a complex ecosystem. Periscopic did not enter the project as experts in salmon, but through interdisciplinary practices they were able to pull from the strengths of multiple entities and facilitate a successful design process. Whereas designers were at one time mediators of visual messages, they are now active partners with their clients and audiences in the creation of those messages. The world is so interconnected that we must work with other discipline experts to create the best possible outcome. Any good collaboration brings together complementary parties who share common aims but different expertise (Zender, Brinkman and Widdice 2017). A willingness to learn from many kinds of experts is paramount to the success of telling stories with data. Much of the best work exhibits communicative teams following agreed principles and rules, and iteration, collaboration and feedback at all stages of the process.

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The following case study examines a project done with my former student, Sophia Alfieri, a Public Relations student that I first met while directing the interdisciplinary communications agency at my institution. Sophia was interested in learning more about data visualization, and I admired her dedication, curiosity and professionalism in her writing and strategy skills. The project is loosely modeled after a 2018 Midterm Election guide for young voters that I had completed a few years prior. Sophia and I sought to research for the 2020 presidential election – a landmark election year that is also the 100th anniversary of the 19th Amendment’s ratification, giving women the right to vote in the United States. This side project was completed during the summer of the 2020 pandemic and represented an exercise in research, collaboration, data visualization and storytelling.

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Case Study: Sophia Alfieri and Courtney Marchese, Fifth Wave “Fifth Wave: Progress and Persistence After 100 Years of Suffrage and Four Waves of Feminism” is a data-driven report detailing the evolution of women’s rights and gender equality since the ratification of the 19th Amendment in August of 1920. With data spanning 100 years, this project reflects on key figures, historical moments and relevant topics regarding women. Through the presented data, gender inequities are evident, but race also exacerbates the imbalance almost across the board. We began this project with an interest in understanding the past and present of women’s issues in society to inform future changes. Using that information, we sought to understand the context of those rights and issues in American culture and a broader global context. While women were the focus, it was essential to look at age, race, geography, education and other demographic data compared to male counterparts of similar demographics. Together, Sophia and I came up with a general list of topics to research before focusing in on the issues that were ultimately selected: voter turnout, education, income, reproductive rights, paid leave, leadership opportunities, equality perception and how to improve women’s rights and gender equality beyond the United States. This content was selected for its ability to be strung into a reasonably cohesive narrative bookended by starting with information about the four waves of feminism and ending with voting as an action item. Digging into the research and meeting once per week, we worked together to identify which information needed to be visual and where text was a more

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suitable option. Ultimately, our information fell into sections, beginning with “fast facts” summarizing key findings followed by notable dates and descriptions of important details on each topic’s past, present and future. The introduction text to each section was followed by data visuals that first look at American gender discrepancies and then look at that data compared to other areas of the world. A different color palette defines each section, along with a topical quote from a famous woman and an icon identifier (Figure 3.14). The use of icons was essential to communicate universal qualities and people free of a specific race, age or other specific demographics. Sophia says that she “learned just how many possibilities there are with data visualization. You can’t present all data in the same way, but there are so many options for how you can best demonstrate your findings.” The issue of “too many options” was one challenge that we worked through together. With so many data points and so many ways to display them, it was necessary to strike a balance between maintaining interest through variety while also having some discipline in limiting the number of graphics used. This discipline meant consistently using line charts to show change over time, stacked bars or tree charts for comparisons across categories, and dot spans to show the difference between two specific values and maps to display geographic patterns (Figure 3.15). To help pace the content, we also integrated an occasional blown up graphic or different form used in particular instances where the information benefited from a different approach. For example,

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Figures 3.14 and 3.15 These images show one of the sections included in Fifth Wave Women in Leadership. Each of the sections in the report include the same general format of fast facts, dates, background, data from the United States, then data showing the United States in a global context.

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Figure 3.16 A spread from Fifth Wave showing an overview of the 2020 Global Gender Gap Index. Alongside each country are five colored rectangles indicating overall score, economic participation, educational attainment, health and survival, and political empowerment.

the spread comparing the Global Gender Gap Index across nations was most appropriately shown with a modified heat map to represent a bird’s eye view of each nation’s score in five different categories (Figure 3.16). For both of us, collaborating across design and public relations allowed us to practice and advance our professional knowledge while also allowing us to teach other our respective skills. This constant exchange of ideas reinforces concepts learned as a student while instilling confidence by having the opportunity to teach someone else how to apply those same skills. Sophia notes that she could practice and enhance her research and writing skills while learning how the written elements and design can complement each other for enhanced communication potential. With an interdisciplinary relationship already established, we

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smoothly transition into tapping Sophia’s public relations and social media expertise to create a short-term online awareness campaign. Sophia says that “working on this project opened my eyes to the inequalities women still face daily. As a young woman just beginning her professional career, I thought I knew and understood all of the issues I could face as a female, when, in reality, my knowledge was scratching the surface. I want to think that if I still had more information to learn about women’s equality today, then so too will our readers.” As a professor, any opportunity to use research and data visuals to create a cohesive narrative inherently fosters growth and learning. It is even more meaningful when a project can help students see the human inequities surrounding us and gain a desire and sense of responsibility to be part of the change for the better.

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The opportunity exists to begin fostering interdisciplinary collaborations in university settings, often bringing together the sciences and liberal arts or multiple design specialties (McDonald 2006). The value of collaboration in education has a lengthy history, with much literature showing that students are capable of performing at higher intellectual levels when working collaboratively instead of individually. Further, the diversity of knowledge and ability to collectively synthesize and evaluate information is improved (McDonald 2006). With the value of an interdisciplinary team and a collaborative design process now established, the next chapter will look at gathering and presenting data that does not mislead.

References Andrews, R. J. Info We Trust: How to Inspire the World with Data. Hoboken, NJ: Wiley, 2019. Bennett, Audrey. “The Rise of Research in Graphic Design.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 14–23. New York: Princeton Architectural Press, 2006. Berman, David B. Do Good: How Designers Can Change the World. Berkeley, CA: AIGA, 2009. Boag, Andrew. “Information Design and Value.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 619–634. London, UK: Routledge, 2017. Bryan Christie (Bryan Christie Design for National Geographic) in discussion with the author, June 2018. Buck-Coleman, Audra. “Assessment Considerations for Social Impact Design.” In Developing Citizen Designers, by Elizabeth Resnick, 284–286. New York, NY: Bloomsbury Academic, 2016. Dyson, Mary C. “Information Design Research Methods.” In Information Design: Research and Practice, edited by Alison Black, Paul

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Luna, Ole Lund, and Sue Walker, 435–449. London, UK: Routledge, 2017. Irwin, Terry. “The Emerging Transition Design Approach.” In Social Design Reader, edited by Elizabeth Resnick, 431-454. London: Bloomsbury Visual Arts, 2019. Janzer, Cinnamon and Lauren Weinstein. “Social Design and Neocolonialism.” In Social Design Reader, edited by Elizabeth Resnick, 361-374. London: Bloomsbury Visual Arts, 2019. Janzer, Cinnamon and Lauren Weinstein. “The Citizen Designer: A Cautionary Note.” In Developing Citizen Designers, by Elizabeth Resnick, 287-289. New York, NY: Bloomsbury Academic, 2016. Kimbell, Lucy. “Rethinking Design Thinking: Part I.” Design and Culture 3, no. 3 (2011): 285–306. https://doi.org/10.2752/1754708 11x13071166525216 Kimbell, Lucy. “Rethinking Design Thinking: Part II.” Design and Culture 4, no. 2 (2012): 129–48. https://doi.org/10.2752/1754708 12x13281948975413 McDonald, Ann. “In Between: Challenging the Role of Graphic Design by Situating It in a Collaborative, Interdisciplinary Class.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 354-369. New York: Princeton Architectural Press, 2006. Shea, Andrew. “Anatomy of the Socially Responsible Designer.” In Developing Citizen Designers, by Elizabeth Resnick, 20–21. New York, NY: Bloomsbury Academic, 2016. Sophia Alfieri (Fifth Wave) in discussion with the author, August 2020. Tukey, John W. “The Future of Data Analysis.” The Annals of Mathematical Statistics 33, no. 1 (1962): 1–67. https://doi.org/10.1214/ aoms/1177704711. Tyler, Ann C. “Shaping Belief: The Role of Audience in Visual Communication.” In Design Studies: Theory and Research in Graphic Design, edited by Audrey Bennett, 36-50. New York: Princeton Architectural Press, 2006. Zender, Mike, William B. Brinkman, and Lea E. Widdice. “Design and Medical Collaboration.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 655–668. London, UK: Routledge, 2017.

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Data literacy

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Data literacy Data literacy, the ability to read, write and communicate data is critical, is now an essential skill across industries today. While reading and writing are at the foundation of solving problems and gaining knowledge, data literacy is not yet widely taught effectively. Too often, data literacy is interpreted as a technical skill for reading charts and making graphs rather than understanding the broader context of application to citizenship and empowerment (D’Ignazio 2017). It is not enough to understand the final graphic output and best practices of creating data visuals. You cannot execute these skills without also having the ability to find, read, assess, work with, analyze and argue with data as a tool for exploring the world. Data literacy skills are now necessary for all fields, including non-technical areas, to minimize the gap between those who can work effectively with data and those who cannot (D’Ignazio 2017). With this growing gap, increased focus as a society on data and varying definitions of what data mean to different professions, it is critical to consider that non-analytical learners need a different pathway to understand the nuances of data than, say, a data scientist. Everyone is a daily consumer of massive amounts of data that can educate, persuade or inform. This reality has immense power to improve our contemporary world if all citizens are trained in how to process and understand the data around them, ideally beginning very early in one’s life like other forms of literacy. Following 2016’s Brexit referendum and the United States presidential election,

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the Oxford Dictionary chose “post-truth” as its Word of the Year. The following year, The Media Literacy Index was created to measure the potential for resilience to post-truth in thirtyfive European countries as a yearly assessment. The rating measures media freedom, education and interpersonal trust, with Finland taking the top spot for 2019 (Open Society Institute Sofia 2019). According to the report, countries in which distrust in scientists and journalists are high result in lower media literacy. Likewise, countries in which their citizens perceive high levels of corruption tend to score lower in media literacy as well. President of the European Research Council, Jean-Pierre Bourguignon, told the World Economic Forum that, “it is the task of our educators and society at large to teach children how to use doubt intelligently and to understand that uncertainty can be quantified and measured” (World Economic Forum, Emma Charlton). Information literacy has become so critical that even the scientists behind the Doomsday Clock, which warns about human-made global catastrophe, added the threat of “the manipulation of facts, fake news, and information overload” to the list of possibilities for destroying the planet (thebulletin.org). Data literacy scholar Catherine D’Ignazio argues that data have become a currency of power. She states, “decisions of public import, ranging from which products to market, to which prisoners to parole and which city buildings to inspect, are increasingly being made by automated systems

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sifting through large amounts of data” (Pasquale 2015, D’Ignazio 6). Much of these data is incredibly useful to society. Still, it requires that we understand best practices of knowing how to collect, find, analyze and communicate what the data show in a way that will help people and communities to the best of our ability. Having a solid foundation in data literacy helps in understanding how to identify the ways information design can mislead and how to avoid misleading others when creating data graphics. There are many ways that information graphics lie to us: by displaying wrong data, including too many or too few data points or by being poorly designed. Even professional graphics can still lie if the information plays to individual mental models and beliefs, allowing the viewer only to see what they want to see (Cairo 2020). Simply put, you cannot design useful data graphics without being able to read and analyze the strengths and weaknesses of other data graphics with a cautious eye first. Human nature dictates that what we see is primarily defined by what we want to see (Cairo 2020). Since the world presents more items to humans at once than can be processed, we rely on mental categories formulated by past individual experiences and established beliefs. Efficiency drives us to search for useful connections for us to understand our environment better, and curiosity pushes us to develop better and connect mental categories that benefit our daily lives. The reliance on mental models inevitably makes our experiences biased, tuning our senses into what our existing concepts find important and ignoring the rest (Andrews 2019). These same personal biases make us more easily misled if a subject appeals

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to our belief systems, making it critical to have good chart-reading practices. Encountering data-driven stories is an opportunity for the viewer to consider and compare that information narrative against their current worldview. No detail of an information graphic can be taken for granted, and the first red flag is unsourced data. Ideally, a graphic should be vetted through directly linked sources, followed by an evaluation of the specific source. The end of this chapter provides a starting point for data sources that are widely agreed upon to have sound data collecting methodology. Beyond these, there are many additional sources available, with a validity that can be hard to verify. When sources are questionable, the overall tone of the message can provide clues. A graphic that speaks in more neutral terms and invites exploration rather than stating definitive views tends to be more honest than a source with ideological terms or aggressive language. Alberto Cairo says that “hyper partisan sources, particularly those you agree with, are similar to candy: A bit of it from time to time is fine and fun. A lot of it regularly is unhealthy” (Cairo 2020) As you would not live on candy alone, it is also encouraged to strive for variety in sources – relying on any one source alone can close you off from other views and possibilities. Cairo also notes that expertise matters, but it is too specific. While an expert in a particular topic may make perfect sense of a data set and methodology, it still needs to be more widely understood by a layperson. Experts can and should be part of a broader interdisciplinary approach to the communication of information in terms that will be most readily understood by the intended audience.

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Best practices of honest data

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Best practices of honest data To give information the best chance at resonating with an audience, it should be accurate, useful and presented as a compelling visual. In turn, the connection made is likely to improve their understanding and knowledge of the topic and better inform future decisions (Cairo 2017). The role of an information designer is to create thorough and accurate representations of essential issues in a way that can enable comprehension and empower the viewers. Honest data visuals bear in mind the widely cited quantitative thinking question of “compared to what”? Doing quality, thorough research, getting information correct and communicating will directly affect the quality of the visual. However, clarity does not imply oversimplification. As Cairo notes, “controlled reduction of complexity, up to a point when reducing matters further would hurt the integrity of the information” is the balance that designers should be striving to meet (Cairo 2016). Inevitably, no information graphic can fully capture the scope of reality and all its nuances. Instead, it can just be made better or worse depending on how well it can balance oversimplifying reality with obscuring data by including too much detail (Cairo 2020). Knowing that we cannot provide all possible data in a graphic story, we must instead carefully construct a context

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and angle for viewing data. It is difficult for numbers alone to mean anything, so one of the best ways to make data more interesting is to provide a comparison that shows contrast for the viewer to consider. Hearing that about 3,900 tigers remain in the wild in 2020 means little without other numbers to compare. To help create context, it helps to know that there were around 40,000 tigers worldwide in 1970, and about 100,000 in 1920 according to the World Wildlife Foundation. Depending on the focus of the story, there may be a comparison to the number of tigers poached, or country-by-country population numbers. Data can also consider the number of cubs born, rehabbed at zoos or any number of other data sets related to population and reasons for the decline. While the evaluation of numbers should always exist in context, it is crucial to be mindful of manipulation tactics, including cherry-picking and lemon-dropping. Cherry-picked data are selected to benefit the agenda of its creator while ignoring less favorable numbers. It will show the most convincing evidence for a particular argument chosen, while lemon-dropping will create compelling evidence by excluding specific numbers that weaken an agenda. Both of these tricks tend to be more intentional than other data manipulation that may occur through inexperience or inconsistency in the data itself.

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Like in the example of the tiger population, the entire world cannot be quantified, so we instead focus on a slice or sample of our world to see what it can tell us. Samples are essential to telling stories with data and also play a big part in science (Andrews 2019). Science progresses by rejecting carefully qualified opinions with new evidence that can also be proven wrong. There is an interest in views that can be proven false in science, and the ranges of numbers are healthy and embraced. This acceptance is because science knows that its claims cannot ever be entirely sure, and so terms like error, confidence, probability and significance are used to describe data. Getting into detailed descriptions of statistical terminology is beyond the purview of this book and best handled by a professional data scientist. Still, there are a few critical areas of focus as they pertain to data literacy and creating information graphics. For one, most statistical terms allude to some degree of uncertainty and the level of this uncertainty needs to be disclosed in the visual representation of the data. As Bret Stephen notes in “Climate of Complete Certainty,” “we live in a world in which data convey authority. But authority has a way of descending to certitude, and certitude begets hubris” (Cairo 2020). Someone responsible with data will acknowledge shortcomings

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rather than pretend that the data are complete and absolute. In statistics, error surrounds estimates, but in this context, “error” is another word for “uncertainty,” not “mistake” as one may assume (Cairo 2020). We sometimes see this error visualized, like the cone of a hurricane project showing the range of possible paths the storm may take. Error is alternatively sometimes noted in a caption or text supporting the graphic, as seen in political polling, where a range appears in a text or numerical form instead of as a visual. Uncertainty is a critical part of any information design that helps to earn trust. R.J. Andrews states that “Too much trust, blind trust, makes you a gullible fool. Too little trust, distrust, destroys all possibilities. The right amount of trust makes all kinds of exchanges better: collaboration, execution, partnerships, relationships and communication” (Andrews 2019). That said, the designer does not get to determine the audience’s level of trust. They must focus on creating graphics that deserve trust by providing consistent accuracy to the best of their ability as well as reliable sources. In R.J. Andrews’s excellent book Info We Trust, he describes critical principles in making trustworthy information design, assuming that the work is already of a high level of quality. These principles include (Andrews 2019):

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Best practices of honest data

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Making a positive first impression, including easily observed signals Is correct and accurate Is accessible, in that it is of an appropriate level for audience comprehension about the topic Is direct Is transparent by disclosing sources Is vulnerable, in that as the creator your name is on it Trusts the audience – that is, compellingly presents the data but also allows the viewer to experience the data and make their sense of it too

Predictions and probability also address uncertainty. Predictions attempt to address the future by using vague expressions like highly unlikely, probably and very likely, among others. While there is not much scientific value to predictions, they do give a sense of how honest and realistic the creator is about their data set. When a prediction uses a precise amount, that value is the result of comparing the number of cases of interest to how many equally likely cases there could be (Andrews 2019). Probability is a numerical prediction,

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referring to a set of possible events rather than a single instance. Like with hurricane paths, humans consider a range of possible scenarios in which only one of those scenarios may or may not occur, depending on evolving data availability. The following case study is an example of a practical application of honest data presentation that also visually integrates predictions and uncertainty. “How Do We Get to Zero Greenhouse Gas Emissions?” was a commissioned piece by David McCandless and Duncan Geere for New Internationalist magazine in support of the publication’s mission to produce “in-depth journalism on human rights, politics and social and environmental justice.” The graphic serves as a response to the October 2018 warning by the Intergovernmental Panel on Climate Change (IPCC) to enact urgent measures to limit global warming within twelve years before the damage to the planet is irreversible. The overall goal was to help people understand where emissions come from what possible measures can get to netzero emissions.

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Case Study: Carbon Footprint, David McCandless Process: The original request for this piece was to get to net-zero by 2030, but in looking at the data and science available, it was quickly evident that no matter the angle, there simply isn’t data or reliable projections to support reaching that goal by 2030. Instead, McCandless and Geere proposed showing how to reduce emissions by 50 percent by 2030 as a realistic and well-supported possibility, and 2050 as an aspirational goal for zero emissions. As a way to show the difference in certainty between today, 2030 and 2050 data, the graphic needed three sections of representation. Precise data exist to show current emissions based on where they are used (as opposed to where they are produced) in gigatons of CO2e per year. CO2e (carbon dioxide equivalent) is a unit for measuring the impact of multiple greenhouse gases. With current emissions as a jumping-off point, there are enough data and research to support specific ways that we can make half the total emissions by 2030, shown in the middle graphic categorized again by where the emissions are used. Since no particular data exist to support net-zero by 2050, there are no numbers listed at all because it would be potentially misleading. However, several promising things can likely get us to net-zero, so they are listed as conceivable policies to further reduce the projections from 2030.

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McCandless used Geere’s prior graphics for an Exponential Climate Action Roadmap as a reference, acknowledging that the audience for the Roadmap was more scientific. In contrast, the New Internationalist needed to appeal to a more general audience. One concern of the general audience was that they couldn’t understand how to reduce carbon emissions if they don’t know all the different places that they are coming from in the first place. An early draft uses a Sankey diagram to break down the sources of emissions but it becomes too complicated to look at and isn’t able to successfully show the three different dates (2018, 2030, 2050). From here, the directional lines are removed in favor of tall, proportionately stacked bars with color integration to help optically sort the content. While this general arrangement is significantly easier to look at, it produces awkward wasted space that McCandless knew wouldn’t be best served with additional information to overwhelm the page. With the flow not working visually, he focused on how to show the emissions today in a different form, eventually connecting with the idea of showing a TreeMap for today and strategically tying in color to help lead the viewer to 2030 and 2050. The final design explains the story quickly, uses the space efficiently and translates to being reformatted for social media well.

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Figure 4.1 An early draft of the graphic drew inspiration from a sankey diagram format with colors indicating sources of emissions – energy (light green), industry (green), buildings (light orange), agriculture (blue), and transport (orange).

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Figure 4.2 Another early draft further explores the idea of categories, how they change over time, and grapple with how to work with unknown future projections of 2050.

Figure 4.3 With space distribution and layout continuing to be an issue, the chart took on a new format integrating a treechart of known yearly emissions, what it would take to half those emissions by 2030 (with approximate values), and potential policy changes that could hypothetically get emissions to zero by 2050 (no values because there is no concrete scientific support).

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Figure 4.4 With a solid format established, colors and number sizes were adjusted to optimize contrast, readability, and general aesthetic interest.

Figures 4.5 and 4.6 Based on the printed graphic, a two-part Instagram post was created by reformatting to squares.

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Rhetoric and persuasion

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Rhetoric and persuasion “How Do We Get to Zero Greenhouse Gas Emissions?” like most other information graphics created by McCandless and the Information is Beautiful team does an exceptional job of being focused on a specific relevant question. Their questions manage to appeal to a broad audience while maintaining as neutral of a tone as possible. At the first Information Design conference in 1984, Robin Kinross examined if the presentation of information can be neutral. His paper “The Rhetoric of Neutrality,” printed in Design Issues, argues that the roots and values of information design trace back to the ideological principles of modernism (1985). Kinross says that the information design movement was concerned primarily with discovering what is considered adequate graphic and typographic communication (Kinross 1985). Defining rhetoric as “the art of using language to persuade or influence others,” a distinction is often seen between design for information and design for persuasion. Using the example of timetables, Kinross argues that “as soon as the move from concept to visual manifestation is made, and especially to a manifestation as highly organized as a timetable, then the means used become rhetorical” (Kinross 1985). That is to say, ALL design has some degree of rhetorical undertone. Tracing information design to modernism, we can see how the modernist’s

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concern with information – instructive knowledge – is something that is still present today. From the simplicity of forms, Swiss typography flourished with its style reflective of technical advance, precision and neutrality (Kinross 1985). In the 1950s, Bauhaus traditions continued through the HfG Ulm, and some of its members grasped onto an interest in communication or information theory, cybernetics and related areas (Kinross 1985). The HfG Ulm, Kinross argues, was a response to making things seem free of rhetoric even if nothing can ever fully be free of rhetoric. All visual representations have a rhetorical function in that they demonstrate, persuade and facilitate communication. Small narratives connect to individual data points in the graphic, “giving human context to the data and rooting the abstract representation in personal experience” (Offenhuber 2010). Since it is impossible to show all data at one time when designers create information design, they have to choose one of many possible stories to tell. The visual (and therefore, the narrative) can be immediately changed by focusing on a different set of the data. Dork, Feng, Collins and Carbondale note that “similar to a photograph’s relationship to reality, visualizations do not capture reality as found in data but rather present a particular angle on it” (2013). Thus, any single angle presented in a

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graphic alone cannot be neutral since it, to some degree, decontextualizes by overlooking the complexity of varying perspectives. Designers can strive for greater neutrality and a complete data story by presenting multiple angles as a series of static graphics or providing interactivity. Ideally, the varying perspectives will challenge one another to better understand the reality of what the data can teach us rather than only focusing on the parts that we want to see. Even with best efforts and intentions to show as complete of a story as possible, providing data citations and notes helps train the viewer to understand which data are used and where potential shortcomings may exist. Understanding the notion that all information design is at some level rhetorical, we are better equipped to embrace the subjective nature of data visuals and make them more meaningful (Campbell and Offenhuber 2019). Numbers alone can be numbing and diminish the importance of the data it represents – people, animals, sea levels, ecosystems, disease, education and infinite other datasets that have a real human impact. As we explore ways to integrate a more human perspective on data, we inevitably rope in emotion and how that emotion becomes persuasive. While it is widely agreed upon that visualizations can distort and mislead, the traditional view states that visual language should avoid appealing to emotion (Campbell and Offenhuber 2019). This “traditional”

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view likely comes from data application in business, finance and weather charts as a means to show a sterile view of numbers from a very distinct point of view devoid of any genuine connection or alternative views as to why humans should care. For example, we are accustomed to seeing the weather forecast and patterns through the lens of one-week segments, generally free of any more significant trend or consequences. Through this lens, the data are relatively neutral – it is whatever the weather models say it is going to be and serves a very distinct short-term purpose. However, if we widen our lens or focus on different angles of the data like the overall pattern of rising temperatures or rain patterns, the resulting information design is now rhetorical and maybe even emotional. What are the different local and global consequences of warmer temperatures? Are there data that represent the positive and negative effects of those warmer temperatures? How does that inevitably affect humans and other elements of our delicately interlaced global environment? Rather than striving for “neutrality” with no emotional connection, designers should instead aim to create accessible multifaceted graphics that invite individual interpretation and exploration. While traditional approaches emphasize the rationality of visualization, it is somewhat hollow: “without affect, information lacks meaning and won’t be used in judgment and decision making” (Slovic 2007).

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Rhetoric and persuasion

Information design will only be persuasive, intentionally or not, if it makes an emotional connection. To form that connection, philosopher George Campbell identified seven factors that appeal to emotion: probability, plausibility, importance, the proximity of time, association of place, relations to the persons concerned and interest in the consequences (1776). While persuasion is sometimes still seen as a tainted goal associated with consumption, exaggerations and lies, Aristotle describes the system of rhetoric through three modes of persuasion appealing to logic, ethics and emotion (Roberts, Bywater and Solmsen 1954). Those three modes are logos (appeal to the logic of the

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message), ethos (appeal to the character and credibility of the speaker) and pathos (appeal to the emotion of the audience). Commonly referred to as the rhetorical triangle, these three modes of persuasion increase the strength of an argument (Campbell and Offenhuber 2019). We can see how these modes of persuasion come into play through a study done by Pandey et al. (2004) that revealed several reasons why people do or do not change their opinion after seeing a data graphic. Table 4.1 summarizes those reasons. With the understanding that information design is inevitably rhetorical and potentially persuasive, designers must use this awareness to help avoid misleading data presentation.

Table 4.1 Reasons people do not change opinion

Reasons people do change their opinion

– Skepticism (data/source): lack of trust towards the presented data, statistics and trends – Skepticism (logic): when individuals question the logic rather than the data themselves. Typically people saw the topic as being overly simplified. – Anchoring in core beliefs: persuasive message may go against what the reviewer already strongly believes to be true – Complexity: some found the message to be too complex, or did not have enough background knowledge to form an opinion – Persuaded already: if someone was already persuaded, it is difficult to increase that level of persuasion

– Struck by evidence: when viewers do change opinion it is often because of the data presented – More persuaded: exposure to more evidence reinforces prior notions – Negative thought (boomerang effect): people could feel that the dataset is either incomplete or makes them less certain than they were before

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Avoiding misleading data There are a variety of ways that data and their presentation can be misleading or just outright incorrect. From the level of overall handling, bias may be revealed through unintentional distortions of data, whereas intentional misuse or misrepresentation of data classifies as deception (Bateman et al. 2010). Disinformation is not a new phenomenon but rather one that has been seen repeatedly throughout history, mostly as a military and political tactic. The rise of global democracy offered more widespread and systematic disinformation efforts that could pander to a population’s biases and fear and ultimately influence the way that they would vote (Ferguson). That influence has only become easier with the prevalence of the Internet, social media usage and data collected to reveal citizens’ every click. Only in recent years has disinformation become part of routine news and discussion because of how successful and efficient it has been in manipulating its audience. As noted earlier, it is essential to develop data literacy so that we can not only identify bogus information design but also avoid becoming part of the problem ourselves.

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Alberto Cairo (2020) describes a variety of ways in which an information graphic can mislead, intentionally or intentionally. Those ways include: ●

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Emphasizing or omitting unfavorable data Showing too many or too few data to allow for any meaning to be drawn from it Concealing or confusing uncertainty Suggesting misleading patterns Pandering to preconceived beliefs and expectations

While there are many ways that graphic design principles can help clarify the presentation of information (covered in Chapter 5), an understanding of the data itself first is critical to accurate communication. The numbers on the graphic itself should be directly proportionate to the quantities represented. Likewise, thorough labeling should address any graphic distortion or ambiguity, including dates, essential takeaways and key data points (Tufte 2001). Without labels, viewers have to guesstimate, assume or rely on mental models. Another commonly manipulated aspect of data visuals is inconsistent scale, whether it be for space constraints or another inadvertent warping of the graphic.

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Avoiding misleading data

Dork presents four principles for creating compelling and honest information design: disclosure, plurality, contingency and empowerment (Dork et al 2013). Disclosure refers to sharing why individual decisions were made regarding data, representation or interactivity as a way to establish trust with the viewer. Plurality is exhibited through exposing multiple facets and enabling various interpretations since no one visual will capture the data in all their richness. Contingency allows a range of ways viewers can experience a visualization and make sense of an issue, rather than presenting a predetermined conclusion. Finally, empowerment allows the designer to have a voice while also inviting viewers to question the visual, utilize it to tell their own story and shift from awareness to action (Dork et al 2013).

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The World Health Organization (WHO) asked Interactive Things to build a platform to become an authoritative source of information about global violence and violence prevention. The ambitious goal was to apply to a range of stakeholders, including academics, internal members of the WHO, policy and program-makers, journalists and the general public. Violence Info needed to give different tools to people who are working to prevent violence in different countries to be more productive with the resources that they have. Already a significant global voice, the team at WHO steered the tonality, accuracy and level of detail of the project.

The following case study, Violence Info, is a web platform created by Interactive Things for World Health Organization (WHO) to become an authoritative source of information about violence and violence prevention around the world. The ambitious undertaking does an exceptional job of not only determining a way to showcase data honesty from varying sources and collection methods but also adheres to the principles presented by Dork.

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Case Study: WHO, Violence Info At the onset of the project, researchers at Morse University provided about 80–90 percent of the necessary data. The research team was responsible for all data aggregation, data cleaning, formatting and handing over the information to Interactive Things to be designed for the Violence Info website. Interactive Things needed to pause their work for six months to allow the data collection to catch up to finalize the design of the site. With the data handed over, Interactive Things loaded it into a database that they then built an API on top of to retrieve data for the public website.

For the design, the Interactive Things team had to consider several stakeholders in the process. As an official publication of the WHO, the website must be immediately identifiable with their logo. At the same time, they also wanted to prevent the site from becoming another overwhelming nonprofit site with confusing navigation and excessive information. WHO was consciously making a platform with a single focus on violence without the extra content of a corporate website.

Figure 4.7 Early brainstorming and concept development show an overarching goal to be the “single, authoratative, comprehensive and highly accessible source for information on interpersonal violence and its prevalence” to its stakeholders (green notes at top). Ideas are categorized under awareness, insights, and analysis (orange notes).

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Avoiding misleading data

To maintain the WHO identity while also giving a distinct aesthetic to the Violence Info site, they stripped down the representation of the WHO, moved away from their typical vibrant blue color and developed simple navigation. The Violence Info site also uses distinct commissioned photographs representational of the individual violence types without becoming too cliché or too explicit. Various broken objects subtly suggest the different audiences that might be affected in each category. For colors, the palette is intentionally not too flashy, but also not too sterile. As they defined the set of colors, they looked for colors that would work well together but would also work well individually. Each violence type is represented by its own color in the sub-pages, but when shown together (like all violence types in one country), the colors are harmonious. In the graphs, maps and other data visuals, triangles mark points instead of a more common circle. For bar charts, triangles are also used instead of rectangles and conjoined triangles in the shape of diamonds appear as parts of a flower/star shape in the country overviews. The decision to move away from rounded shapes on the website was because round is something that the human eye finds pleasing. However, triangles are a bit offsetting and give a slightly uncomfortable feeling. The pointy figure is not as pleasant to look at and is a bit edgier, and that is precisely the vibe Interactive Things wanted to give because the website is a platform about a topic that is devastating to individuals and communities around the world. The massive amount of data were difficult to grapple with, especially in cases

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where countries had fewer data studies to work with than others, making statistical sensibilities critical. In early visualizations, some countries felt misrepresented because data might come from a single study or a study that was too small of a sample to be statistically significant to represent a whole country. To address this issue, the flower-like charts in the country overviews depict which data were available for each country (Figures 4.8 through 4.11). If the diamond petal is colored, that means that there is enough data to make some sort of conclusion, but if it is gray, that means that there is not enough data available. The missing data are disclosed through labeling that helps the viewer understand the data’s shortcomings and build trust (Figure 4.12). As an overview page, handling the visualization in this way is helpful to quickly understand which countries have studies and to what degree have they collected data on violence and violence prevention. Plurality and continuity are exhibited through multiple ways of exploring the data – by topic of data, the subtopic of data, by country or by studies conducted. The website empowers by providing information on prevention and response and access to the original data and studies. To assure that the information on the website was accessible and expertly presented, Interactive Things performed two rounds of qualitative user testing – one evaluating navigation and another for the visualizations. With the WHO, they recruited participants and did remote user testing using screen sharing and certified tracking software. User testing should be integrated into the design process of most projects, but especially one that has a broad global audience.

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Figure 4.8 The homepage introduces users to the types of data available on the website and the visual cues associated with them (colors, shapes, and categories). Those categories include child maltreatment (teal), youth violence (blue), intimate partner violence (red), elder abuse (purple), sexual violence (pink), and homicide (black).

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Figure 4.9 Each of the websites topics follows the same general format of overview statistics, rates by countries and WHO region, studies of risk factors, and examples of strategies and interventions.

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Figure 4.10 Overall violence studies can be viewed by prevalence, consequences, risk factors, prevention, and response. This view shows consequences with each diamond representing a study and it’s placement representing lifetime prevalence (the farther to the right, the more prevalent). The size of each diamond indicated the sample size of the study.

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Figures 4.11 & 4.12 The countries and areas page allows for a global overview of available information. Any diamonds that are grey represent insufficient data, while colored diamonds mean that there are studies available for those topics that are specific to that country. Clicking on a country allows the user to see an overview of the country’s prevention programs, laws, action plans and policies regarding different forms of violence.

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Maps While a world map appears in the Violence Info site, it is clear that it serves more as a familiar reference for the countries represented in the data. The map is a simple canvas for approximate markers representing more massive datasets and, as such, does not need geographic details. However, maps are one of the most widely used graphic devices that is not only easy to lie with; they, by design, always only offering a partial piece of reality (Monmonier 1996). Monmonier’s insightful book How Maps Lie argues that “to present a useful and truthful picture, an accurate map must tell white lies. . . because most map users willingly tolerate white lies on maps, it is not difficult for maps also to tell more serious lies.” Free online mapmaking tools are widely available and allow users to freely experiment with features, measurements, symbols and other details to best present the narrative that they want. This freedom, combined with a professional level of output, unfortunately can lead to inadvertent lying to themselves and unquestioned authority to others who may view it. It is, therefore, essential to be skeptical of maps to become a better mapmaker and be more aware of what makes a biased and dishonest map (Monmonier 1996). Monmonier describes three primary mapping attributes: scale, projection and symbolism. Because maps are almost always smaller than the area that they represent it is necessary to provide scale as a ratio, a

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short sentence or as a simple graph. Projection refers to how we choose to represent a globe, or portion of it, on a flat surface. The process of flattening a globe always distorts features, either by stretching or squishing different elements like continental shapes, distances and directions (Monmonier 1996). The globe is most commonly projected onto a cylinder, cone or plane referred to as the developable surface (Figure 4.13). The areas of developable surface that are tangent to the projection of the globe are standard lines and represent the only area of the map where the scale is accurate. The father away from the standard lines you reference, the higher the distortion (Cairo TA 265). Since most mapping tools allow you to choose standard lines, they should always be as close as possible to the area referenced in your story. It is important to be familiar with two mapping groups – conformal projections and equal-area projections – when determining how to best show

Figure 4.13

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Maps

geographic data. No map can be both simultaneously. Conformal projections preserve continental shapes but significantly distort their size. The most widely used conformal projection is called the Mercator projection. Initially intended for sea navigation, the Mercator is not a good choice for world land masses because they have heavy distortion the farther they are from the equator, which serves as the standard line. For its major shortcoming, the Mercator is still typically the default in most online mapping, including the widely used Google Maps (Cairo 2016). The Mercator tends to be the online default because rarely do users reference the entire world map at once. Instead, they zoom to critical areas of interest. Since Mercator projections preserve shape, the more zoomed in a user is, the more accurate the projection will be. In contrast, equal-area projections tend to distort shapes heavily while preserving area ratios. The two most widely used are the Gall-Peters

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Projection and the Robinson Projection. However, in 2018, the Equal Earth Projection was developed to show countries and continents at their actual size relative to each other (Figure 4.14). The map is available from three different centerings – Africa and Europe, The Americas and E. Asia and Australia. The projection is remarkably designed and is already widely adopted by most major mapping programs (ESRI 2019). The final mapping attribute is symbols, used to highlight the features, places and other locational data represented on a map (Monmonier 1996). Symbols serve as a code to aid in describing and differentiating various map features, and as such need to be selected based on “symbolic code based on an understanding of graphic logic and the limitations of visual perception” (Monmonier 1996). Selecting map details to show should consider the familiarly of the viewer with the area and access existing mental models to relay new information (Monmonier 1996). Symbols are addressed further in Chapter 5.

Figure 4.14 The Equal Earth Projection is available for free download at equal-earth.com, allowing for different centering points and languages.

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Case Study: Pablo Robles, South China Morning Post, “Apocalypse Now” Fueled by months of severe drought and high temperatures, Australia experienced one of its worst bush fire seasons in 2019 when over thirty people lost their lives, and over 2,000 homes were destroyed. This project shows how the bush fires devastated Australian parks and forests, took human and animal lives and spread plumes of carbon across the world, adding to the climate crisis. The South China Morning Post ran digital and print versions of the story, each taking an approach highlighting their medium’s strengths. The newspaper has a readership of around 340,000 people, and their analytic studies recorded an average reading time of more than three minutes for the digital version of the story. With all information shown simultaneously, visual hierarchy plays a critical role in guiding the viewer through the page. Beginning at the title and brief introduction, we are introduced to the relationship between green land, high temperatures, drought and deaths. The large map background focuses on at just the right level of zoom to understand the proximity of the fires tearing through green land to major Australian cities (highest populated urban areas) and several national parks and forests (further fueling the devastation). The entire continent’s inset map allows the viewer a sense of scale and context by showing fires elsewhere in Australia.

With the focused map showing only characteristics applicable to the story, the viewer can work their way around the perimeter of the page to better understand the causes and effects of the fires. They all hold comparable visual weight, which is suitable since comprehension of the story is not reliant on reading order. However, one could argue that the “best” way to read the smaller graphics is the conventional left to right (combined with the less conventional bottom to top) to read causes before effects. Processed in its entirety, “Apocalypse Now” offers a full understanding of how and why Australia suffered such a terrible 2019–2020 bush fire season in simple visuals that anyone could quickly grasp. Robles successfully shows an overview of the fires to give a sense of the disaster scale, the areas affected and the proximity to major cities. However, his narrative angle for the digital version shifted when he found new information from Australia’s government showing the plumes traveled at altitudes above 16 km and traveling to South America. Despite this little shift, the consistent muted color palette, style of graphics and illustrations and overall narrative tie the print and web pieces together nicely.

Figure 4.15 The full-page printed version of “Apocalypse Now” as it appeared in the South China Morning Post on Tuesday, January 14, 2020.

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The online digital version drills deeper as readers scroll through the story in a defined order. The carbon plumes emitted by the fires are compared to first the world’s tallest building and Mount Everest to give a sense of scale and then shown in relation to commercial airspace ceiling for readers to appreciate the altitude at which the plumes travel. As the real fire plumes’ path was long and horizontal, the website imitates the actual movement by introducing a horizontal scroll motion on the desktop version. As the story developed, Robles realized a critical angle was to show how pollution from the fires had spread to New Zealand and as far away as South America. The path the smoke took was about 11,000 km at an altitude above 16 km. It took around ten days to travel from Australia, arriving in Chile on January 8, 2020, while the organic carbon released was moving around the globe for months. According to Robles, the project’s most significant challenges were collecting the data and generating the maps from Geographic Information System (GIS). He needed to combine a wide range of data from urban and green areas, streets and buildings and precipitation levels. The project also involved managing live fire data from NASA, which becomes outdated very quickly.

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Figure 4.16 Concept sketches by Robles in a vertical format suitable to the newspaper’s full page features.

Figure 4.17 Sketches by Robles exploring the traveling smoke plume, eventually taking a horizontal format for the online horizontal scrolling story.

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Methods – finding and cleaning data A reliable dataset should be at the core of every good information graphic, and trustworthy sources should be at the core of every dataset. Whether looking at or creating information graphics, it should be done with a critical and questioning eye. To develop good practices, it is important to know what to look for in a “good” data source, how to clean data to prepare them for visualization and good places to start when looking for data. Look for the following characteristics when determining a dataset’s level of quality. 1. Validity: The degree to which the data follow rules and restraints set by the industry that it is coming from. 2. Accuracy: The degree to which the data are close to the true values. 3. Comprehensiveness: The degree to which all required data are collected and available. 4. Consistency: Ensuring that all data follow the same rules, and no values contradict each other (within or across datasets) 5. Uniformity: Using the same unit of measure across data. 6. Primary Source: No matter what data you plan to use, trace and confirm that they are the original collected data.

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7. Reliability: Once you have the original data, verify that the source is legitimate and reasonably free of biases. Make sure that a name and credentials are associated with the data and that they include any sources. Also check if the data are affiliated with a credible organization with a history of honest data practices. 8. Most Current: Always use the most recent data available and use discretion if the data are more than a few years old particularly for topics that are always present or evolving relatively quickly. Data cleaning refers to the process of finding and correcting or removing errors and irregularities in data, such as mislabeling, duplications or units of measure to name a few. These data inconsistencies can wreak havoc on your outcomes and lead to misleading or false conclusions. Like with creating the visual itself, there are a number of programs available to aid in the long process of inspecting data. Depending on the tools and personal preferences, data can be assigned certain parameters, automatically scrubbed or replaced or flagged for manual review. Figure 4.18 shows common steps of data cleaning. Since datasets vary greatly, there is no single right process to clean data, but establishing general guidelines for yourself will help develop a consistent practice.

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Figure 4.18

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References Andrews, R. J. Info We Trust: How to Inspire the World with Data. Hoboken, NJ: Wiley, 2019. Bateman, Scott, Regan L. Mandryk, Carl Gutwin, Aaron Genest, David Mcdine, and Christopher Brooks. “Useful Junk?” Proceedings of the 28th international conference on Human factors in computing systems – CHI ‘10, 2010. https://doi. org/10.1145/1753326.1753716. Benjamin Wiederkehr (Interactive Things for WHO Violence Info) in discussion with the author, June 2018. Cairo, Alberto. How Charts Lie: Getting Smarter about Visual Information. New York, NY: W W Norton, 2020. Cairo, Alberto. “Moral Visualization” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 161–174. London, UK: Routledge, 2017. Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication. San Francisco, CA: New Riders, 2016. Campbell, Sarah and Dietmar Offenhuber. “Feeling Numbers.” Information Design Journal Information Visualization 25, no. 1 (2019). https://doi.org/10.1075/idj.25.1.06cam David McCandless (Information is Beautiful) in discussion with the author, July 2018. D’ignazio, Catherine. “Creative Data Literacy.” Information Design Journal Information Visualization 23, no. 1 (2017): 6–18. https:// doi.org/10.1075/idj.23.1.03dig Dörk, Marian, Patrick Feng, Christopher Collins, and Sheelagh Carpendale. “Critical InfoVis: Exploring the Politics of Visualization.” CHI ‘13 Extended Abstracts on Human Factors in Computing Systems on – CHI EA ‘13, 2013. https://doi.org/10.1145/2468356.2468739

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ESRI “Equal Earth Projection.” Accessed September 15, 2020. https://pro.arcgis.com/ en/pro-app/help/mapping/properties/equalearth.htm. Kinross, Robin. “The Rhetoric of Neutrality.” Design Issues 2, no. 2 (1985): 18. https://doi. org/10.2307/1511415. “The Media Literacy Index 2019: Just Think about It.” osis.bg, November 29, 2019. https://osis.bg/?p=3356 . Monmonier, Mark. How to Lie with Maps. Chicago, IL: The University of Chicago Press, 1996. Offenhuber, Dietmar. “Visual Anecdote.” Leonardo 43, no. 4 (2010): 367–74. https://doi. org/10.1162/leon_a_00010. Pablo Robles (South China Morning Press) in discussion with the author, May 2020. Pandey, Anshul Vikram, Anjali Manivannan, Oded Nov, Margaret Satterthwaite, and Enrico Bertini. “The Persuasive Power of Data Visualization.” IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2211–20. https://doi.org/10.1109/ tvcg.2014.2346419. Roberts, Aristotle W. Rhys, Ingram Bywater, and Friedrich Solmsen. The Rhetoric and the Poetics of Aristotle. New York, NY: Random House, 1954. Slovic, Paul. “The Affect Heuristic.” In Heuristics and biases: The psychology of intuitive judgment, edited by T. Gilovich, D. Griffin, & D. Kahneman, 397–420. New York: Cambridge University Press, 2007. Tufte, Edward R. The Visual Display of Quantitative Information. Cheshire, CT: Graphic Press, 2001.

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Exploratory to explanatory data After collecting and cleaning raw data, it is essential to conduct preliminary analyses to familiarize ourselves with what the information has to offer. This phase, known as exploratory analysis, allows us to ask questions of the data and test different hypotheses in several quick visual forms. As we investigate the data, we watch for interesting relationships and patterns between variables, trends and any outliers. With these very first charts, it is important to consider if other patterns might exist behind the visual shown. Beginning exploratory analysis with an open mind helps discover the story that the data have to tell, rather than assuming you already know. An explanatory analysis is when a few interesting observations from the data are prepared to share with a more general audience. Shifting to this form of study typically focuses on data context – the why and how of its insights. With key findings extracted, a narrative is built to help communicate the information more thoroughly, either with written or additional visual components.

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Using data to explain a concept or idea is very common, but there are also instances when a hybrid exploratoryexplanatory approach is appropriate. In this approach, data are curated to be somewhat topically focused but encourages the audience to draw personal conclusions from it. This type of visualization is most often interactive, allowing for the choice of parameters and constraints to be applied as the user desires. An example of an exploratoryexplanatory data visual is shown later in the chapter. Ultimately, storytelling with data is in the hands of the designer, so it is critical that you not only have a sound data set but are also telling an honest story that resonates with your audience. This chapter will describe the functions and goals of telling stories with data, explore what makes a story persuasive and memorable and look at types of narrative structures and how to storyboard a data-driven narrative.

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Functions and goals of storytelling with data At its most basic level, information design seeks to show something in a visual way that is otherwise difficult to explain and engage an audience verbally. Not all information design is narrative, but as we explore ways to address social issues throughout this book, some form of narrative is typically necessary. For that story to form, there needs to be a sequence of facts or events, generally including a beginning, middle, end and change occurring in between (Black et al, 250). In his book Effective Data Storytelling, Brent Dykes describes a trifecta of critical elements in skillful data storytelling as data, narrative and visuals. These three components, Dykes (30) says, work together to: ●

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Explain: The audience understands what is happening in the data and why it is essential because the data present as a narrative visual form Enlighten: The viewer gains insights that they would not have otherwise seen without visuals applied to data Engage: The audience becomes interested and invested when narrative and visuals are combined

Using the right combination of these three elements can create a data story that can influence an audience and drive change (Dykes 32). Figure 5.1 is a recreation of Dykes’ model.

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The story of information graphics can be used for thinking and showing by having both exploratory and rhetorical functions. They serve as a tool for finding new knowledge as well as a means for communicating (Offenhuber 367). However, it is critical to study the audience to understand what will extend their understanding. There is a delicate balance between what is too familiar and uninteresting to the viewer and too strange, which will cause them to disengage. It is human nature to ignore information that we have already attained, and we are equally likely to ignore information that we cannot possibly comprehend (Andrews 38). In instances when data need to be communicated, but are also abstract or complex, metaphors can help link the unknown to something already known by the viewer. They help generate understanding through visual cues to ground them in a familiar reality. One example is the use of basic computer icons like the trashcan for deleting files and an envelope to indicate email. When computer technology emerged, these were natural visual metaphors for users to grasp, linked to a physical equivalent that they were already very comfortable with. The next chapter will further explore the role of icons as an essential tool in information design.

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Figure 5.1

The following two case studies will help demonstrate how explanatory data can analyze very focused sets of data within a broader range of possibilities. The 2020 COVID-19 pandemic saw countless ways of analyzing data – case counts, deaths, new cases, recoveries, tests conducted and so on. Arguably, the ones that provided the most insight were those presenting information as part of a narrative. Though hard to measure for sure, it is hard to imagine that seeing these graphics does not

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influence the audience in some way. Each of these unique perspectives shares the common goal of educating the audience on how the world changed so quickly. The first piece by Martin Krzywinski follows COVID-19 mutations worldwide from Wuhan to outbreaks in Italy, Iran, a Grand Princess cruise ship, and beyond. The second project by Giorgia Lupi and her team at Pentagram tackles redesigning New York Governor Andrew Cuomo’s daily COVID-19 press briefing slides.

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Case Study: Martin Krzywinski (Canada’s Michael Smith Genome Sciences Centre at BC Cancer), The Many Paths of COVID-19 Spread Martin Krzywinski designed a rendition of this visual for Scientific American to show the spread of COVID-19 by following different genetic mutations. The data come from Nextstrain, an open-source project that continuously updates publicly available pathogen genome data to improve outbreak response. Nextstrain analyzes sequenced SARS-CoV-2 data from around the world and offers visualization tools to compare sequences (Figure 5.2).

Figure 5.2 Nextstrain’s SARS-CoV-2 (COVID-19) analysis is updated daily with sequences from around the world. By contributing data to Nextstrain’s team, we can see the regions of each strain’s presence and mutations that occur.

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The story of the initial spread of the coronavirus that causes COVID-19 is complex. Some outbreaks can be traced to a single individual. Others are triggered by multiple incoming infections from various destinations in a short time. The availability of high-throughput and rapid genome sequencing allows us to track the global outbreak as it happens. This type of complex graphic has great value in understanding patterns of disease transition to better prepare for the next pandemic. As the virus spread, random mutations slowly accumulated in its genome. Every new case inherited the mutation from the infection source. Using these mutations as a “barcode” allows us to construct a “family tree” of cases. Although the genomes’ differences are tiny – the largest number of mutations in this chart is sixteen, representing about 0.06 percent of the genome – there are different enough “barcodes” of mutation to track the spread accurately across locations and time. Krzywinski explained that the brief for this project was specific in that the goal was to trace how COVID-19 cases progressed after the initial epicenter in China. As a form of tree chart with branches of information, the cases could be traced as they made their way through Italy, Iran, the Grand Princess cruise ship and the United States. The idea would be for the graphic to be the background for the story vignettes, initially ones that are in the news; however, they may or may not work once they are superimposed on the data. Each country is an experiment on how to manage the outbreak. As more cases are sampled and added to the tree,

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together with hospitalization and mortality data, we will be able to retrospectively assess the benefits of mitigation strategies such as social distancing, travel restrictions and quarantines to provide better guidelines for the next outbreak. While the original graphic itself is generated through Nextstrain’s visualization tools, Krzywinski had to analyze and stylize the output further to make it tell a story to a broad audience. Simultaneously, it was critical to respect the complexity and not overly dilute anything about the representation in a way that would be criticized by the scientists collecting and working with the data (Figure 5.3). For example, even the word “evolution” was called into question as part of the original title, with Martin thinking that the word might be misleading. Even though evolution typically requires some variety of selective pressure, or amount of genomic change that occurs to drive natural selection, it’s not just random mutations. Being cautious about word choice was important not to imply that the virus somehow got smarter, more dangerous or more or less infectious. While any of those scenarios may be true, the data changed so quickly that no one outcome was supported yet. Graphically, there were many questions about how to best present the data, as the tree of mutations split into smaller and smaller branches without making the visual too busy. How far down the tree should be highlighted? Would it be technically correct to highlight it to the end? Where does the graphic stop, mainly because there’s a lot of mixing and uncertainty in the model?

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Figure 5.3 Krzywinski uses Nextstrain’s general visualization, while building in additional information and contextualizing based on specific outbreaks that people are likely familiar with from media coverage. Rather than dots staying the same size, Krzywinski uses slight variation in sizing to show the number of mutations in the sampled genome relative to the first genome sampled in Wuhan, China. He also calls out different clusters and patterns representing the Grand Princess cruise ship, and outbreaks in Iran, Italy, and the United States.

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One flexibility that a tree offers is that it could flip along the main branches and not affect the data’s integrity. To benefit the layout, Krzywinski considered flipping the tree to bring its parts that were the focus of the story closer together. Ultimately, he was conservative in leaving the Nextstrain branch layout because he did not want to open himself to criticism on such a sensitive topic. Deferring to the experts how they show the data while highlighting areas to make sense of ultimately felt like the most appropriate solution. Filtering the data set for what is relevant and removing what is extraneous and does not add to the story is challenging. It can be hard to accept the fact that not everything can be shown. To select which data to show requires a lot of combing, finesse and admittedly also takes a subjective sense of how much density is appropriate. Color choices created another challenge: how to display the broad color palette representing the different regions in the story without getting them muddled together. While the instinct and even design training would suggest making the colors as different as possible, it isn’t necessary in

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this case because Krzywinski wasn’t trying to tell a story about all regions. Areas of minimal contrast are intentional, while the areas highlighted as part of the narrative are much easier to decipher. An effort was also made to assure that there were no inadvertent color associations made representative of regions or cultures. Krzywinski describes his visual goal as one that gives a compact, digestible snapshot of COVID-19 complexity that allows the viewer to experience the data authentically. He wants the viewer to feel like they’re somehow quantitatively digesting the information without buying into an extensive narrative. The experience can be as simple as reading one of the story callouts on the right and having an adequate general understanding. The visual is flexible enough that it can be entered and exited from any direction at any time. Krzywinski argues that most data sets tend to be very important to a small group of people, but a data set rarely carries such significance to almost everyone. The universal importance highlights a need to be extra careful to represent everything as neutrally as possible with the story’s emphasis.

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Case Study: Pentagram, Covid-19 Like most New Yorkers in the epicenter of COVID-19, the designers at Pentagram organized their day around Governor Andrew Cuomo’s press conferences, relying on his rational explanation of developments in the fight again COVID-19. With uncertainty and misinformation rampant, these predictable, measured daily briefings became a daily lifeline. Governor Cuomo routinely used graphs and tables in these press conferences, and often individual slides could be seen circulating on social media. Despite an appreciation for the governor’s use of data, the designers also saw an opportunity to improve the visualizations to effectively and vividly communicate these critical statistics. They immediately saw the potential to inject more nuance, context and humanity into the governor’s various graphs and charts without sacrificing the exactitude that they

had come to depend on. As a team, they decided to redesign the slides themselves. They began their work by conducting an audit of the governor’s presentations throughout the pandemic. They tried to understand what made his visualizations effective while also identifying areas to target for improvement. After re-watching half a dozen of his presentations, they quickly discovered that legibility and simplicity were key. These are vital stats that all people need to know, so this was no time for anything less than total clarity. But they also came to the realization that important information was being glossed over and ignored in these visual aids. Additional insights were hiding in the data, ones they felt were important to surface for New Yorkers who were tuning in every day (Figure 5.4). They also noticed a few graphs that reliably made an appearance at every press

Figure 5.4 Sample graphics used by New York Governor Andrew Cuomo in his daily COVID-19 daily press briefings during New York’s lockdown.

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conference: a bar graph of hospitalizations, a list of numbers signifying deaths per day and a diagram illustrating the “R-naught”, or the average rate of infection for the virus. They decided to focus on these three core visualizations, redesigning them from the ground up and adding new layers of data to enrich them. They also created a new graphic for COVID-19 tests given/positive diagnoses. As testing became a central focus across the state, they wanted to try imagining how that graph should look (Figure 5.5). Visually, the designers wanted to be as accurate as possible in the new visualizations, down to using the color

Figure 5.5 Concept sketches by Lupi with added layers of context to develop a deeper connection between data sets.

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palette and fonts specified in New York State’s brand guidelines and preserving the familiar “Stay Home, Stop the Spread, Save Lives” footer graphic that has occupied the governor’s slide deck. However, it’s important to note that redesigning these slides was a wholly speculative exercise. The group emphasized that they are information designers, not statisticians. They don’t pretend to be experts on these numbers and know, for instance, that there are considerable discrepancies in how hospitals and health agencies are collecting data. Some of the data included in these slides doesn’t exist in publicly available sources, so they had to make informed conjectures (noted with asterisks). But despite these shortcomings, the work is an important starting point for more successful data visualizations. The designers saw their role merely as engaged citizens who care deeply about ensuring both broad and deep understanding of the pandemic’s unfolding impact on all of us. Above all, they wanted the project to start a conversation about the role of information design in crises and hope to iterate on this work as more feedback is collected. Since the time of these graphics, the Cuomo administration has been investigated for undercounting nursing home deaths in the first half of 2020. Further, aides reportedly rewrote a health department report to hide the real number, which is said to be around 50% higher. In the July 2020 report in question, it said that more than 6,200 nursing-home residents had died, rather than the nearly 10,000 who either died as a resident in the home or in the hospital. The defense for the discrepancy was that out of facility data was omitted because of verification issues. Despite the clear data integrity issues that we are now aware of, I have decided

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that the humanizing approach of Lupi and her team (with the “trusted” information believed to be true at the time) is still worthy of inclusion in this book. In a time of such massive loss of life, her team found a way to better contextualize and feel the number of casualties from widespread tragedy beyond default PowerPoint graphs. Hospitalizations The governor’s slide for hospitalizations focuses on the daily hospital population and the rolling average curve to imply a trend. Starting with these elements, they added layers of additional information that they felt were necessary to contextualize the core statistics further. It illuminated

the forces that were moving the graph’s slope downward by overlaying both new hospitalizations and discharges (Figure 5.6). Deaths The Governor’s slide recounting daily COVID-19 deaths, or “lives lost”, is arguably the most interesting. Rather than presented as a sterile bar graph, the number of fatalities is listed only numerically, like a grim tally. To underscore the inherent humanity of these statistics, the designers introduced an imprecise dot to metaphorically represent one individual death from the virus, creating a sort of visual memorial to those who have unfortunately succumbed (Figure 5.7).

Figure 5.6 The redesigned bar chart allowed the viewer to see the relationship between data. Each days shows number of deaths, net change in hospitalizations, released patients, new hospitalizations, and daily R-naught slope (infection rate).

Figure 5.7 The press briefing included many facts and figures about the pandemic, with lives lost hitting staggering amounts that are difficult to comprehend. This graphic helps in taking pause to actually see what 540 lives lost in a day looks like.

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Tests/Positive Diagnoses (added slide) In this slide, they wanted to give shape to the proportion of tests completed (an essential measure of societal response to the pandemic) to positive cases. It was also important to compare these data with the number of hospitalizations, which in this context look minuscule. Finally, they wanted to shed light on missing data: the number of asymptomatic individuals who didn’t know they had the virus or those who suspect they do but are self-treating at home. They chose to represent these data, which is unknown but estimated to be up to 25 percent of all New Yorkers, with gradient color to evoke imprecision and ambiguity (Figure 5.8).

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R-naught This chart was the most comprehensive redesign: the R-naught, or the average number of people one person will infect. It’s a complicated stat for most laypeople, but by visualizing the number on a timeline, compared to the R-naught of Wuhan, this vital measure was made more evident. Something as impactful and unprecedented as COVID-19 made the magnitude of change challenging to explain and comprehend. It was sometimes equally as difficult to persuade and educate people as to what was known and not known about the virus. However, presenting the information in a narrative format is a step in the right direction towards building comprehension and recall of the most critical information (Figure 5.9). Figures 5.8 & 5.9 Figures 5.8 and 5.9 show testing and infection rate that effectively show uncertainly, particularly in asymptomatic people. Helping to contextualize, testing numbers are compared to the broader New York population.

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Persuasion and memorability In R.J. Andrews’ book Info We Trust, he describes elements that make us doubt storytelling. In stories, “speculation beats truth-seeking, exaggeration beats subtlety . . . excitement beats monotony; self-deception beats rational analysis, expectation beats living in the moment” (S1 P135). He argues that stories are irrational, but they also help us do things that only humans can in making meaningful connections. At its best, information design is the art of balancing visual communication and human connection. All humans – storytellers and audience members – react to characters within a story. We empathize with them and imagine feeling what they are feeling. Their experiences have the power to affect us if they reach us on an emotional level. Whether a story and a character reach an emotional level is dependent on the viewer, and their response is tied to their own unique prior experiences. While we cannot make them care about a topic, we can try to utilize their existing beliefs to help them adopt new beliefs. This link is similar to the role of visual metaphors previously described in this chapter. It is the use of existing ideas and new beliefs that “contribute to maintaining, questioning or transforming social values through argument” (S6, P38). Intentional or not, information design can be persuasive.

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Essentially, storytelling with data is a type of persuasion. While persuasion is a potentially tainted term with negative connotations in other fields, designers persuade to educate or endure some sort of action. Sykes notes that despite a designer’s instinct for utilizing stories, analytical minds and organizations can struggle to see how stories can be more powerful than statistics. Facts are viewed as hard and objective, while stories translate as soft and subjective. However, if trying to change behavior or create action, stories have proven to be more persuasive and memorable than statistics (Dykes 29). Like in the past example from Governor Cuomo’s COVID-19 briefings, it is difficult to remember even approximate total hospitalization numbers of lives lost by day as raw numbers. When the same information is presented in a more narrative humanized format, it becomes more engaging and easier to learn more about trends in New York and feel the magnitude of lost lives. They can also see a complete picture of how different variables appear to connect. Both the original and redesign express facts, but the redesign leverages points to create a more story-based interpretation. Table 5.1 summarizes how viewers react to facts versus stories. The traditional view of stories being soft or fuzzy can and should be challenged. By combining data with narrative, we give the audience something to interpret, compare against their current belief systems and consider insights.

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Table 5-1:  Reaction to facts versus stories, as presented by Brent Dykes Reaction to facts

Reaction to stories

1. We scrutinize facts we don’t like. An audience is more accepting of data that they already agree with. 2. We see facts that conflict with our worldview as threatening. 3. Our brains may warp facts to support preexisting biases. Motivated reasoning positively reinforces false, biased conclusions. 4. Attempt to correct what is seen as fact can potentially strengthen a misinformed position. The backfire effect occurs with corrective information reinforces rather than weaken misinformed beliefs. 5. When facts are visualized, they are harder to reject. Information deficits can be reduced through visual explanations.

1. More of the brain is engaged Stories engage more sensory areas in the brain beyond producing and processing language. 2. A unique connection between the storytelling and audience is formed. Neural coupling can occur when similar brain activity is mirrored. 3. Attention and empathy is increased. Stories cause the brain to release two hormones – cortisol and oxytocin – that increase attention and empathy. 4. Stories reduce skepticism more than facts and make people more open to change. Narratives draw us in and reduce criticism of details while modifying beliefs to better align with the story. 5. Stories enhance comprehension. Stories help explain and understand difficult or complex concepts.

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Narrative structures and plot Constructing an insightful data story takes extreme care to assure that it is interpretable, relevant and offers a fresh perspective. Fortunately, we already have models of successful storytelling techniques to serve as a foundation. Storytelling has been a part of humanity for thousands of years to pass relevant knowledge from one generation to the next. The content of the stories is essential to our livelihood, reinforcing cultural standards, building social bonds, instilling values and educating (Sykes, 94). A few narrative models that have stood the tests of time include Greek philosopher Aristotle’s tragic play structure and German playwright and novelist Gustav Freytag’s pyramid for dramatic structure. Aristotle used a simple three-act cause-and-effect sequence that includes beginning with a setup to introduce characters and setting, encountering an obstacle near the middle and ending with a resolution. Influenced by Aristotle’s model, Freytag refined the narrative arc by adding a step on either side of the middle conflict or climax, calling them the rising action and falling action. This model most closely represents what we use today for a linear story. Figure 5.10 shows how Freytag’s literary format relates to our efforts in storytelling with data.

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The flow of a narrative brings life to the story through a sequence, closure and meaning. A story with life has an impact on its audience, drives action and is retold to others. While linear stories are the more traditional model, it is sometimes beneficial to use a more non-linear or hybrid linear-nonlinear format in information design. Graphics that require sequential, step-by-step information or chronology benefit from being shown in a linear form. There is typically no choice on the part of the viewer for the order in which they experience the information. If interactive, a user can expect simple navigation elements like a start or forward button, progress bar or scroll. A non-linear narrative is much more viewer-driven and requires more data savviness on the audience’s part because non-linear formats offer the maximum amount of information and exploration. A hybrid linear-nonlinear visual falls nicely in the middle, allowing the viewer to choose the order in which they want to experience the narrative but with predefined parameters (Cairo 2020). It is common to see feature stories of major publications presented in a linear web narrative and a hybrid print format. For example, National Geographic

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often creates beautiful multimedia web experiences of a feature, like the “Katie’s New Face” and “World Plastics” stories shared in previous chapters. The web version of the story will be presented in a linear format with a traditional story arc, integrating video content, information design, photos, maps and written narrative. This format is a conscious decision on National Geographic’s part because they strive to educate their audience and create a human connection through the people and places they feature. It is important to them to set the stage, provide relevant background and introduce characters before introducing

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obstacles, turning points and some sort of conclusion or call to action. However, they may present the same story in a printed two-page spread that thoughtfully uses visual hierarchy to show multiple pieces of the lengthy web feature simultaneously. This linear-nonlinear presentation allows the viewer to choose the order they want to explore while also facilitating a more readily available comparison between elements. Print production allows for more detail and smaller sizing of items without compromising quality and readability, whereas web resolution and screen size necessitate a more gradual reveal of information.

Figure 5.10 Freytag’s literary format.

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Another narrative approach is common to journalism. It shifts the story arc to share the most critical information at the beginning instead of building to the climax by familiarizing the audience with various features of the story. This narrative structure is useful when trying to pass along information quickly, for instance, breaking news or even social media versions of stories. The audience quickly sees the main takeaway of the piece and then can decide if they want to know more about the underlying story. Unfortunately, many people predominantly receive their information in this way, and the absence of a traditional narrative structure deprives them of building emotional connections. The information designer’s role is to create a compelling story that makes others want to know more. The following is a general overview of how to tell a story with data: 1. Identify the compelling narrative in your data. If you have a compelling insight, it should be clear what you want the audience to know, how the data supports your point and what the audience should ultimately do with that information. If your data does not necessarily lend itself to a particular narrative format, that does not always mean that it is not worthy of sharing. Instead, it might benefit from an exploratory form. Exploratory visualizations should encourage examining relations and facilitate interaction between data to allow users to uncover their own stories (Stikeleather).

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2. Consider what the audience knows. Even if this is the viewer’s first exposure to the topic, most people will ignore or be bored by oversimplification. A general audience is already aware of the topic but will benefit from an overview, major themes and unique perspective. Expert audience members require less storytelling and will likely want more opportunity for detailed exploration and discovery. Knowing who you are trying to communicate with will help decide what specific data to use in the story. 3. Curate objectivity. Judgment will need to be made regarding what data to include in the narrative. Be sure to have ample contextual and background information to help minimize any personal biases that you may have. Even if the goal is to persuade, it should be based on the data itself rather than on what you want the data to say. Simple ways to “encourage objectivity include: labeling to avoid ambiguity, matching graphic dimensions to data dimensions, using standardized units and keeping design elements from compromising the data” (Stikeleather). Objectivity requires that you acknowledge missing data, outliers and values out of range to provide a complete view of the story.

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4. Create the story. Determine the flow, order and how each of your elements come together. Explore different ways of visually representing the data and pairing it with a written narrative to move your story along. What do the visuals say themselves, and where do they need some extra information to aid their understanding? Does this require additional graphics, text or both? Also, consider the layers of what the data reveals – are their personal stories that could help humanize the numbers? 5. Your audience will be expecting reasonable closure at the end of the story. Try to provide pieces of narrative that motivate them to resolve by creating suspense, answering a sub-question to the main presented issue, elaborating over time or surprising with an unexpected piece of information. 6. Edit, refine, edit, refine. Try to massage your narrative into a relatively smooth and well-defined arc, free of excess information that is not essential for understanding the story, its characters and its context. When you can, declutter and tidy your story by chunking information into manageable pieces – you are not necessarily looking to simplify the complex reality of your story, but rather assure that it communicates effectively.

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The following case study tells the story of life in the camps in a linear web format for Reuters. Dawn Kai describes the Rohingya refugee crisis as a human rights and humanitarian disaster involving a Muslim minority fleeing Myanmar to mostly Bangladesh. While the crisis has been happening for many years, a surge of refugees began resettling in camps starting in August 2017. The purpose of this Reuters piece was to bring awareness to the living conditions in the over-populated camps. As a global news outlet, Reuters does not make any assumptions about what their audience may know or not know about any given topic. However, since they generate a broad audience via social media, it is presumed that most viewers have a general interest in world news or significant current events.

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Case Study: Dawn Kai, Life in the Camps, Reuters Process: After the August 2017 surge of refugees, Reuters began to wonder what the living conditions were like once most of the people had settled. Using data from United Nations agencies, they began to plot data points in search of connections. Granular data sets locating latrines, water pumps and defecation areas were all plotted. It became clear the issue was not the refugees lacking access to washrooms or water, but instead that water and washrooms were located so close to each other that it became a health problem. While the data points provide a piece to the puzzle, it lacked any indication that this was affecting humans. To link the data to real people, coordinates of the areas that Kai was interested in were given to Reuters photographers on the ground to picture the actual regions. With the main takeaway for the story identified, the narrative needed to be fully developed for the audience if they had minimal knowledge of the crisis. The story begins with satellite images comparing before and after the refugee surge as a visual that makes it very easy to understand how quickly the camp grew. The Reuters team strung together a variety of other data components that contributed to the story, including size comparisons, washroom conditions, landslide dangers and overviews of the most common health concerns. The narrative takes the viewer through the complex crisis in manageable chunks – looking at individual data sets, comparing those sets to others and putting

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them in context with UN standards. Soon we can see the connections between the physical map, data points representative of separate variables and how those variables interact together. Kai says that one struggle throughout the typical information design process is continually thinking about how much information people can receive. These considerations include overestimating or underestimating readers’ knowledge level, their attention span or if they are familiar with this sort of media and how close they are paying attention to the details. However, Kai believes that the value in information design is that it is a common shared experience that you and your friends can have without reading a 2,000+ word article. Sometimes visuals can honestly explain things in a faster and more engaging way. Visuals also provide a considerable advantage in telling stories that include a lot of spatial information, but maybe more important is how you choose to implement those visuals. The satellite map includes too much detail to see data points, but it does serve the purpose of setting the stage. Once the focus shifts to the datasets themselves, the map is stripped down to only feature essential information in a vector format. This simplification allows for the data to step into focus and be better understood. Even with little knowledge of the refugee crisis, “Life in the Camps” is presented in such an approachable way that the audience can immediately understand what

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Figure 5.11 According to data from the U.N. migration agency, there were 7,839 latrine blocks in the camp at one point. This amounted to up to five latrines per block being shared by up to 115 people (with U.N. guidelines recommending 20 people).

made the living conditions so dangerous. Photographs also play an important role in having a clear idea of what the circumstances are like and make the viewer imagine what it must be like to be there. (Figure 5.11) All the various visual and textual elements contribute to making this a memorable explanatory piece, served to the viewer linearly to ease understanding. The following case study is an example of what exploratory data look like in a non-linear format. GOV | DNA is an interactive application that allows users to explore the “DNA” of what makes a good national government.

Figure 5.12 When water pumps are plotted in relation to latrines it is clear that they are too close (and also too shallow), making them susceptible to contamination.

Figure 5.13 Photography immediately contextualizes and humanizes the plotted data points. Sanitation guidelines say that latrines and water sources should be at least 30 meters from each other.

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Case Study: Werner Helmich, GOV | DNA Background: GOV | DNA was Helmich’s submission for the World Data Visualization Prize, awarded at the World Government Summit in 2019. The World Government Summit is an annual event held in Dubai, United Arab Emirates, to gather government leaders for a global dialogue about governmental processes and policies. The summit serves as a knowledge exchange between government officials, thought leaders, policymakers and leaders from the private sector to analyze future trends, issues and opportunities facing humanity. The 2019 prize focused on how governments improve citizens’ lives, and the innovations – seen and unseen – that drive and measure success in this realm. This improvement could include the environment, happiness, employment, technology, agriculture or any other government support. Helmich decided to focus on the competition concept of “What Makes a ‘Good’ Government?” with the thought that policymakers would be interested in being able to learn how their country could improve and which countries could serve as examples of good governments. Process: Helmich sees data visualization as a user interface that people can interact with to gain insights. This notion makes his visualizations a starting point for discoveries, depending on a user’s needs and interests. In this case, the concept was to visualize and compare countries using a DNA sequence made from country indicators, including government, social,

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economic and environmental values. This visual would be the starting point for an interactive data browser to compare countries and analyze specific indicators or single countries. These comparisons could then indicate the relative success of characteristics for a nation, and how a “good” government can play a role in that success.  Helmich started by familiarizing himself with the visualization context, including the theme, goal, audience, conditions and stakeholders. He was provided with a dataset from the organization with relevant indicators and then added data on press freedom. With an idea of what the data entailed, he came up with the DNA concept and parsed the data to enable fast processing and iteration. Through iteration, Helmich tested ideas with the data to see what different results would look like, then improve or move based on the outcome (Figure 5.14). Using Pixi JS, the primary visual environment of the animated countries was created first, followed by the supplementary views. These views include the data-view with a dynamic x-y axis (plus z as the size of the country circle), geographic view, DNAview and country view. In the data-view countries with missing data fly out of sight, and the DNA-view handles missing data with a dot in the sequence. For all data, on a hover pop-ups show the indicator, absolute value and relative value as a percentage (meaning, within the group of countries in the indicator, if your score is 84 percent, that is 84 percent of the maximum score within the scoring range) (Figure 5.15).

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Figure 5.14 Concept sketches by Helmich of potential visuals to support global government data.

Figure 5.15 The DNA overview view lists each country with their unique sequences representing their scores in 30 different indicators of government health. The darker the color within each the sequence, the higher the score in a particular category. Any missing lines that appear instead as a dot indicate unavailble data. The default view shows countries by order of ranking, but the order can also be shown by global region or GDP per capita.

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One of the most visually striking parts of GOV | DNA is the use of color. Colors were grouped depending on the region, as seen in the geographic view most clearly. The palette was then adjusted until it optically felt right and showed an adequate distinction between areas. For the DNAview and scatterplot, all data convert to a relative value, 0-1 (x: 0-1, y: 0-1 for scatterplots and opacity 0-1 for DNA). Helmich made the visualizations, views and transitions as smooth as possible. The resulting application enables the user to interact with the data to: • View DNA sequence in which you can find patterns and outliers • View data to compare indicators in detail • View countries to select specific regions • View a world map in geographic view to get a sense of the colors used for each region • Transition between views by clicking on a country, a DNA element or using the filters • Share findings on Twitter. The selected views and filters store in an URL that can be shared so a user could say things like “hey, look what our country X is doing on subject Y”

Figure 5.16 A bubble chart view allows the user to explore different combinations of the indicators from the DNA overview, with indicators available to switch through x and y axises. By analyzing and comparing various indicators a user can begin to investigate the influence that the factors have on countries. This can further help understand the behavior and performance of governments around the world.

The GOV|DNA format is also flexible enough to be the basis for another visual created by Helmich on European Health. The HEALTH|DNA application uses data from the World Health Organization’s European regional database of over 600 indicators (Figure 5.19). Figure 5.17 Clicking on a country’s bubble allows the user to view the specific DNA sequence and values of each government health indicator.

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Figure 5.18 Clicking on axis labels reveals how each indicator is calculated and links to the data source.

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Figure 5.19 The regional view of the countries displays each of the indicators (as controlled through the x-axis). As the indicator is switched, each of the regional bubbles resizes according to score in that particular topic.

Figures 5.20 and 5.21 (opposite) Figures 5.20 and 5.21 show that Health | DNA uses the same general interface as GOV | DNA, with numerous indicators included in each DNA sequence.

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Storyboarding Whether a narrative is linear and explanatory like Life in the Camps, or non-linear and exploratory like GOV | DNA, a storyboard is a powerful way to plan and organize information. Linear squares or rectangles make storyboards an easy way to visualize and sequence a process, the passage of time, or story. By breaking a narrative into small chunks of information, it allows the designer to focus on each frame separately, then think about order and transitions between them. Storyboards are incredibly flexible and versatile, used for a range of activities, including film production, event design, user experience design, and advertising. As it applies to information design, the value of storyboarding is not only its general format but also its language and considerations. In the paper “Storyboarding for Visual Analytics”, Rick Walker et al. (2013) explore the components of storyboarding language: composition, viewpoint, progress, annotability and interactivity. Composition refers to what information is included in a given frame. Viewpoint refers to the type of shot or angle represented in a frame – wide-angle for the start of a progression, and fullshot or close-up for a zoomed-in, more focused view of a specific area or thing. The mantra of overview first, zoom and filter, and then details on demand fit well with displaying information design.

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For example, “Life in the Camps” starts with a bird’s eye over the growth of the camps, then drills down into separate details before showing how all the components tie together to create a public health issue. Progress is how transitions occur between frames. Print, web, exploratory and explanatory information design all require consideration for how one piece of data rolls into the next. Fading or zooming in, turning a page, scrolling or a pop-up should be planned and integrated seamlessly into the narrative experience. If a motion is involved, that should be indicated through annotation or a written description to represent elements and details that don’t translate well to the storyboard frames themselves. Explanatory narratives rely mostly on a traditional linear story structure to resonate and keep the viewer’s interest. The narrative structure takes hints from Freytag’s model, with a set-up, rising action, climax, falling action and conclusion. In the following example, the topic of incarceration rates and abuse among imprisoned members of the LGBTQ+ community begins to take structure with assistance from storyboarding (Figure 5.22). By laying out the story, it becomes clear that there are other considerations that may need to be worked into the story to

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Figures 5.22 The general set-up to the story about incarceration and abuse among imprisoned LGBTQ+ people is formulated in frames 1-5 before reaching some key insights in frames 5-7. The story concludes on a positive note by acknowledging the increase in social justice protests in the last decade, accompanied by actionable resources. Other considerations to potentially work into the story are listed before the frames.

better connect the data points. Although exploratory data does not necessarily lend itself to a sequential order, the same key frames should be considered. Like in the GOV | DNA case study, there is still a need for an overview or set-up, details (determined by the user depending on their specific actions), and key insights and resolution (again determined by the user, depending on what stands out as important to them). While explanatory stories usually rely on textual transitions, exploratory will often implement motion or filtering to transition to a user’s selection. Regardless, both explanatory and exploratory data have a story to tell.

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Given the amount of time and energy that a complete data story can take to make, beyond planning storyboarding also serves as a valuable time-saving device. Getting ideas and sequencing on paper is a fast way of gathering and sharing ideas with an interdisciplinary team to gather feedback. It will also allow any holes in the story to come to the surface before investing time in fully committing to production. Once a narrative is decided on with critical insights and visuals are determined, it is time to encode the story with a comprehensive system of visual cues. In the next chapter, we will focus on the aesthetics of our story.

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Introduction With collected data from trusted sources and a narrative in mind, the visualization process brings meaning to the information by inviting the audience to explore, learn, draw inferences and question the data. Functionality, usability and accessibility are the priority of information design, much like in user experience design. That is to say that even with complex data, they should be understandable and approachable while also encouraging exploration. These goals are seemingly contradictory because clear communication implies a single guided interpretation, while exploration is more ambiguous and allows for many analyses (Tversky 2017). However, clear communication does not always need to result in a single correct reading, but rather it needs to be engaging, empowering and communicate as truthfully as possible.

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For this book, a comprehensive survey of graphic design foundation principles will not be presented. For basics of design, a few primers include Robin Landa’s Graphic Design Solutions, Ellen Lupton’s Graphic Design: The New Basics and Thinking with Type, and Universal Principles of Design by William Lidwell. In this chapter, we will focus on enabling the understanding of information by applying visual cues to nonvisual concepts (Andrews 2019). Although information design is sometimes assumed to have a neutral aesthetic, it can still have personality and elegance. Visual encodings define information design through a vocabulary of symbols, text annotations and other conventions that allow graphics to be arguably more flexible and efficient than written language (Cairo 2020). The goal of data visualization is sometimes assumed to be bringing simplicity to data when the complexity of data should be embraced to engage the reader and communicate the complexity. When complexity is processed and engages the audience, it has the best potential for initiating a specific action or response. An information design’s effectiveness lies in its legibility, graphic elements, structure and overall approachability or impression, among others (Moys 2017). These features inevitably influence the audience’s perception of the data and can be measured to assess the effectiveness of information design (more on assessment in the final chapter).

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Perception and gestalt Since we are bombarded with information at nearly every moment, selecting what we focus on is the first step of perception. When we pay attention to something, we choose that information for further processing. Perception is the objective process that we go through to organize and analyze the data in focus (Pettersson 2017). Individuals differ in how they perceive visuals depending on age, gender, culture, politics and any number of other references that might be pulled from past personal experiences. Human nature dictates that we strive for clarity by comparing what we see and what we know or expect to see. Deviations or outliers, patterns and clusters all typically have a specific meaning to how we perceive data. Once we recognize a particular data pattern or anomaly, we try to provide context for the data that helps determine how relevant it is to us (Andrews 2019). Often the most successful information designs are the ones that make the viewer further question what they are looking at – not because of questionable sources or poor design – but because they become connected to the data and invested in the story they have to

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tell. What the viewer perceives and interprets in the information design depends on the relationship of the elements to one another. The brain works in a holistic way, seeing the whole as more significant than the sum of its parts, which is the central thesis of gestalt psychology. Gestalt principles in the design are widely recognized and applied and can “help us guide the reader’s focus of attention, emphasize certain groupings, and organize the content” (Andrews 2019). Gestalt psychologists argue that the conscious experience is a “whole form” that cannot be broken into individual parts. An excellent example of gestalt is maps because unique symbols, lines, colors and patterns carry little meaning on their own. They must instead be seen together to create a meaningful graphic environment (Andrews 2019). Gestalt principles play an essential role in data visuals because they help organize visual chunks of information that may otherwise compete for attention or overwhelm. While there are several recognized Gestalt principles, the following are most useful in information design (see visual overview in Figure 6.1):

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Perception and gestalt

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Closure: we automatically fill gaps to perceive completeness because our eye prefers complete shapes. An example is a dotted line used to outline, indicate prediction or uncertainty. We see the line as a full boundary or directional aid, while also recognizing the dashed style as being conventionally indefinite. Continuation: we follow and “flow with” lines beyond where they end. This perceived flow is accurate of line graphs where our eye may continue the trend line beyond the parameters of the chart’s frame. Continuation also applies to common symbols like arrows, in which our eye will lead in the direction that it points.

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Figure 6.1 A summary of gestalt principles.

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Figure/ground organization: we perceive an object as either the focus in the foreground or as a separate part in the background. Similarity: we understand and group based on similar properties, including color, pattern, orientation, shape, size, value and other graphic features. Similarity is used widely in information design, with one example being a stepped color palette to show values like temperature on a weather map. This same principle can highlight differences by showing contrast across groups. Proximity: even when we see individual elements, we group ones based on their proximity in time and space as “belonging together”. Grouping is typical of details on a map, like the circles representing plastic in waterways from Chapter 1. Enclosure: when another element or boundary surrounds multiple items, the parts read as a group. Connection: features that are connected by lines read as related to each other.

When designing, it is critical to be aware of the likely perceptual outcomes of your design. Regardless of which Gestalt principles are used, the Law of Pragnanz indicates that people tend to interpret complex data visuals in the simplest way possible. It is vital first to understand how people see before determining how to encode information visually.

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Marks, type, color, and other graphic details By strategically encoding critical information with visual cues, you help guide the audience through your data narrative and ease comprehension. Visual cues should also establish a visual hierarchy and draw attention to critical comparisons and anomalies in the presented data. Together, good aesthetic choices in information design have proven to improve audience judgments of trust and usefulness (Li and Yeh 2010). Jacques Bertin’s Semiology of Graphics (1967; 2011) is arguably one of the most comprehensive analyses of how principles of graphic design apply to information design and cartography. In his collection of over 1,000 diagrams, he presents logical applications of shape, orientation, color, texture, volume and size in creating compelling data visuals. He argues that these elements create graphic density, or the optimum number of marks per square centimeter, between a density that is too great and one that is too sparse (Figure 6.2). He also describes what he defines as an efficient graphic that can support elementary, intermediate and overall interpretation. An elementary level focuses on a specific element within the graphic, whereas an intermediate level focuses on a group of items and may shed light on a trend. An overall level can decipher the general message of the graphic in its entirety. Bertin argues that when all three standards are met,

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the graphic enables the viewer to create new knowledge. In addition to Bertin’s approach, the following addresses some of the standard components of contemporary information design: marks, text and typography, color and imagery. Marks Marks are elements like lines, dots, arrows and shapes. They can be representational like line drawings, metaphoric like an icon, symbolic like a $, or words. In information design, we typically see them as part of a visual system to help communicate specific data and show how they are related to one another. While a mark is meaningful in itself, it can also carry expressive qualities that enhance the message. Lines can be dotted or dashed, blurry and smooth or rigid and pointy. We have grown accustomed to the clean perfection of machine-generated outputs. Still, there are times when imperfection (without comprising accuracy) allows us to feel more connected with the creator and the dataset. As R.J. Andrews (2019) points out, “the superficial aesthetics of roughness can be mimicked, but lacks time.” When more life goes into something, it feels more alive. Giorgia Lupi is perhaps the most widely recognized designer to consistently use this sort of humanized aesthetic. Still,

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Figure 6.2 Recreation of Jacques Bertin’s visual variables as articulated in Semiology of Graphics.

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in this chapter, you will see two other examples of tactile, handmade data visuals. These examples make evident that handmade does not necessarily mean “lacking accuracy or precision”, and helps the viewer see and feel the data. Text and typography The use of typography in data visuals is not just a foundational visual technique – it has a social purpose that demands that the designer understands the situation, context, and audience within and to whom they are communicating (Margolin 2016). Kostelnick (1990, 1996) describes structural and stylistic needs as the two functions of text in information design. Fundamental features reveal the overall document structure, develop cohesion across the visual and enable expansion or contraction. Stylistic functions include creating interest, conveying tone, establishing credibility, signaling emphasis and indicating usability (Moys 2017). A viewer should not have to work very hard to read type in information design. Rather, type should support and clarify while being thoughtfully integrated into the design. All principles of visual hierarchy as they relate to type apply to their use in information design as well. Weight, size and color can be used in different scenarios to highlight similarities, differences or other vital patterns and takeaways. Titles and annotations Naming a data visual carries responsibility with it because, at worst, there is a chance that it is the only thing

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a viewer sees. At best, it interests and engages the audience enough to explore the data further. A title should be clear, informative and factual and can function as a sort of thesis statement worthy of investigating. Annotations, or callouts, help describe elements that cannot translate well visually. This issue is especially true of scientific diagrams or maps of unfamiliar regions or content. They can also be used to draw attention to an essential piece of information within the graphic. Labels clarify scale and measurement, keys and other essential values. If anything is missing from the data (like a country with no reported value), that should also be disclosed. It is critical that text references where data are from and how and when they were collected. Without this information, the data lacks credibility. Color Like in all other forms of design, color can carry critical meaning in information design. Principles of color usage like contrast and harmony hold a particular purpose in conveying information and showing data relationships within different chart types and maps. Color is also strategically implemented in various ways to show qualitative and quantitative similarities and differences. As one of the most powerful design attributes and challenging to master, color can convey categories, quantities, mood, emotion, culture and content themes when used effectively. See Figure 6.3 for a visual summary of the following color use strategies.

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It is important to note that our ability to distinguish between contiguous colors is poor. So stepped palettes can help see differences more clearly if that is critical to the message.

Figure 6.3 Sample palletes representing diverging, sequential and qualitative color schemes.

Associations When a variable or element that you’re working with is already associated with a particular color, opt for using that color rather than assigning your own random one. Things like countries, political parties, brands and even holidays already have deep-rooted color associations. When possible, tastefully leverage that familiarity to strengthen the information you are encoding while also avoiding becoming overly cliché (through the selection of complementing colors elsewhere in the design). Sequential Sequential color palettes vary from light to dark, with a shift in hue or saturation. Low data values usually have the lightest color, and higher values represented with a darker color.

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Diverging Diverging palettes have two distinct sequential palettes merged with a central neutral color, representing a center “breakpoint.” They are typically used when there is a critical midpoint value (mean, median or zero) and two ends of the data distribution to either side. Categorical or qualitative Most useful for distinct categories rather than sequential or divergent data, categorical color palettes try to contain colors as different from one another as possible. The colors help signal to the audience that variables are distinct for representative items rather than on a comparative scale. Choropleth map Typically used for thematic qualitative content, a choropleth map uses a color shaded proportionately to the value of the measured variable in each region. By default, a choropleth map will divide breakpoints based on the count, as in sequential color palettes. However, this is problematic because when a color is representative of a value and spreads over large areas, it gives a false impression that large areas are typically more dominant than they should be. The variable can be normalized by dividing it by the total area to create density.

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Accessibility and color blindness About 8 percent of men and 0.5 percent of women are colorblind, with the most common difficulty in distinguishing between shades of red and shades of green (Nussbaumer Knaflic 2015). Using red and green together to signify important information should be avoided. Still, sometimes standard associations exist that are hard to avoid, like red for loss and green for growth. When the colors are necessary together, they should have an additional visual cue like a plus or minus sign or small arrows before numbers or strategic boldness. If in doubt, websites like VisCheck (vischeck.com) and CheckMyColours (checkmycolours. com) can check your contrast and accessibility for colorblindness. Imagery . . . and when is it chartjunk? “Chartjunk”, coined by Edward Tufte in The Visual Display of Quantitative Information, is the anti-visual cue that distracts and potentially hinder the effectiveness of communication. These distractions include overly decorated or excessive type, dark gridlines, unrelated or busy pictures, unnecessary added dimensions, noisy backgrounds and any other “decoration” that disrupts from the central purpose of the information design. This is not to say that all

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supplementary imagery is chartjunk; it should be selected and integrated with purpose. Although imagery may not be essential to understanding the presented information, it does have the potential to create an emotional connection. The Picture Superiority Effect indicates that pictures are remembered better than words alone. Elements like photos, icons and diagrams can add clarity to information and increase the likelihood that the insights will be remembered (Dykes 2020). In many of the case studies in this book, photos and diagrams play a critical role in making a human connection and allowing the audience to see and feel what the numbers represent. In the following case studies, visual and physical cues are used to see and feel data. First, Shing-Yun Chiang uses data physicalization to show the relationship between poverty levels and flood damage from Tropical Storm Allison in Houston, Texas (USA) in 2001. Data physicalization is a storytelling approach beneficial to multi-sensory experience, engaging and resonating with the audience. Based on previous data work that Chiang had done regarding flood data, this multi-sensory experience integrates an additional more human connection with poverty. Her goal in creating this piece was to provoke people’s awareness.

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Case Study: Shing-Yun Chiang, Houston Flooding and Poverty Houston, Texas, is a historically flood-prone city. The 2001 Tropical Storm Allison killed twenty-three people and damaged more than 70,000 households (Harris County Flood Control District). The flooded homes were located more in the areas where poverty rates were higher. After almost two decades, poverty and flooding are brought into discussion again because of Hurricane Harvey in 2017 (Reeves 2017). The two datasets investigated in this map are poverty status in 2000 and flood damages from Tropical Storm Allison in 2001. The flood damage data come with coordinates in latitude and longitude, whereas the ratio of income to poverty calculates poverty status. The ratio of income to poverty refers to the total family income divided by the poverty threshold (US Census Bureau). This map focuses on those who live below the poverty line. Geographic boundaries in the artifact show Houston, divided by Census tract. The five different depths on the map represent poverty levels of 0–10 percent, 11–20 percent, 21–30 percent, 31–40 percent and above 40 percent, meaning that the lower the Census Tract is, the higher the poverty rates are in that area. Depth acts as the metaphor for social status, with lower-income residents having lower social status and lower position in the map. Blue dots represent the locations of flooded households. To test the laser cutting capabilities for the smallest areas, a 12-inch square prototype was created, focused on the central regions of Houston, where most of the smaller Census Tracts are. In the prototype, the areas in the lowest bracket of poverty level (0–10%) are five stacked

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sheets of plywood, while the areas in the highest bracket (above 40%) have only one layer. In evaluating the prototype, it felt as though the varying depths of the layers needed color integrated to be sure which poverty level the viewer was seeing. A graduated red color scale representing danger addresses this issue by reinforcing the poverty level values with depth simultaneously. The thickness of each poverty level was doubled from the prototype to the final 24-inch square artifact to make the disparities more visible. Some digital visualizations of poverty used 2D choropleth maps as the visual approach. A 2D choropleth map is useful for displaying one variable at a time through a graduated color scale. When coloring two variables

Figure 6.4 The 12-inch prototype displays the dramatic difference between Census Tracts in Houston. With all poverty levels indicated by only depth of layers, it was suggested that the layers adopt a graduated color scale to make poverty levels easier to identify. To make the disparity more visible, the thickness of each poverty level is twice as it was in the prototype.

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in one map (poverty and Census tract), it could confuse due to the overlapping nature of the data. With flood damage as the third layer to be added and colored blue, the depth dimension is adopted to prevent the confusion of overlapping colors from the two data layers. Since the texture of data visuals can be perceived by both visual and tactile senses, Chiang was also thoughtful in how flooded households were represented on the wood. While each flooded home is represented with a dot, the border of each is rough and messy, imitating people’s impression of flood damage. To create this effect of irregular edges and surfaces consistently, Chiang used thin wooden sticks dipped in blue paint. For the five levels of poverty, five files were prepared for laser cutting. The color for each poverty level was painted on the whole plywood board before laser cutting to simplify the process. The flooded households were added layer by layer while assembling the map. The stacked and painted wooden map was placed in a case with a physical stair-like legend. The case makes the top layer (0–10%) as the ground surface, and areas with a poverty rate above 10 percent are underground. The case helps the audience focus on how deep the impoverished areas are instead of how high the wealthy areas are. It was essential to Chiang that the audience could feel the relationship that information made. If she had done an interactive map format, it would likely translate as many dots that you could possibly hover over. Still, it wouldn’t provide the same emotion and tactile experience of the poverty metaphor. Like Chiang’s three-dimensional approach to seeing data, the next example creates a layered data experience that adds meaning to the story behind the iconic Georgia peach.

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Figure 6.5 The center of the map in the final piece better shows the dramatic change of values. Flood damage is indicated by cyan dots created with wooden sticks to better imitate rough and messy irregularities of damages (as opposed to perfect dots common to data visualization).

Figure 6.6 A stair-like legend helps make the connection that with increased poverty comes decreased social status. The deeper the Census Tract is in the map, the higher the poverty level is.

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Figure 6.7 The final map alongside the prototype

Figure 6.8 A brown and more red color scale were tested for contrast with the cyan paint. The deeper the layer, the darker the color.

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Case Study: Sarthak Kathuria, Greetings from Georgia This project examines the data-driven story behind the history of the iconic Georgia (USA) peach. Kathuria used data on peach acreage and slavery in Georgia to create an unexpected souvenir book for tourist-destination Savannah, Georgia. The experience of interacting with the book tells the history of Georgia through data, wherein the deeper you go into the book, the deeper you are in time. The peach has long been a symbol of Georgia in popular culture and tourism, featured on everything from license plates to postcards and gift store collectibles. However, historically Georgia was known for cotton plantations and its substantial connection with slavery. When the government and residents wanted to distance themselves from the cotton association, the peach was adopted as the new crop of choice to symbolize the state. Turning the pages of the book is an unexpected data and user experience, peeling away the layers to trace the history of the peach back to its ominous history rooted in slavery (1790–1920). Process: Using archival data on peach acreage, slave population and cotton, Kathuria tabulated the data to gain a better understanding of patterns over the years. When looking at the data, he could see how the slaves that were recruited to work on cotton were the same slaves that worked on peaches because they were harvested at different times during the year. Yet, people rarely think of slaves in connection with peaches but very strongly associate them with cotton.

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Working with the idea of the landscape shape of fields, Kathuria worked on letting the rectangular pages create a subtle bar graph. He calculated increments and assigned measurements of page sizes proportionately to show not only through numerical values but also through page size the increase in slavery as you dig deeper. A peach fleuron was designed as a navigation element to capture time as the book progresses. In the preliminary prototype, the right-side pages were black with the timestamps, but the final draft has more impact with the zoomed-in texture of the back of a dark-skinned African slave. The change is subtle but is an impactful image choice to humanize the content going from sterile flat black to human flesh. The first book was perfect bound with thick chipboard material for its pages. The cover has the famous postcard style Georgia typography. In the second production, the book was case-bound and UV-printed on velvet to simulate a real peach’s tactility. When viewed from the side, the book is a graph in time, showing that with increased peach acreage came increased slavery from 1790–1920. When the book is open, the viewer first encounters a top-view photograph of a peach grove. As you go deeper into the book, the pages slowly reveal the horrific history of Georgia’s slavery. The data story culminates with an image of the illustrated plan of a slave ship from 1790. The connection between the aerial view of the first and last pages hint at the equivalence between the commodification of peaches and the commodification of human life.

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Figure 6.9 Correlating the datasets of number of slaves and peaches with the number of pages of the book, by Sarthak Kathuria. Figure 6.10 Sarthak Kathuria, structuring the pages with datasets: this was probably the funnest exercise in the process, as it was converting the data into the whole tactile experience of the book. This is how the measurements of the pages were decided.

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Figure 6.11 Sarthak Kathuria, rough 3-D sketches: before the final double-door design of the book was designed, a few other ideas were tried, along the same lines of trying to make datasets tactile in the experience of a book.

Figures 6.12, 6.13, and 6.14 Sarthak Kathuria, greetings from Georgia - the final outcome: it takes the shape of a souvenir book, with the aim of informing tourists about the true implications of the overly glorified “Georgia peach” as we dig deeper in time (1790-1920). The book is perfectbound with thick cardboard material for its pages. The cover bears the famous post-card-y Georgia typography. The cover is made to mimic the texture and color of the peach skin.

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W.E.B. Du Bois and black America Representing the same span of time as Greetings from Georgia, scholar, writer and civil rights leader W.E.B. Du Bois created two impressive sets of infographics for the American Negro Exhibit at the Paris Exhibition of 1900. The two related sets of visuals were created in collaboration with the Atlanta University sociology department by utilizing census data and original research, tracing information from the slave trade to beyond Emancipation. The Georgia Negro: A Social Study represents a focused study of the largest black population in any US state, consisting of twenty-nine brightly colored diagrams, three maps and four tables. A Series of Statistical Charts Illustrating the Condition of the Descendants of Former African Slaves Now Resident in the United States of America includes an additional twentyseven diagrams. Du Bois and his team intended to use Georgia’s diverse and growing black population as a way to demonstrate the progress made by African Americans since the Civil War (Du Bois 2018). Created twenty years before the Bauhaus design school was founded, the modular style of the infographic collections offers foreshadowing of the organized structure and functionality to come. The minimal typography and pictograms also precede Isotype and the International Typographic Style. Through the use of maps, circle and

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spiral diagrams and bar and area charts, Du Bois and his team presented social data including education, economic class and race. Like Kathuria’s project, the information has an intentional order to it that is both narrative and persuasive, while also embracing the perfectly impact nature of hand-made design. Some of the graphics include also photography of black family daily life, mostly in portrait form – a contrast to the disturbing slavery images from Greetings from Georgia. The level of collaboration and co-creation by Du Bois’ team is impressive, highlighting innovation in the social sciences complemented by forward-thinking aesthetics. As Silas Munro notes, “these visualizations offer a prototype of design practices that were not widely utilized until more than a century later, anticipating the trends – now vital in our contemporary world – of design for social innovation, data visualization in service to social justice, and the decolonization of pedagogy”(Du Bois 2018). Both Du Bois and Kathuria offer perspectives of the often concealed and repressed stories of black Americans. It is impossible to know for certain what future plans Du Bois may have had for the infographics, but it is clear that they take on new potential life today to inspire creative forms of social justice and a form of accountability in the age of Black Lives Matter (Du Bois 2018).

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W.E.B. Du Bois and black America

Figure 6.16 Two maps of Georgia from 1870 and 1880 respectively, with colors indicating the number of African Americans in each county.

The use of data visualization to illustrate social condition is critical and requires dedicated collaboration across disciplines that Du Bois knew how to coordinate successfully. As BattleBaptiste and Rusert note, “the Du Bois data portraits reflect a moment just before the disciplines had hardened into the academic specializations and structures of knowledge that we are familiar with today. The crossfertilization of visual art and social science here marks an important transitional moment in the history of the disciplines while offering alternative visions of how social scientific data might be made more accessible to the populations and people from whom such data is collected”(Du Bois 2018). His collection is a welcome reminder of the power of data to tell important stories about marginalized communities.

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Figure 6.17 Graph showing percentage of freemen and slaves among American Negros between 1790 and 1870.

Figure 6.18 Graph showing money spent on rent, food, clothes, taxes, and other expenses representing 150 Negro families of various income brackets.

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The influence of isotype As seen earlier in the chapter, Kathuria’s work challenges the audience’s innocent beliefs surrounding the meaning of the peach as a symbol. While the peach is iconic to a specific audience, symbols can be much farther-reaching. As a critical way of expressing universal ideas, icons have a long history and have experienced a resurgence as information design becomes more prevalent. One of the most common ways of efficiently visualizing information is through the use of icons, pictograms and even emojis as a means of communication shorthand. Many contemporary icon systems can be used universally, free of any language barrier. Even before writing, forms of icons and pictograms trace back to cave paintings and hieroglyphics. Otto Neurath developed one of the first attempts at making an organized picture language to meet societal needs. Neurath’s International System Of TYpographic Picture Education, or Isotype, was created in Austria after the First World War to educate the people of Vienna and elsewhere on social and economic issues and catalyze social reconstruction.

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Throughout graphic design history, developments have come from changes in society and their consequent evolving needs. After the collapse of the Habsburg Empire, Vienna and Austria experienced dwindled resources and shortages of food and housing. With deteriorating public health and increasing inflation, Neurath sought to empower citizens with information by making comparisons between the present and past conditions of Vienna and other cities and believed that picture language was useful in making these connections (Twyman 8). Using a picture language, he thought it to be an effective communication method across a wide range of ages and abilities, speaking as clearly to a young child as an intelligent adult. Additionally, picture language had the benefit of being able to be presented linearly or in alternative formats for enabling comparison between items. Isotype was never intended to replace written language fully; instead, Neurath saw it as a “helping language” to supplement written words (Figures 6.19 and 6.20). Acknowledging that some things could not translate well to pictures, and others could become more meaningful and memorable through pictures, Isotype’s usage was admittedly circumstantial (Twyman 1975).

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Figure 6.19 An example of a complex Isotype chart with side by side charts of the United States and United Kingdom. The dates run from top to bottom, descending from the second industrial revolution. To the right of the dates (and reading left to right) are icons representing political power, then black squares representing “pig-iron” production used in iron and steel. The red horizontal bars represent years of war. While there is plenty of detail and narrative to unpack, the main takeaway is that the UK is generally more stable and also more contained (Jason Forrest).

A key characteristic of Neurath’s work on Isotype that is still critical today is a need to establish conventions to communicate easier and more effectively. While his rules for Isotype appear in his book International Picture Language, the underlying principle is that consistency facilitates understanding. When a visual system is consistent, the audience can get used to how information is structured, information assimilates more quickly and can make comparisons more

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readily. The rules of consistency apply to all aspects of data graphics and help to build trust in an audience while ensuring that data interpret as accurately as possible. Stylistic considerations reinforce the notion of a uniform language through the repetitive use of line weights, shapes, boldness and detail (Lupton 56). An Isotype character is in general easily recognized, concise and repeatable, lending itself to relative neutrality in communication.

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Figure 6.20 This spread by Neurath shows his visual work in addressing public health. Tuberculosis, from 1939, was produced from the National Tuberculosis Association and designed by Neurath’s International Foundation for Visual Education.

To further the quest for Isotype to serve as a universal picture language, Neurath and his wife Marie established the Isotype Institute in London in 1942 (Lupton 1986). It can be argued that the Isotype team was ahead of their time in developing a clear design process and interdisciplinary team. Economists, historians and statisticians collected data; “transformers” were in charge of organizing the information, applying Isotype design rules and assuring that the symbols communicated the information clearly (Twyman 1975). The “transformers” were a form of graphic

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editors, mediating between the scholars and artists. Graphic artists drew the symbols and finalized compositional placement, and technical assistants would paste down symbols, print and perform other production tasks. This whole team of visual languagemakers created numerous flat, simple silhouettes that are immediately understood as a temporary impression of an actual object rather than a detailed depiction. The consistency of creating and applying the symbols allows for them to become a convention, as is the case with airport restroom and baggage

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claim signs. Most travelers have already unlocked the message behind these standard symbols and have committed them to memory. Each time the signs are encountered, they reinforce what they represent (Lupton 1986). Isotype has been used to depict numerous sociological data sets for museums, books, posters and pedagogical materials (Lupton 1986). Its legacy includes the design of charts as well as comprehensive symbol sets used to unite populations. Twyman notes that “Isotype demonstrates that successful designing depends on clarity of thinking and provides the view that the graphic designer’s primary role is to serve the needs of society” (Twyman 1975). Isotype and contemporary icon sets inspired by the work of the Neuraths are common. However, the prevalence of websites with free downloads or large icon libraries like The Noun Project makes it difficult for the novice information designer to adhere to the Isotype ideal of consistency in form. When designing, it is critical to keep an eye on consistency in icon aesthetics (discussed further at the end of the chapter). When consistent, information design can significantly influence a viewer’s understanding and interest in a topic. Cheng, Chen, Larson and Rolandi (2017) examined how redesigned graphic abstracts influenced readers’ expectations of scientific papers. The goal of their study was not to alter the message, just to communicate the same information with better visual design (Cheng et al 2017). In some ways, the process of redesigning the abstracts mirrors the process followed by the Isotype Institute:

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1. Evaluate – read and research, develop an understanding of the content (Isotype scholars and researchers) 2. Edit – determine the most critical information, what needs to be shown and how (Isotype “transformer”) 3. Compose – design and organize in space (Isotype designer) 4. Refine – simplify, label, group (Isotype technical assistant) Some of the conventions that the redesigns addressed include having a start and endpoint, flowing left to right and top to bottom, creating logical groupings and visual hierarchy and highlighting key findings with visual contrast (Cheng et al 2017). Reviewers of the original and redesigned graphic abstract responded to a series of statements, resulting in all redesigns scoring higher than the original, with the most significant improvement coming from a perceived understanding of the paper. The results align with past psychological studies of fluency and perception and reinforce the benefits of interdisciplinary collaborations between science and design. Scientists that utilize accessible, well-crafted visuals will reach broader audiences and have more significant potential for change and future discoveries. While the following example is not one of the redesigns from Cheng et al., it accomplishes a similar goal of easing comprehension of scientific information.

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Case Study: Institute for Disease Modeling, Malaria Lifecycle Background: The Institute for Disease Modeling (IDM) is part of the Intellectual Ventures’ Global Good initiative based in Washington. Funded by Bill Gates and co-founded with Nathan Myhrvold, Global Good strives to invent technology that could solve some of the world’s most formidable problems, including issues of global health and global development, particularly in underserved areas of the world. On the global health and technology side of Global Good’s mission, IDM “focuses on creating powerful and innovative disease modeling and data analysis tools, and software to help the global health community evaluate health policies and intervention strategies”(idmod. org, 2018). To better foster these collaborations for researching new ways to understand and combat disease, IDM shares their online research models and simulation software with their partners to widen the global health community’s evaluation of various combinations of health policies and intervention strategies. Since IDM had determined that their software was underutilized, they suspected it was because their instructions were exclusively text-based. The scientists thought that incorporating visuals into their guidelines would aid in better communication about their software offerings. The Institute for Disease Modeling (IDM) needed to create a visual overview of malaria to explain IDM’s online malaria simulation. The simulation software replicates malaria conditions to evaluate the effectiveness of eradication approaches. The program is highly customizable. It

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allows users to add various interventions to their simulation, such as antimalarial drugs that can be specified to target specific populations of humans or mosquitoes. The software can also account for external variables, including transmission intensities, mosquito behavior and seasonally driven environmental factors. The goal of the visual was to quickly summarize the malaria lifecycle to users (primarily students and scientists doing research studies) and the potential data entry points they could use in the simulator. Process: As experts on the simulator, IDM’s team of malaria research scientists created a first draft of the malaria lifecycle. The model’s variables were relayed to the designer by the science writer. The senior science writer and primary collaborator with the designer in the project served as an “interpreter” of scientific terminology. For the weeks spent creating the malaria overview, she spoke with a team of scientists studying malaria to verify the details of the information in the initial chart (Figure 6.21), including clarifying terms and intended interactions between nodes. The biggest initial hurdle that the design and writing team confronted was the fact that the first draft of the malaria overview was created by scientists for scientific purposes. The terms, arrows and perceived interactions were logical and perhaps routine within their specialized group of study, but not widely usable to others. The collaborative process between different scientists, the scientific writer and the designer involved in the malaria

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Figure 6.21 The original graphic provided by scientists to explain the malaria lifecycle. It was critical to understand proper sequence and relationship between elements.

lifecycle project required many iterations to communicate clearly. Through the multiple feedback cycles, specificity in questions was vital in keeping the project moving smoothly and efficiently. Minor changes such as color or positioning were labeled as such in emails, while more significant conceptual changes were noted with detailed clarifying questions. Inquiries focused on getting precise messaging from the scientists, not advice on how to portray it visually. At the onset, ignoring aesthetics was essential until we could establish accuracy in vocabulary, the relationship of items on the chart and the flow of the overall

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process. By not talking about design, and focusing on meaning first, the team was able to focus on the goal of the visual in speaking to the target audience. When questioning scientists about their research process, the focus was on possible areas that could confuse others (as shown in Figure 6.22). Some questions asked early on included: • “In the chart, fever, anemia, and mortality each have a bubble with an arrow leading to ‘symptoms’ – are those just listed symptoms? Right now, the arrow indicates some sort of effect, and I’m is that what is really meant?”

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• “The ‘immunity’ bubble has a series of bubbles off of it with double arrows – what is that relationship?” • “What is the message of the large red interventions octagon in terms of how it interacts with the other various bubbles?” • “‘Human infection’ and ‘vector infection’ are purple instead of blue – what is the significance?” To build a level of understanding, the goal was to start to transform from the appearance of a brainstorming web to evidence of event sequence and cause and effect between items. It can be incredibly difficult to decode a visual knowing that there is zero significance to the sizing, spatial relationship and arrows or color provided. Still, information accuracy is critical first and foremost. By asking questions that helped the client

explain what they knew as the malaria lifecycle, it became easier to assert the value of fundamental design principles to communicate what they were describing. After this first series of questions, a draft of a chart was created using what was understood from their answers and isolating some areas that still lacked clarity on the bottom. It was still unclear how interventions and immunity fit into the picture (seen at the bottom of Figure 6.22). It turned out that part of that confusion came from the fact that it was not a linear process and could happen at various points in the malaria life cycle. It only could not be part of a sequential, traditional flowchart. Once the order of events and the subsets of items were presented correctly, it was easier to research some of the chart’s specifics to see if there were better ways to visualize the relationships.

Figure 6.22 Final malaria lifecycle overview chart.

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As the design process continued, it became clear that some relationships were more cyclical than linear, and others were more in the background of the actual malaria transmission. As Figure 6.22 demonstrates, the mosquito lifecycle and mosquito feeding/infection cycle took the forefront as critical cyclical processes. In contrast, the habitat took on a background role that climate could contribute to promoting the mosquito lifecycle. These simple visual cues of the proximity of objects, foreground versus background and using appropriate groupings suddenly helped transform the scientific chart into something understandable and more engaging. At a glance, it is evident that some event sequences happen in a recurring nature, while others have one-way progressions or reciprocal interactions. Mosquito control and human-based interventions fell onto their layer as something that can coincide as malaria transmits and spreads. Likewise, the climate has its own layer showing that it occurs at the same as the mosquito/vector lifecycle and mosquito control intervention.

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Subsets of information were moved under appropriate categories to show their association with one another rather than having misleading arrows attached to them. As Edward Tufte (2018) notes in his acclaimed book Envisioning information, “information consists of differences that make a difference.” Every tiny aesthetic change has convincing consequences for information design and are thoroughly tested worldwide by artists, designers and architects. The scientists’ satisfaction with the accuracy of the chart allowed more freedom in the aesthetic component of the process. Colors and type choices acknowledge IDM’s preexisting branding and digital content categories. IDM’s primary logo color is the blue used for the icons, which are universal for use across their graphics for research papers and software, while the green arrows tie with the green category color used for malaria content throughout the IDM website. These simple aesthetic details are thoughtfully selected, with no extraneous information or graphics to clutter the space or disrupt the flow of information.

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Methods and tools – making better icon systems While I am not suggesting that iconography is necessary for all information design, it is common for icons to be used but relatively uncommon for them to implement thoughtfully and consistently. Head of content for Iconfinder.com, Scott Lewis (2016), proposes a loose guide to building better icon sets. He argues that the three main features of an effective icon system are: ●

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Form: details aside, icons should be first constructed from simple shapes – circles, squares and triangles – to establish a foundation. With simple shapes created, details can then be added as needed only until the icon reaches a level of effective communication of what is being represented. Aesthetic unity: arguably the most commonly overlooked feature, though critical in creating cohesion in a visual, aesthetic unity is the shared elements within an icon set. These aesthetics include rounded or square corners, the size of corners, line weight, color palette and style (flat, line, glyph, gradient). Recognizability: how well the audience can comprehend the object, idea or action represented. Generally speaking, icons ideally take up a square’s worthy of space (total height is similar to total width). This allows for placement flexibility that is minimally intrusive to a design when an icon is used repeatedly as a visual reference.

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The following set of icons (Figure 6.23) was created for the Fifth Wave project from Chapter 3. To create a cohesive set with unifying aesthetics, the focus was first on having a feel of approachability and even youthfulness to them. To achieve that feeling a combination of soft corners, rounded composition, bright colors, proportionate stroke width, and slightly offset color fills were used. Knowing that an icon system would be an important part of the overall design, a hierarchy was established with large circular dual-icon graphics for section topics followed by slightly more generic sub-category icons that appear within each of the larger section topics (turning point, the journey and marching on). A tertiary set of smallest icons focus on key women trailblazers within the broad categories of writer, innovator, businesswoman, politician, activist or healthcare worker. The icon system is immediately identifiable lending itself to successful usage across mediums. Selecting and properly using a reliable data set is critical in information design, but so too is creating a reliable intuitive visual system to pair it with.

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Methods and tools – making better icon systems

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Figure 6.23

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Visual cues

References “About.” Institute for Disease Modeling, 2018. https://www.idmod.org/. Andrews, R. J. Info We Trust: How to Inspire the World with Data. Hoboken, NJ: Wiley, 2019. Bertin, Jacques. Semiology of Graphics: Diagrams, Networks, Maps. Redlands, CA: ESRI Press, 2011. Cheng, Karen, Yeechi Chen, Kevin Larson, and Marco Rolandi. “Proving the Value of Visual Design in Scientific Communication.” Information Design Journal Information Visualization 23, no. 1 (2017): 80–95. https:// doi.org/10.1075/idj.23.1.09che Du Bois, W. E. B., Whitney Battle-Baptiste, Britt Rusert, Aldon D. Morris, and Mabel Wilson. W.E.B. Du Bois’s Data Portraits: Visualizing Black America: The Color Line at the Turn of the Twentieth Century. Amherst, MA: The W.E.B. Du Bois Center at the University of Massachusetts Amherst, 2018. Dykes, Brent. Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals. Hoboken, NJ: John Wiley and Sons, Inc., 2020. Knaflic, Cole Nussbaumer. Storytelling with Data: a Data Visualization Guide for Business Professionals. Hoboken, NJ: Wiley, 2015. Lewis, Scott. “6 Easy Steps to Better Icon Design.” Smashing Magazine, May 11, 2016. https://www. smashingmagazine.com/2016/05/ easy-steps-to-better-logo-design/.

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Li, Yung-Ming and Yung-Shao Yeh. “Increasing Trust in Mobile Commerce through Design Aesthetics.” Computers in Human Behavior 26, no. 4 (2010): 673–84. https://doi. org/10.1016/j.chb.2010.01.004. Lupton, Ellen. “Reading Isotype.” Design Issues 3, no. 2 (1986): 47. https://doi. org/10.2307/1511484. Margolin, Victor. “Graphic Design Education and the Challenge of Social Transformation.” In Developing Citizen Designers, by Elizabeth Resnick, 14–15. New York, NY: Bloomsbury Academic, 2016. Moys, Jeanne-Louise. “Visual Rhetoric in Information Design.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 205-220. London, UK: Routledge, 2017. Pettersson, Rune. “Gestalt Principles.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 425–434. London, UK: Routledge, 2017. Tufte, Edward R. Envisioning Information. Cheshire, CT: Graphics Press, 2018. Tversky, Barbara. “Diagrams: Cognitive Foundations for Design.” In Information Design: Research and Practice, edited by Alison Black, Paul Luna, Ole Lund, and Sue Walker, 349–360. London, UK: Routledge, 2017. Twyman, Michael. “The Significance of Isotype.” ISOTYPE Revisited, 1975. http://isotyperevisited.org/1975/01/thesignificance-of-isotype.html.

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Measuring impact with Laura Willis, PhD

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While the success of design projects is notoriously difficult to measure and often brushed off as purely subjective, there are ways to measure impact if thoughtfully integrated into the design process. Although it is impossible to collect meaningful data on every person that comes into contact with a visual, online or in print, that does not mean we should abandon the effort altogether. In this final chapter, I practice what I preach in taking an interdisciplinary approach to projects. I am not a statistician, and I am also not an expert in implementing a comprehensive evaluation. For that, I will turn to my friend and colleague, Dr. Willis. Laura E. Willis, Ph.D., is an associate professor of health and strategic communication at Quinnipiac University, where she teaches communication research methods and strategic health communication courses. I asked her to describe the evaluation methods that information designers should be considering, common mistakes and key considerations. I have included two final contrasting case studies followed by Willis’s thoughts on how one could evaluate them. These suggestions are

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intended as a way to help information designers think about how to not only design with socially motivated goals but also confirm that they are making a positive impact. I am not suggesting that it will always be possible or that you will be able to convince team members to conduct testing. We should, however, be evaluating our work whenever possible. Not only will it confirm or deny that design was effective, it will also validate the critical work of visualizing information and hopefully broaden its practice. This chapter offers an introduction to key terms and the basic mechanics of social science research and evaluation processes. With the information provided here, design practitioners have a basis for understanding how such practices can be applied to examine the impact of their work. Ideally, this new knowledge encourages designers to begin thinking about engaging in the evaluation process themselves and feel more comfortable discussing evaluation efforts with trained researchers. In the following section, options for evaluating the case studies “Bruises” and “Bussed Out” are presented.

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Case Study: Nadieh Bremer (and Shirley Wu), The Guardian, “Bussed Out” Process: Funded by a grant from the Gates Foundation, The Guardian US has a section on its website entirely dedicated to houselessness in America. “Bussed Out” represents the flagship piece based on eighteen months’ worth of data collection from cities and shelters totaling around 34,240 bus ticket journeys. Essentially, major cities across the country were giving out one-way bus tickets to people experiencing houselessness to get them out of their city. The tickets had a variety of restrictions, and The Guardian became interested in the story and circumstances surrounding where the houseless people were ultimately going. Using data gathered primarily with Excel and PDFs by the Guardian team, the first hurdle was consistently formatting the data because every city had its own way of tracking. For the journey data to be correctly mapped, there needed to be an actual US city name, free of typos, and formatted the same (for example, Philadelphia versus Philly or PHL). To tackle the data and start composing a story, a two-day meeting was set up with the Guardian team. For the prior month, Bremer prepared all the data in R and ran some preliminary analyses looking at age

distribution, gender and most popular cities to go to, as well as any other potentially interesting insights that she could draw. In the meetings, reporters would suggest different hypotheses, and Bremer would see if the data supported them. Throughout this exploration process, they were cautious to acknowledge the fact that there was a certain potential margin of error in the ticket numbers. At the end of the first day, they knew what story they wanted to tell and how they wanted to break it into parts. The second day of meetings focused on which media would be necessary to tell the story – which data visuals should represent for each section, which photos, interviews, section titles and general conclusions should be drawn. To come up with ideas for visuals, Bremer relied on sketching and focusing on the goal that she wanted the visual to show considering what data she had available, and what she wanted the audience to learn. The practice of sketching, she says, frees her from the constraints of the computer and lets her explore creative ideas beyond standard chart options. She attributes her more creative graphics to becoming proficient in D3, allowing her to do things that aren’t possible in other visualization tools (Figures 7.1, 7.2 and 7.3)

Figures 7.1, 7.2, and 7.3 Concept sketches by Bremer.

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Figure 7.3

With everyone on the same page for the overall content of the feature, Bremer and Wu divided the visuals to work independently with weekly checkins for feedback and status updates. At this point, they had already agreed on initial styling, sketches and ground rules for code formatting so that all the pieces would seamlessly work together visually in the story. Once all of the visuals were complete, Wu put all the pieces together on a single page, made sure it was in line with Guardian styling and addressed technical details like mobile optimization. The final story incorporates motion that allows the viewer to feel the movement of people from one place to another.

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Integrated within the data visuals are personal stories of houseless people in the United States. The personal accounts follow some of the people with a line meandering across the country, while others are featured in emotional video content. The effect of merging raw numbers, motion, photos and personal videos is incredibly powerful and makes the viewer feel the story. The narrative is presented in a linear format. It draws in the audience to both become aware of the issue and the people affected and then offers a chance to act by ending with a donation area for supporting affordable housing with the National Low Income Housing Coalition (Figures 7.4, 7.5, 7.6 and 7.7).

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Figure 7.4 Many of the graphics in the Bussed Out story have movement that captures the essence of migration. In this example, users can follow San Francisco’s homeless population over time as they are bussed in and out of the city.

Figure 7.5 This radiating circle chart shows the people and institutions who receive homeless travelers, beginning in the center ring with those who are closest to the individual (parents and children). Subsequent rings include sibling and grandparents, other relatives, friends and acquaintances, and institutions or other.

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Figure 7.6 “The folks who so often fall into homelessness come from communities that have been experiencing a Great Recession for decades, living in neighborhoods with broken or absent support systems, enervated public schools, and little or no economic prospects to lift themselves up beyond their current circumstances,” said Arnold Cohen, president and CEO of The Partnership for the Homeless in New York (The Guardian).

Figure 7.7 This chart represents the approximately 650 people that were flown to foreign countries from New York City alone. Destinations include Dominican Republic and Mexico all the way to the Philippines, Guan and New Zealand.

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Figure 7.8 Using public records, the Guardian team collected data from 16 cities and counties that give homeless people free bus tickets to live elsewhere. The size of the circles on the map represent the number of homeless arrivals at destination cities (of 21,400 obtained records). The bar chart tracks the distribution of the total number of tickets per month over a 6-year span (2011-2017).

“Bussed Out” is a compelling narrative for presenting an issue that affects an alarming number of Americans and it will be utilized within this chapter to provide tangible examples of evaluation concepts. The next case study is a much smaller scale of data collection. As will become evident throughout the chapter, there are projects where evaluation guided by the social scientific methodologies are more and less appropriate. The chapter

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will conclude with the discussion of a second case study on the “Bruises” project by Giorgia Lupi. This project serves as a therapeutic process of humanizing the typically cold raw medical data of a friend’s daughter coping with a rare condition and demonstrates how the evaluation processes discussed within this chapter may not be suitable, or a smart use of resources, for all design for social-good projects.

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Goals and reach Current measures of success mentioned by the practitioners interviewed for this book included awards, adoption of the message by policy makers, nonprofit organizations and/or government officials and publisher feedback regarding the piece. While these can be used to signal success, the consistent application of any of these measures to each piece of design work is not possible. Instead, the success of a visual message can be managed in more a consistent and scientific way by adopting the evaluation practices from strategic communication fields, such as public relations and health communication. As some UX research and human-centered research closely mirror the methods discussed in this chapter, you will likely recognize many of the concepts presented and already feel comfortable thinking about moving beyond the evaluation of the aesthetics of your design work to consider how a piece impacts your audience. To begin the process of developing an evaluation plan, there are a few key questions that must be answered:

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1. What specific group(s) of people are the target for this visual message? 2. What is the ultimate goal of having said people engage with the message? and 3. What evaluation criteria can be used to determine if the goal has been met successfully? Once those big picture questions are answered, decisions regarding the research method(s) to use to determine effectiveness can be more informed, and therefore evaluation planning will be more time and cost efficient. Evaluation research allows designers to learn what works for an audience and what misses the mark, and by integrating the knowledge gained from previous evaluation work into future projects designers are able to produce more effective pieces over time. Through consistent evaluation research, designers can provide evidence of their previous successes to their clients while demonstrating the intentional and strategic nature of their work.

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Goals and reach

In determining the specific groups, or target audiences, both demographic and psychographic variables should be considered. Demographics provide boundaries for a target audience, dividing a population by factors such as gender, race or age, and they can be an excellent starting point for audience segmentation. However, as not all members of one demographic group think, feel and behave in the same way, psychographics, which speak to values, attitudes and habits, provide a more nuanced understanding of the target audience. Through the complementary use of demographics and psychographics, target audiences can be identified and assessed – before, during and after the life of a message. It is important to note that even when working with clients who intend to reach a vast, general public audience, such as The Guardian in the “Bussed Out” project, every publication has segments of the public they are more and less likely to reach. When beginning a new project, designers may want to inquire with the client what, if any, audience demographic and psychographic information they have available.

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Before a message can have any effect on an audience, the message must reach that audience. Exposure to the message can be assessed through reach – how many people were exposed to the message, and impressions – the frequency of the exposure. This information may be readily available through media publishers and social media metrics and it can certainly be useful in ensuring that the message is reaching members of the target audience. To begin the conversation about evaluation from the start of a project, designers could inquire about how the organization measures exposure and who from the organization they should be connected with in order to receive reach and impression data for their piece(s) at the same time they request any available audience demographic and psychographic information from a client. While this data will be valuable, message exposure must not be equated to message receipt, and certainly not to message retention. Therefore, while an essential first step, simply reaching the target audience is not enough to deem a message effective. To determine effectiveness, whether the goal of the message is being achieved must be assessed.

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Design for social good inherently has a goal, whether that be to enhance knowledge, change or bolster an attitude or promote the adoption or cessation of a specific behavior. By identifying the ultimate goal of having a target audience engage with a message, you have highlighted the key concept(s) that must be measured in order to determine effectiveness. For example, if the goal is to enhance knowledge about an issue, the evaluation materials must assess what the target audience knows about the issue. Or, if the message is focused on changing an attitude (or behavior), effective evaluation must include attitudinal (or behavioral) measures. Looking back to “Bussed Out”, it would be appropriate to measure the audience’s knowledge about the practice of using one-way bus tickets to reduce the number of people experiencing houselessness in US cities, the audience’s attitudes regarding this practice and how it impacts the individuals who are being bussed out, as well as the audience’s donation behavior. Once a goal has been determined, it is helpful to set clear objectives, or evaluation criteria, by which success can be measured. By defining success in specific and measurable objectives, the relevant data become clear. For example, for the goal of enhancing the audience’s knowledge about the practice of using one-way bus tickets to reduce the number of

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people experiencing houselessness in US cities, an appropriate evaluation criteria could be a 20 percent increase in scores on a knowledge assessment by members of the target audience within six months of the message becoming available. Or, if a goal of the “Bussed Out” project is to promote empathy toward people experiencing houselessness, the evaluation criteria could be that 10 percent of the target audience report greater empathy towards that population three months after the message became available than before the launch of the message. These examples highlight how the development of specific, measurable evaluation criteria underscore what must be measured in order to determine message effectiveness, knowledge regarding the bussing practice and empathy toward people experiencing houselessness respectively. After determining target audience(s), the goal of the message, and how to determine whether the message was successful, then decisions can be made regarding research method, evaluation design and the materials that will be used in the evaluation. While the process for determining reach and impressions may differ by channel – be it print, web or social, the mechanics of evaluating the impact of a piece of design work discussed in the following, based in social science research methods, can be applied to content prepared for any channel.

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Research methods

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Research methods There are many methods by which to solicit feedback from your target audience. The selection of one method over another can be driven by a handful of factors, including the kind of information presented in the message and from whom you are seeking feedback, as well as how much time and budget is available for evaluation. Each method has its own unique mix of advantages and disadvantages. The question guiding the research should be a primary factor in determining the appropriate research method, and strategic communication and marketing research firms can support the planning and management of these endeavors. Qualitative research methods such as interviews, focus groups, field observation and diary studies result in an in-depth understanding of participants’ attitudes, experiences and motivations. Generally associated with small sample sizes, selected through a non-random process, qualitative methods can provide detailed insight into the lives of the participants but cannot provide statistical inferences about the larger target audience. For example, focus groups, which are moderator-led discussions usually featuring fewer than fifteen participants, can be effective for copy and product testing. By encouraging honest feedback on multiple versions of a product or message, focus groups offer practitioners the opportunity to address areas of concern and avoid costly mistakes.

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Quantitative research methods are used to yield reliable statistical data. Generally associated with a large number of randomly selected participants, the findings of quantitative studies, like surveys and experiments, can be used to make generalizations about the target audience overall. Careful attention must be paid to every step of the process, from questionnaire design and sample selection through data cleaning and analysis, for the findings to be valid and reliable. When conducted with the rigor of the scientific method, however, experiments offer practitioners the opportunity to assess causal relationships to determine the effect of their design work with confidence. Qualitative and quantitative research methods result in very different types of data, which require specific data analysis techniques. The transcription and analysis of qualitative data can be time-consuming but can result in findings that are rich in perspectivetaking opportunities. The cleaning and analysis of quantitative data sets requires an understanding of statistical analysis, while offering the ability to mathematically assess trends and relationships between variables. Research firms employ researchers who specialize in specific methods and their related analysis techniques.

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Table 7.1 Type of concepts measured

Access to target population / Sample size

Exploration, Description

Attitudes, beliefs, experiences, meanings.

Requires access to representatives of population, but random sampling not required. Sample size can be relatively small.

Requires a good deal of time, but not necessarily a good deal of money.

Qualitative, can provide quantitative data if running many focus groups.

Exploration, Description

Attitudes, beliefs, experiences, meanings.

Requires access to representatives of population, but random sampling not required. Sample size can be relatively small.

Requires a fair bit of time, as well as a space to hold the focus groups. Financial considerations should include paying a trained moderator, renting an appropriate space, financial incentive for participating.

Field observation

Generally qualitative, can record quantitative data as well.

Exploration, Description

Who, what, when, where, and how people interact; exposure and engagement with messages.

Requires access to representatives of population, but random sampling not required. Sample size can be relatively small.

Requires a fair bit of time and access to the observation space, but not necessarily a good deal of money.

Diary studies

Generally qualitative, can record quantitative data as well.

Exploration, Description

Exposure and engagement with messages; experiences, meaning.

Requires access to representatives of population, but random sampling not required. Sample size can be relatively small.

Usually diary studies run for a fair bit of time. Primary financial consideration is incentivizing participation.

Qualitative vs. Quantitative

Purpose of study

Interviews

Qualitative.

Focus groups

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Resource Requirements

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Type of concepts measured

Access to target population / Sample size

Exploration, Description

Nature of messages, structure of messages, frequency of messages; non-causal relationships between message characteristics.

Does not require access to individuals, but instead access to materials. Quantitative content analyses require larger samples than qualitative content analyses.

Requires a fair bit of time and access to the materials, but not necessarily a good deal of money. Primary financial consideration is paying coders for quantitative studies.

Generally quantitative; can collect some qualitative data through openended questions.

Description

Attitudes, beliefs, behaviors; non-causal relationships between concepts

Requires access to representatives of population, and random sampling is required to generalize beyond the sample. Larger sample size preferred.

Time and financial considerations are controlled by research design decisions, such as channel (online, mailed, phone) or if/how participation is incentivized.

Quantitative.

Explanation

Attitudes, beliefs, behaviors; causal relationships between concepts.

Requires access to representatives of population, and random sampling is required to generalize beyond the sample. Larger sample size preferred; sample size dependent on number of conditions.

Quite resource intensive.

Qualitative vs. Quantitative

Purpose of study

Content analysis

Both types of content analysis exist. Qualitative content analyses employ thematic analysis and inductive reasoning, whereas quantitative content analysis require a pre-developed and reliable codebook.

Survey

Experiment

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Resource Requirements

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Evaluation design Evaluation can occur in several phases, including before, during and after the life of a message. The information collected in these phases offer different insights and opportunities from each other. Common evaluation designs include post-test only, pre-test/posttest, pre-test/post-test/control, and pre-test/post-test/follow-up. With each point of data collection, the resources required to complete the evaluation increase, as do the strengths of the evaluation plan overall. A post-test–only design collects all data at the end, and without any points of comparison it is difficult to determine the magnitude of any outcomes or the cause of those outcomes. Therefore, at minimum, a pre-test/post-test design, where the same measurement instrument is used to collect data both before and after an interaction with the design piece, is suggested. Fairly simple to implement, this design allows practitioners to control for prior attitudes, knowledge and behaviors and provides better evidence of the effectiveness of the piece. Adding a control or comparison group to account for influences outside of the design piece strengthens the overall evaluation design and is critical to valid experimental design. Including a follow-up data collection point at some time in the future after the posttest offers practitioners the opportunity to assess any lasting impacts.

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Data collected before a message goes live, sometimes called formative research or baseline evaluation, provides information on the attitudes, knowledge and behaviors of the target audience prior to their viewing of or interaction with the piece of design work. This phase of evaluation is where A/B testing should be conducted. Assessing pieces of design work independent from each other during this formative research stage can illuminate the impact of each individual design piece within a larger project or media piece by eliminating the “noise” of the other components of the project as a whole, which cannot be removed once a message goes live. Of particular importance for evaluating effectiveness, findings from this phase of data collection can provide some evidence of change within the target audience when paired with impact/ outcome evaluation data. For example, baseline measures regarding how much the target audience knew about an issue, like the practice of using oneway bus tickets to reduce the number of people experiencing houselessness in US cities, prior to exposure will be helpful in assessing if any knowledge was gained by interacting with the “Bussed Out” story.

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Evaluation design

Depending on the specific piece of design work to evaluate, it may make sense to collect evaluation data at multiple points across the life of the message/work. In-progress monitoring would be particularly valuable for pieces that are expected to be attended to or interacted with more than once across time. Collecting key attitudinal, knowledge and behavioral information over the lifespan of the piece, instead of waiting until the end, can allow practitioners to determine if the piece is reaching the intended target audience and if it is having the intended impact. This practice creates opportunities for adjustments to be made that would otherwise be missed. In-progress monitoring can also create the ability to track the effects of the piece over time. Tracking over time allows you to confirm the message is reaching its intended audience through exposure data and highlights if changes in attitudes, knowledge or behavior occur after an initial interaction or accrue over multiple interactions with a piece, as well as if and for how long such changes are sustained. This monitoring can be done in either a trend or panel design. A panel study requires access to the same sample of respondents at the data collection point. This can be quite costly but allows you to speak to how individuals change over time. Trend studies, on the other hand, assess different independent samples of respondents at each data collection point, allowing you to speak only to population-level changes.

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Assuming unlimited resources, the “Bussed Out” project’s evaluation design would implement formative research that utilized content-specific knowledge assessments related to each data visualization piece in a pre-test/ post-test/follow-up evaluation design. By doing this evaluation work prior to publishing the project online, evaluators would be able to determine how impactful each piece was on respondent learning, both in the short-term (often within minutes of interacting with a piece) and longer-term (usually at least one week after interacting with the piece). This evaluation plan would also allow for comparison of the individual pieces, which may suggest which types of visualizations are more or less likely to impact learning, and it may highlight issues or areas for improvement that could be adjusted to the individual pieces prior to the release of the story by The Guardian. The formative research would also capture more general baseline measures of the key goals for the project, such as the public’s knowledge of the practice of using one-way bus tickets to reduce the number of people experiencing houselessness in US cities, the public’s attitudes regarding this practice and how it impacts the individuals who are being bussed out, as well as the public’s current donation intent and donation behaviors to relevant non-profits. The evaluation materials utilized to capture these baseline measures would be reused during in-progress monitoring and outcome evaluation to assess changes in knowledge, attitudes and donations behaviors over time.

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Once the project is publicly available online, the evaluation plan could also follow and examine the content of social media posts in which the webpage was shared. A content analysis of these posts could capture whether the poster simply shared the link, shared the link and a quote from the article or shared the link with their own thoughts, as well as which, if any, of the pieces of data visualizations (or their corresponding topics) are mentioned, the overall tone of the post (positive or negative) and the goal of the post (education, advocacy, and so on.) Of course, once the project is published, the evaluation of the individual pieces of data visualization would likely become too muddied, as the content of the webpage may be discussed as one overall piece.

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Another route for evaluating whether “Bussed Out” has reached its educational goals would be to identify and interview individuals whose work includes educating others about houselessness in the United States to learn their thoughts on the project, determine which, if any, of the data visualization pieces they found particularly effective and capture if they have shared, discussed or utilized any of the information from the project in their own work.

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Evaluation materials The specific materials required for evaluation will depend on the research method. Interviews and focus groups require scripts made up of open-ended questions and discussion prompts to guide conversations and may vary their flexibility of question order. Survey research requires the development of a questionnaire, which may include a combination of question types, such as multiple choice, Likert-scale, and open-ended questions. There are many issues to consider when drafting survey questions, such as writing questions that are easily understood (e.g., free of jargon, limiting use of negative terms like “not”), avoiding leading, emotionally-loaded, biased or doublebarreled questions, the implications of question order and context and ensuring that response options are exhaustive and mutually exclusive. Effective survey design and question writing is an expertise all its own and it is strongly recommended that questionnaires

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utilize previously established measures where possible, that questionnaires are pre-tested and/or that questionnaire development is guided by the expertise available at research firms. Diary studies and field observation may employ thematic analysis of notes or a codebook to capture when specific concepts are observed. Regardless of the specific materials required by the method, the purpose of these materials is to measure the key concept(s) that were determined to be markers of success. Again, if the goal of a piece is knowledge generation, then the materials developed must assess what the participants know about the issue; if the goal is behavioral modification, the materials must capture participants’ behaviors and/or behavioral intentions. Simply put, the specific persuasive, educational or informative goal(s) of the piece of design work must be captured by the evaluation materials to assess the impact of the piece.

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The process of developing reliable and valid measures is its own art and science. In particular, the evaluation materials developed for quantitative methods should be developed with an eye toward consistent and accurate results. Established scales, which have previously met the requirements of reliability and validity, exist for many attitudinal and behavioral concepts and are, therefore, preferable to the work of creating new measures. Regardless of method, all evaluation materials should keep in mind the target audience’s literacy levels and sense-making of relevant jargon and phrasing to ensure respondents understand the questions they are answering.

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To those without social science research experience, the evaluation process may seem daunting. However, an individual evaluation plan for an individual piece of design work does not require the incorporation of all the elements described previously. Instead, each individual evaluation plan should be informed by the specific work it is evaluating – its goals, target audience, the length of time it will be live and available to the audience, the resources available for the project and so on – and therefore, each individual evaluation plan can be developed in concert with those considerations. Just like any social scientific endeavor, the knowledge generated through evaluation processes should be understood to be tentative knowledge, knowledge that is everevolving in connection with findings that came before and those still to come and subject to human error at any step in the evaluation process. Therefore, the goal of any evaluation plan should not be framed as seeking proof but instead evidence that supports (or doesn’t support) the relationships being assessed. The acknowledgement that all evaluation plans will have their limitations may be used as an argument for avoiding evaluation all together; however, it also offers a freedom from the pressures of expecting perfection. When developing an evaluation plan, one should attempt to develop the best plan they can, staying mindful of common mistakes and key considerations while not allowing the fear of imprecision to immobilize their efforts.

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Common mistakes and key considerations An area of evaluation where avoidable mistakes commonly occur is in the construction of measurement items, such as the wording of a question in a survey questionnaire, the order of prompts in a focus group script or the rules for capturing the presence of a concept in a codebook for content analysis. When developing measurement items, it is important to determine that the items successfully measure what you intend for them to measure. For example, after giving a respondent time with a message, asking them “Did you learn anything from interacting with this piece?” will provide information regarding their perceived learning; whereas asking content-specific questions about the topic of the piece both before and after the respondent has engaged with it will provide a more accurate reflection of the learning that occurred. Additionally, it is important to avoid confusing knowledge measurement items and data with those that address attitudes, as well as confusing attitudinal measurement items and data for those that address behavior. While knowledge, attitudes and behaviors are all interrelated, none can stand in for the other. Therefore, if the ultimate goal of a piece is to change behavior, an evaluation plan must include behavioral measures.

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In addition to developing measurement items that accurately measure the key concepts (determined through the goal(s) of the piece), it is also valuable to identify related concepts that could impact your findings. Once such extraneous variables have been identified, they can be controlled for through either evaluation design or statistically in quantitative analysis. For example, respondents’ baseline data literacy levels would be of particular importance for the evaluation of any data visualization work. Measuring and controlling for such extraneous concerns allows for greater confidence in the assessment of the relationships between message engagement and the key concepts. Other extraneous concerns that can be measured may include time spent with the message, attitudes toward issues like those presented in the message and demographics.

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Finally, it is vital for practitioners to understand the capabilities and limitations of the various strategies that can be used to develop a sample. Simply because samples are made up of representatives from the target audience does not mean a sample will be representative of the whole target audience. Probability sampling strategies, which generally use a systematic selection procedure, allow for the calculation of the unrepresentativeness of a sample, called sampling error. Non-probability sampling strategies, such as volunteer or convenience sampling, also lead to sampling error; however, the degree of unrepresentativeness is inherently incalculable. If generalization is not necessary, non-probability sampling strategies are appropriate and are frequently used in communication research. However, if the generalization of findings beyond a sample is necessary, probability sampling should be employed.

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This chapter offers an introduction to key terms and the basic mechanics of social science research and evaluation processes. Equipped with the information provided here, design practitioners have a basis for understanding how such practices can be applied to examine the impact of their work. Ideally, this new knowledge encourages designers to begin thinking about engaging in the evaluation process themselves and feel more comfortable discussing evaluation efforts with trained researchers. That said, it is also valuable to appreciate the limits of evaluation work. In the next section, the “Bruises” case study is presented. Due to the personal nature of this project and the limited scope of its audience, it serves as a great example of a design project for social good that may not be suitable for the evaluation practices discussed in this chapter so far.

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Case Study: Giorgia Lupi, “Bruises” Purpose: In August of 2018, talented musician, composer and guitarist Kaki King woke up to find blood in the mouth of her three-year old daughter, Cooper. Upon closer look, the blood was coming from a dime-sized lesion on Cooper’s tongue. Shortly after, a blood test in the hospital emergency room revealed that she had a rare auto-immune disease called Idiopathic Thrombocytopenic Purpura (ITP), which caused her body to attack her platelets. As a result, Cooper would get spontaneous bruising and burst blood vessels called petechiae all over her body. For weeks, Cooper endured platelet transfusions, courses of steroids and blood tests as her parents monitored her rising and falling platelet levels, which play a critical role in blood clotting. Friend and Pentagram partner Giorgia Lupi wanted to “find a way to support or least to make clarity and sense to what was happening in their lives” by using her skills as a data visualization designer and artist. Lupi says that with her research and practice, she uses data as a tool to better understand human nature and data visualization as her expressive medium to narrate what she discovers (Lupi 2018). A new level of humanized insight was created to attach to the clinical records by collecting and analyzing data associated with the feelings experienced while coping with illness. The result transforms an overwhelming and disturbing time into one of understanding and beauty.

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Process: Since ITP presents the very visual symptoms of large bruising and petechiae, Kaki was directed by the doctors to watch Cooper’s skin for any significant changes. Having worked with Lupi in the past, Kaki learned to track personal data and began recording what was seen on Cooper’s skin, activities they did that day, any treatments that were received and the readings from them as to how Kaki herself was feeling. The thoughts and feelings included fear and hope on scales of 1–10 and personal written notes. Daily observations, therefore, included hard data from Cooper’s lab results juxtaposed with the soft data from Kaki’s life. This practice was designed to help “channel stress and anxiety into a semblance of control through a meditative action to make sense of what was happening” (Lupi 2018). With a collection of four months’ worth of data, Kaki and Lupi created both a musical score and a data visualization, respectively. Lupi began the visualization by creating a fluid timeline, with each white petal shape indicating a single day. The days group into clusters that represent the time between Cooper’s admissions to the hospital to have her blood checked, with each new grouping signaling a new lab test. The platelet counts from each of the blood tests are represented at the beginning of each cluster by red dots, with each dot equaling one platelet. It is important to note that the normal range of platelets in the blood is between 150 and 400, a startling range when it is clear from the visual that Cooper was far out of that range at times.

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With a timeline and blood tests mapped, Lupi then incorporated Kaki’ s daily observational data of Cooper’s skin. The purple and green splotches across the petal shapes represent the intensity of bruising, with larger size and more intense color indicating severity. The tiny pink dots on each petal shape represent the amount of petechiae (small purple spots caused by bleeding into the skin) present each day, with higher density indicating more visible spots that day. To show periods of steroid medications, grey brush strokes appear on top of the day. If some sort of incident

caused Cooper’s skin to worsen, that was shown through added colored pencil strokes. A black dot on a petal means that Kaki was away on tour and likely feeling stressed to be away from home. Any yellow areas represent positive bright times to brighten up an otherwise challenging four months. Finally, Lupi showed Kaki’s daily levels of hope and fear on a scale from 1–10 with floating lines that frame each day. Surrounding the data visual are hand-written personal notes from Kaki’s daily records (Figures 7.9, 7.10, 7.11 and 7.12).

Figure 7.9

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Figure 7.10 This graphic represents the fluid timeline component to the data. Each white “petal” represents a day, and a new grouping of petals begins each time there is a lab test to have Cooper’s blood tested. The platelet counts from the results are represented by the red dots at the beginning of the group. For context, the normal range of platelets in the blood is 150–400. At the beginning of this time period, you can read counts of 1, 7, 30; numbers that clearly have ties to the additional layers of data (shown in Figure 7.11)

Figure 7.11 This graphic shows the daily recorded data including skin observations, feelings, levels of fear, and treatments.

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Figure 7.12 The final visual combines a timeline with clusters of days between blood tests, layers of daily data collection, and hand-written personal notes by Kaki for each day with most defining words highlighted.

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As a beautiful companion to the data visual, Kaki composed a song based on Lupi’s artwork of her collected data. Kaki says that “the song has 120 measures in 3/4 time, covering the 120 days that I collected data. You will hear the elements that reference the amount of petechiae on her skin, my changing mood. . . . And the main guitar part is a musical map of the bruises on her skin, changing, bleeding into each other” (Lupi 2018). A full recording synchronized to an animation of the data visualization can be found on YouTube and Giorgia Lupi’s website (http://giorgialupi. com/bruises-the-data-we-dont-see). Data are more than quantified numbers. They come with layers of nuanced subjective information that help give a complete picture of the topic at hand. From the beginning, Lupi never intended this to be a scientific representation of data. Instead, it uncovers the sensorial softer data that accompanies the cold, sterile numbers of pathology. Pathological data is, of course, critical in the understanding and treatment of patients. Still, this project demonstrates that other data sets can enhance the lab data and help us empathize with what the patient and their loved ones are going through. We need to consider some of the imperfect and human qualities in the data that surrounds our every daily action and feeling. As the world becomes increasingly automated, Lupi’s work reminds us that behind the numbers is a very human context to be expressed and the quantitative can often benefit from qualitative considerations. Due to the personal nature of the “Bruises” project and the limited scope of its audience, it may not be necessary to explore any scientifically rigorous evaluation.

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That said, if Lupi and King were interested in exploring the impact of “Bruises,” it is still helpful to consider the goals and audience of the project. Lupi noted that she thought the project could help Kaki King “process and communicate her emotions.” Therefore, one “target audience” of the piece is King, and the goal for this audience is therapeutic. With the sharing of the project at the STIR Experience Lab and subsequently online, a second target audience, the public, emerges and with it, a corresponding goal. Returning to Lupi’s words once more, the piece is meant to “evoke empathy” through humanizing the data. To assess how successful “Bruises” was at achieving its first goal, King could be interviewed to ask how helpful the project was in her ability to process and communicate her emotional responses to parenting a child with an illness. Alternatively, any personal writings by King about the project, written either during data collection or since, could be examined using qualitative analysis techniques. When considering the second goal of generating an emotional, empathetic response from the public, the premiere could have offered a great opportunity for quasi-experimental design, with a pre-test/post-test/control evaluation design. Conference attendees could have been asked to complete a short questionnaire featuring items that would measure emotional responses to scenarios like Kaki and Cooper’s both before and after the conference session during which “Bruises” was premiered. If the post-test questionnaire also collected information on whether the respondent attended that session, it would then be possible to see if interacting with the piece impacted empathetic responses.

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Conclusion Design for social good is created with pro-social goals in mind. Unlike the “Bruises” case study, much of the design work presented in this book share a large, general public target audience, who they hope to impact through their projects. However, without proper evaluation, it is not possible to speak to whether their goals were met, for how many or to what degree. Any piece of design work can be evaluated in any number of ways, and each possible evaluation plan offers its own set of strengths and weaknesses. Therefore, evaluation requires the adoption of new, complex skill sets and/or the collaboration with trained researchers. The value of such efforts is complex as well. Evaluation can allow for the individual designer to improve their skills by highlighting what they are doing well and where they could use more training or experience. Beyond the individual designer, evaluation can allow for theory testing and generation of new knowledge that can inform future design work. Finally, evaluation provides evidence of success, which can be used to demonstrate one’s individual value, as well as the value of design work more broadly.

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References Bremer, Nadieh in discussion with the author, July 2019. Lupi, Giorgia. “Bruises – The Data We Don’t See.” giorgialupi.com, 2018. http://giorgialupi.com/bruises-the-data -we-dont-see. Outside America Team. “Bussed out: How America Moves Thousands of Homeless People around the Country.” The Guardian. Guardian News and Media. Accessed March 23, 2021. https://www.theguardian.com /us-news/ng-interactive/2017/dec/20 /bussed-out-america-moves-homeless -people-country-study.

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Image credits 1.6-1.8 From The Washington Post. © 2020. The Washington Post. All rights reserved. Used under license. 1.13 Bayer, Herbert; Container Corporation of America 1.14 Fox Inc. 2.1-2.5 Londono, Juanita. (May, 2019) Impact Over Form: An exploration on how to redeem the Ethical Designer. M.F.A: Graphic Design Visual Experience Thesis at The Savannah College of Art and Design. Savannah, GA. 2.6-2.8 Jan Willem Tulp (TULP interactive) 3.7-3.8 Treat, Jason / National Geographic Creative 3.9-3.13 Periscopic and the Pacific Salmon Foundation 3.14-3.16 Courtney Marchese and Sophia Alfieri 4.1-4.6 Informationisbeautiful.net 4.7-4.12 WHO/Interactive Things 4.14 Equal Earth: http://equal-earth.com /index.html

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4.15 South China Morning Post, Pablo Robles 4.16-4.17 Pablo Robles 5.2 Nextstrain: https://nextstrain.org /sars-cov-2/ 5.3 Martin Krzywinski, Canada’s Michael Smith Genome Sciences Center 5.4-5.9 Pentagram 5.11-5.13 Reuters 5.14-5.21 Werner Helmich 6.4-6.8 Shing-Yun Chiang 6.9-6.14 Sarthak Kathuria 6.19-6.20 Otto and Marie Neurath Isotype Collection, University of Reading 6.21-6.22 © 2020 Bill and Melinda Gates Foundation 7.1-7.3 Nadieh Bremer, Visual Cinnamon 7.4-7.8 Copyright Guardian News & Media Ltd 2020, Outside America Team, Nadieh Bremer, Shirly Wu 7.9-7.12 Bruises - The Data We Don’t See, by Giorgia Lupi and Kaki King

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Index A accessibility, 154 Action Plan, 53 aesthetic unity, 172 affective empathy, 57 AIGA and the Collective Action Toolkit, 69 Albums de Statistique Graphique, 24 Allport, Gordon, 50 Andrews, R.J., 92, 130, 150 annotations, 152 “anti-cyclonic,” 22 “Apocalypse Now,” 110–12 ArcGIS, 30 Aristotle, 99, 132 artifacts, 73 associations, color, 153 audience, 134, 135 B Baker, Robert, 18, 19 baseline evaluation, 190 Battle-Baptiste, Whitney, 163 Bauhaus’ effect, 25–7 Bayer, Herbert, 25–6 Beck, Harry, 26, 35 behavioral change, 42 Berman, David, 72 Bertin, Jacques, 14, 27, 150, 151 black America, 162–3 Bourguignon, Jean-Pierre, 89 Brahe, Tyco, 16 Bremer, Nadieh, 178–83 Brown, Tim, 68 “Bruises,” 197–203 Bryan Christie Design, 73–7 bubble chart view, 140 “Bussed Out” project, 178–83, 186, 190–2 C Cairo, Alberto, 90, 91, 100 Campbell, George, 99 Carbon Footprint, 94–6 categorical/qualitative color palette, 153

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“change over time graph,” 21 chartjunk, 67, 154 cherry-picked data, 91 Cheysson, Emile, 24, 35 Chiang, Shin-Yun, 154–7 choropleth map, 153 Clark, Kenneth, 51 closure, 149 cognitive empathy, 57 color, 152–4 color blindness, 154 commerce-driven projects, 43 Communications Research Institute (CRI), 67 composition, 144 conformal projections, 109 connection, 149 contingency, 101 continuation, 149 Covid-19, 126–9 coxcombs, 22 Crome, August F.W., 16–17 Cuomo, Andrew, 126–30 D data cleaning, 113, 114 data-driven design Bauhaus’ effect and universal functionality, 25–7 collaborative opportunity starts early, 83–7 comprehensive atlases and first disease maps, 18–19, 22 design processes and data exploration, evolution of, 65–82 evolution of form, 28–9 Fourcroy and Playfair, 16–18, 34 information design vs. data visualization, 13–14 methods and tools, 34–7 people and places, measurements of, 15–16, 34 statistical graphics, golden age of, 22–4 data graphics, 17, 28 data literacy, 89–90 data physicalization, 154

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Index

data presentation, truth and integrity avoiding misleading data, 100–1 data cleaning, 113, 114 data literacy, 89–90 data sources, 115–16 finding and cleaning data, 113 honest data, practices of, 91–3 maps, 108–9 rhetoric and persuasion, 97–9 data visual(s), 44, 100 data visualization, 13–14, 42, 138, 147, 163 design-based social change, 57 Design for the Real World (Papanek), 43 design processes and data exploration design thinking and rethinking, 68–70 graphic design, 65 having shared goals, 78 Irwin and transition design, 70–1 Sless and information design process, 67–8 Tukey and exploratory data analysis, 65–7 design thinking, 68–70 developable surface, 108 Dino Citraro of Periscopic, 79–82 disclosure, 101 disinformation, 100 diverging palettes, 153 DNA, 138–43 Dörk, Marian, 101 Du Bois, W.E.B., 162–3 Dykes, Brent, 120 Dykes’ model, 120

evaluation design, 190–2 evaluation research, 184 explanatory narratives, 144 exploratory analysis, 119 exploratory data analysis (EDA), 27, 65–6

E Effective Data Storytelling (Dykes), 120 efficiency, 90 Empathetic Design (Koskinen), 56 empathy, 45 applied empathy + humanization, 56–7 history of, 50–2 methods, 59–61 phases of, 58 Empathy (Lanzoni), 50 “empathy gap,” 51 empathy mapping, 60 empowerment, 101 enclosure, 149 Equal Earth Projection, 109 equal-area projections, 108–9 error, 92 ethnography, 61

H Halley, Edmond, 16 Helmich, Werner, 138–43 heuristic evaluations, 78 High-Level Panel on Water (HLPW), 53 holistic thinking, 73 How Maps Lie (Monmonier), 108 human-centered design, 61, 69 Human-Centered Design Toolkit, 69 humanization, 56–7 hybrid linear-nonlinear format, 132

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F fading, 144 Fifth Wave, 84–6 figure/ground organization, 149 filter, 144 First Things First, 43, 51 focus groups, 61 formative research, 190 Fourcroy, Charles de, 16–17, 34 Freytag, Gustav, 132 Freytag’s model, 144 Friendly, Michael, 13, 15, 22 G Gall-Peters Projection, 109 Galton, Francis, 22, 24, 31, 35 Garland, Ken, 43 Geographic Information System (GIS), 112 Georgia peach, 160 gestalt, 148–9 Global Gender Gap Index, 86 GOV, 138–43 graphic design, 13, 14, 65, 100 graphical innovation, 22 Guerry, Andre-Michel, 18, 34

I Idiopathic Thrombocytopenic Purpura (ITP), 197 Illustrator, 30 impact measurement common mistakes, 195–6 evaluation design, 190–2

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Index

evaluation materials, 193–4 goals and reach, 184–6 research methods, 187–9 Impact Over Form, 46–9 impact/outcome evaluation, 190 in-depth self-reflective process, 44 Industrial Revolution, 25 Info We Trust (Andrews), 92 information design, 13–14, 18, 28, 34, 35, 42, 45, 52, 67–8, 99, 147, 167 information graphic, 90 in-progress monitoring, 191 Institute for Disease Modeling (IDM), 168–71 intellectual content, 66 Interactive Things, 101, 102 interdisciplinary teams, 71 Intergovernmental Panel on Climate Change (IPCC), 93 International Picture Language (Neurath), 165 “International System of Typographic Picture Education,” 25, 164 interviews, 61 Irwin, Terry, 70–1 “Isotype,” 25, 164–7 Isotype Institute, 166, 167 J Jan Willem Tulp, 53–5 Janzer, Cinnamon, 69, 72–3 Jarzombek, Mark, 50 K Kai, Dawn, 135–7 Kandinsky, Wassily, 25 Kathuria, Sarthak, 158–61 Katie’s New Face, 74–6, 78 Kimbell, Lucy, 70 Kinross, Robin, 97 Kolko, John, 41 Krzywinski, Martin, 122–5 L Landa, Robin, 147 Lanzoni, Susan, 50, 52 laser cutting, 156 Law of Pragnanz, 149 lemon-dropping data, 91 Lertola, Joe, 75 Lewis, Scott, 172 Lidwell, William, 147

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“Life in the Camps,” 135–7 lines, 150 Lissitzky, El, 25 London Underground Diagram, 26 Londono, Juanita, 46–9 Lupi, Giorgia, 56, 121, 150, 183, 197–202 M malaria lifecycle, 168–71 maps, 108–9, 148 marks, 150, 152 McCandless, David, 94–7 meaningful narrative, crafting, 119 exploratory to explanatory data, 119 narrative structures and plot, 132–5 persuasion and memorability, 130–1 storyboarding, 144–5 storytelling, functions and goals of, 120–1 Media Literacy Index, 89 memorability, 130–1 Mercator projection, 109 Meteorographica (Galton), 22, 24, 31 Milestone Project, 15 Minard, Charles Joseph, 22, 23, 35 Monmonier, Mark, 108 multi-disciplinary practice, 28 Munro, Silas, 162 N narrative structures, 132–5 National Geographic, 18, 29, 30–3, 74–7, 132, 133 Neurath, Otto, 25, 164 Nextstrain, 122 Nightingale, Florence, 22, 35 non-linear narrative, 132 non-probability sampling strategies, 196 Noun Project, 167 P Pacific Salmon Explorer, 78–83 Papanek, Victor, 43 Pentagram, 126–9 perception, 148–9 personas, 60 persuasion, 97–9, 130–1 pictograms, 162, 164 Picture Superiority Effect, 154 Pixi JS, 138 plastics, 30–3 Playfair, William, 17–18, 34

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Index

plot, 132–5 plurality, 101, 103 political arithmetic, 16 political climate, 51 practical application, 16 predictions, 93 probability sampling strategies, 196 probability, 93 progress, 144 projection, 108 proximity, 149 Q qualitative research methods, 187 quantitative research methods, 187 R RawGraphs, 30 recognizability, 172 Reik, Theodor, 50 rhetoric, 97–9 rhetorical triangle, 99 Rifkin, Jeremy, 51 R-naught, 127–9 Robinson Projection, 109 Robles, Pablo, 110–2 Rogers, Carl, 51 rose diagram, 22, 24 Rosslyn, Stephen, 52 S SARS-CoV-2 (COVID-19) analysis, 122 scale, 108 Semiology of Graphics (Bertin), 14, 27, 150, 151 sequential color palettes, 153 shared goal, 78 Shea, Andrew, 42 sign-off process, 75 similarity, 149 sketches, 66, 112 Sless, David, 67–8, 78 Snow, John, 18, 19, 21, 22, 34 social design, 41 history of, 43–4 Social Design Action Matrix, 72 social design action matrix, 72–3 social good, 41–2, 203 social media, 44 social responsibility, 41 social science research, 177 South China Morning Post, 110–2

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stakeholder, 70 state-sponsored artifacts, 24 statistical graphics, 15 creation of, 18 golden age of, 22–4 Stephen, Bret, 92 Stevens, Harry, 20–1 storyboarding, 144–5 storytelling, 119, 130, 132 functions and goals of, 120–1 Strode, Josephine, 57 survey research, 193 Sutnar, Ladislav, 25 sykes, 130 symbolism, 108 symbols, 109 T Tableau Poleometrique, 17 text, visual cues, 152 The Future of Data Analytics, 27 The Guardian, 178–83, 191 The Visual Display of Quantitative Information (Tufte), 67 time-lapsed map, 31 titles, 152 “transformers,” 166 Transition Design Framework, 70–1 transition design, 70–1 Treat, Jason, 30–3 Trout, J.D., 51 Tufte, Edward, 67, 171 Tukey, John, 27, 65–6 TULP interactive, 53–5 Twyman, Michael, 167 typography, visual cues, 152 U uncertainty, 92, 93 V viewpoint, 144 Violence Info, 101–7 violence studies, 106 visual communicators, 13 visual cues, 147 color, 152–4 Du Bois and black America, 162–3 imagery and chartjunk, 154 isotype, influence of, 164–7 marks, 150, 152

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Index

methods and tools, 172–3 perception and gestalt, 148–9 text and typography, 152 visual encodings, 147 visual hierarchy, 152 W Washington Post, 20–2 Weinstein, Lauren, 69, 72–3

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209

wide-angle, 144 World Geo-Graphic Atlas, 25, 27 World Health Organization (WHO), 101–7 World Water Atlas, 53–5 Z zoom, 144 zooming in, 144

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