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3.4: Data Visualization and Infographics

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    50695
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    Data Visualization and Infographics

    When we think about visual rhetoric, one of the most iconic tasks we associate visuals with is the graphical display of information. Visual representations of data are one of the quickest ways to take something numerical and data-driven and make it digestible to a lay audience. For example, you may not know much about quarterly profits or the levels of methane in the atmosphere, but you can understand a trend line on a chart showing these figures rising and falling. Visualizations of data are powerful, and potentially perilous on ethical grounds.

    What is data visualization?

    Data visualization is at its heart a fairly simple task—you take data and you represent it in a visual format, often in the form of a chart. Now, if you are like most people, the idea of working with charts does not exactly get your blood pumping. I think that is a natural reaction, perhaps even a healthy one considering the state of most charts out there. But, it doesn’t have to be so boring and dull. Data visualization can be one of the most engaging and useful parts of your work in technical writing and an essential tool in communicating complex information to others.

    We tend to think of data visualization hand-in-hand with charts because charts have traditionally been the name of the game for most folks, historically speaking. Until the relatively recent creation of powerful and accessible data visualization software that laypeople can use successfully with little training, simple charts were the most accessible option for sharing data visualizations because they often had to be drawn by hand or with tedious software. With the rise of more and more software tools to craft visuals and design texts, data visualization has changed dramatically in a short amount of time. For example, infographics are a type of data visualization that have existed in one form or another for a good while, but they have seen an astounding burst of popularity in the last several years, likely due to the confluence of easier-to-use tools for their creation and low-cost/high-traffic venues for sharing them: social media and websites.

    At its core, data visualization is a process of translation more than anything else. Much like the standard definition of technical writing that we started with early in the course, data visualization takes a piece of data and then passes it on to a new audience, making choices along the way of how to present that data and which parts of that data to make more visible and which parts to make less visible or to omit altogether. For example, you might have a dataset that includes information on fifteen different criteria about different institutions of higher education in your state, but you may create a chart that only displays three of them together to make an argument about how the three are interrelated or dependent on each other or to simply show a huge disparity between institutions. The entire dataset doesn’t always come through into the visualization, and even if it does, it will be altered in some way by the shift from text/number to visual.

    Now, you may have paused briefly at what I just said—shouldn’t data visualization just be about numbers? Why would we want to talk about text? Well, thinking that data visualization is just about numbers is a mistake, one that limits our understanding of the power and flexibility of visualizations. For example, I could have a dataset that lists the colors used by a college athletics team since its inception. This dataset likely includes color swatches, perhaps color codes, and will also contain dates for the usage of these colors and perhaps even imagery demonstrating the jerseys that the colors would used on during any given time period. Notice that most of this dataset is not actually numbers. A good bit of it is going to be visual already! From this information I could create a timeline that shows the evolution of jersey design and coloration, or I could create a pie-chart that shows how much of the team’s existence is devoted to each particular color choice, or I could do something else entirely. Data visualization doesn’t have to be about taking numbers and turning them into images; visualizing any sort of data counts!

    Data Visualization, Education, Persuasion, and Discovery

    When we think about data visualization, we must keep in mind that we’re working with a particular goal in mind (at least, I hope we are) to either persuade or educate our readers about something in our original dataset. (Now, that isn’t to say this is a definitive list of stuff we can do, but let’s keep it simple for our purposes). In each of these cases, we may actually find ourselves doing even more than simply educating or persuading—we may end up learning things we didn’t set out to learn.

    When we work to educate or persuade with data, we’re making editorial choices, choices that we’ve discussed above. We situate ourselves, our audience, our goals, and the dataset. We try to figure out what particular part of the dataset needs to be visualized and how that visualization will work. This may sound familiar, and it really does align almost one-to-one with our visual rhetoric workflow above. The key here is that we need to think about our dataset and what we need to pull out of it— what will be of use for our users and our context? Once we figure that out, we need to see how that can be carried out in a way that is clear and that doesn’t mislead.

    Going about the work of figuring out how to share information visually can lead to unexpected discoveries, and that is part of the beauty of data visualization. The approach can be used to share information, but it also has a strong role in invention. You may be wanting to simply relay the growth of different categories of students at your university, but in that process you realize that while all groups are getting larger, the rate at which they are growing is different, resulting in some groups actually becoming a smaller percent of the overall student body despite continually getting larger. You wouldn’t have started out looking for this, but in the process of crunching all of those numbers into visuals you stumbled upon this information, giving you a new perspective.

    When we share visualizations, we’re trying to get people to engage with data they might otherwise overlook or ignore. For example, many people take numbers and simply don’t hear them as data, but instead hear them as noise or non-meaningful parts of your message. This isn’t to say that folks who think numerically don’t exist—they do—but, they aren’t going to be in the majority as far as I’m aware. The same is true for other datasets—they are often super meaningful to a specific group of folks. What a data visualization does is present the information you are sharing in a new way, giving your readership a new lens to view your data through. In some cases, this can actually cause them to engage with the data when they otherwise wouldn’t. In other cases, it may help them see around pre-existing conceptions they have about the data or subject by allowing them to see figures and information they are already familiar with in a new way or with new connections emphasized and old ones deemphasized.

    Types of data visualization

    Just like with any other type of visual rhetoric, data visualizations work off of existing visual rhetorical conventions. Often times these are codified into types of charts such as pie charts, scatter plots, bar charts, etc. These types of charts exist because they represent an approach to visualizing data that has a value and a purpose. For example, pie charts show us how much of a whole something occupies. Other more novel designs exist, especially in the realm of infographics, but even those designs often rely on visual conventions from outside sources to make sense.

    With charts, we have a powerful tool because most popular types of charts are easily read by the general public. More specialize charts have more specialized audiences, but simple bar charts and line graphs and their companions are found all over and can be easily created with spreadsheet software or design software. The advantage of the popularity of these types is that we don’t have to do much work to get across the gist of what we’re trying to say—the charts have a built in message. Pie charts, as mentioned above, show us a slice of a pie versus the whole. When you take a dataset and you want to emphasize just how much of the dataset is a certain category of content or just how big a certain group is, a pie chart makes sense. If you want to instead show how five different groups are outclassed by one larger group, you might want to use a bar chart because having the different group next to each other allows for a much easier comparison between them and emphasizes size similarities and differences in ways that a pie chart can’t do with much rigor. And the beauty of all of this is that folks already understand how these charts work.

    Sometimes, it can be useful to try to get your data into multiple types of charts and graphs, just to see what these types can teach you and show you, but you’ll often come to a particular type of graph or chart because it represents something you want to say about your data, something you already know.

    With infographics, the conventions and audience understanding can vary wildly depending on how you create your text. An infographic might be made up of a bunch of normal charts with some text to narrate, but often you find more novel approaches in infographics, approaches that borrow visual conventions from elsewhere to make their point.

    For example, if we used our previously mentioned dataset on the color changes in an athletic program’s history, we could compare the different colors by the number of wins a team had while wearing them. You see this sometimes with home and away and alternative jerseys in sports discussions. In this case, we would likely want to create a miniature version of the jersey that we could then use to represent a particular amount of wins, perhaps 50 per jersey. Then, we could have a grouping that showed all wins over a certain period of time with each jersey in each particular color representing the amount of wins that occurred in that color. The result would be a block of tiny jerseys that would meld together into chunks, letting us see how stark or not-so-stark the differences in wins were between the colors. In this case we’ve turned data into a visual, one that borrows the visual convention of the colors and the general jersey design to convey information via a key that each jersey is 50 wins.

    Now, the above is just one example of how an infographic might be made—you’ll often find that infographics are not as simple as one subject and one visual. There can be any number of charts and unique constructions in a given infographic: the goal of the project will help constrain and guide the choices of what needs to be shown and what doesn’t.

    Building a workflow for data visualization and the ethics of visuals

    With data visualization, we can mostly work with the previously discussed workflow on visual rhetoric and visual conventions. When you’re identifying what to share and what your goals are, think about which parts of your dataset are going to be shared and why you’re doing that sharing. When you think about what an audience can understand, think about the types of charts that could be used or the visual conventions that could be altered to create infographic components.

    When visualizing data, one special extra issue stands out—the relative ease of purposefully or accidentally distorting the dataset behind a visual through design choices. When someone looks at our visual, they often do so with very little critical thinking, or at least that is what any number of sources and anecdotes claim. We tend to trust visuals, especially pretty looking visuals that are professional and shiny. This perhaps is part of a larger cultural trend in the US that connects polish and graphical prowess with excellence. (Just think about how much value is placed on the visual appeal of certain brands of phones, for example, rather than their functionality. Think about older looking websites— do you want to give them your credit card?) In addition, realize that visuals operate on the rhetorical idea of synecdoche—the part stands for the whole. When you represent a dataset to someone, they often take that to be the dataset. We really should be more critical I suppose.

    When creating data visualizations, we need to ask ourselves: am I making choices that alter the way this data is perceived? You may be using a truncated X or Y axis on a chart to make a point, but that may not be obvious to your readers. In this case, your chart may be showing what looks to be a huge change in figures where one simply doesn’t exist. Once you have created a visual, audit it. Get it tested with various users. Make sure that what it is showing and emphasizing can be backed up by the data behind the visual. If the visual can’t be backed up by the dataset, you’ve got a problem.

    Section Break - Data Visualizations

    1. One critique of visualizations that is often made is that they simply don’t cite their sources. Find five visualizations or infographics and check and see if each has sources. If they do have sources, see if you can locate the cited data.
    2. Draft a quick data visualization about something related to your institution. You can often find data related to your institution via the office of institutional research or another similar group. Think about what you want to represent and why. Sketch this out—don’t worry about fidelity.

    This page titled 3.4: Data Visualization and Infographics is shared under a CC BY-SA license and was authored, remixed, and/or curated by Adam Rex Pope.

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