How To Consume Data Viz Like a Superhero
Extract the Meaning of a Chart, Map, or Graph in Seconds
What’s the first thing you do when you approach an annual report, article, or book? How do you decide if it’s worth your time? Many of us will scan the headers, images, charts, pull-quotes, or summaries. Sometimes we will decide a brief perusal is enough, and sometimes something grabs us and we read on.
Charts, graphs, maps, and other types of data visualizations (aka “data viz”) often pull me in, especially if they are visually striking. But until I became well versed in the art and science of data visualization, even charts that caught my attention would often frustrate me. I could not extract their meaning quickly and thus I moved on, missing out on the story the chart might have told. This article describes the method I have since developed to read data visualizations so that their meaning rapidly comes into focus.
Earlier articles in this series have addressed the importance of data visualization to the work of nonprofit fundraising, evaluation, marketing, and planning and how to visualize your data best. In case you missed any of them, here they are:
- Let’s Use Florence Nightingale’s Secret Weapons
- Data Visualization: Wielding Your Secret Weapons
- Sharpen Your Data Visualization Superpowers
But nonprofits not only produce data viz, they also consume it to understand the issues they are trying to address and to track their own progress. This article takes the data viz consumer’s point of view.
We will use this visualization series on carbon emissions to walk through the process of quickly extracting meaning from a data viz. It comes from the New York Times, a giant in the field of data visualization. The viz is animated and automatically toggles among these three options: “Current,” “Paris,” or “2 degrees.” Also, the series is repeated for particular countries including the United States, China, and India. But we will focus on just the whole world series in this article.
There are five steps in swiftly consuming a viz. I know that doesn’t sound quick, but most steps take only seconds to do. In each step, you answer a simple question. The questions are:
- What’s this about? What question is it answering?
- What’s my guess about the answer to that question?
- What’s the quality of the data?
- What more can I learn from the structure of the viz?
- What questions am I left with?
Let’s look at each question in turn.
1. What’s this about? What question is it answering?
This first question comes from a 1940 classic book called How to Read a Book: The Classic Guide to Intelligent Reading by Mortimer J. Adler. Adler maintains that you don’t save time on books by learning to speed-read. Instead, you save time by making an informed decision about what to and what not to read. And the best way to make this decision is to do an “inspectional read,” which means skimming through titles, headings, tables of context, etc. Similarly, when you encounter a chart, map, or graph in text, skim over it by reading the title and subtitle, and any captions or annotations. Then determine what it’s about and, more specifically, what question it is trying to answer.
The title of the New York Times visualizations is, “Here’s How Far the World Is From Meeting Its Climate Goals,” and the caption (which differs depending on whether “Current,” “Paris,” or “2 degrees” is selected) provides key words like emissions, Paris agreement, and warming. So, we know it’s trying to show if the world is on track to meet climate goals, probably in terms of emissions, the Paris agreement, and global warming.
2. What’s my guess about the answer to that question?
This might seem like an unnecessary step, but studies have shown that comprehension increases when a reader forms questions about a text before consuming it. This is called a “meta-cognitive strategy.”
It only takes a few seconds and can motivate you to read on, to see if your hypothesis is confirmed or undermined by the data in the chart. A question primes your brain for an answer. The more our curiosity is piqued, the easier all learning becomes.
My guess, before reading the sample viz closely and based on my memory of news reports on this subject matter, was that the world was NOT meeting its climate goals.
3. What’s the quality of the data?
This might be the most important step and the least likely to be taken. At minimum, determine the source of the data and whether the source appears to be reliable and credible. True, individuals will disagree on which sources are reliable and credible. Some of us, however, might be wary of data from institutions with clear political leanings or agendas. If no data source is noted, the viz is not worth your time.
For extra credit, look for information on what is and what is NOT included in the data. Consider, for example, the time period of the data and the demographics of people represented by the data. If data were collected from a sample, does the sample adequately represent the larger group? In short, you are trying to determine if the data appear to be equal to the task of the visualization. Can it really answer its question? Or are there gaps in the data that weaken its ability to answer the question fully or at all?
Inspecting our example viz, we learn from the second paragraph (and the footnotes) that the data comes from the Climate Action Tracker. Probably most readers are not familiar with this source but will feel reassured that the source is listed and a link is provided for those who want more information. If we follow the link, we see that the Climate Action Tracker is “ an independent scientific analysis produced by three research organizations (Climate Analytics, Ecofys, and NewClimate Institute) tracking climate action since 2009.”
We learn more about the data sources and what is included in the data from the footnotes, which tells us that the c harts show the carbon dioxide equivalent of greenhouse gas emissions and that the Climate Action Tracker’s analysis is based on emissions reported to the United Nations by each country. We also learn that required emissions for Paris and 2°C scenarios are drawn directly from the latest available year of historical data to the final projected year under Paris agreement goals. And, more specifically, that the global two-degree scenario reflects a greater than 66 percent chance of limiting warming to 2 degrees Celsius by 2100. So, we have heard of the UN. But we would need to know about the reliability of countries’ self-reports to the UN to answer other questions about the data. And, if we are really interested, we might look into the Fair Share calculation to better understand the 2 degrees projections.
Additionally, the scope of the data, in terms of time period and countries included, appears to be sufficient for answering the question about meeting climate goals. However, again, we would need dig deeper to feel more confident about this.
For most readers, it will come down to trust in the New York Times and possibly the UN. But it’s good that they gave us a way to investigate further into the data source, if we want to.
4. What more can I learn from the structure of the viz?
If you have gotten this far, you are engaged by the viz. Now consider what it all means. A visualization is, by nature, an abstraction of reality. It shows data collected in the real world using position, color, shape, and size to represent the data. Thus, it’s important to understand what these visual cues mean in the particular viz you are consuming. To do so, consult legends, captions, and notes, and then consider two basic questions: 1) What are the trends over time? And 2) How do groups compare to one another? Of course, not all vizes will show change over time or show multiple groups. But many do, and you will extract significant meaning by answering these questions.
Back to our example. Let’s consider the visual cues:
Position: Axes show position on a scale. In these charts, the Y-axes scale shows metric tons of CO 2, ranging from zero to 60 billion. A metric ton is equal to 1000 kilograms, so we are talking about a lot of CO 2. The X-axis scale shows years, ranging from 1990 to 2030. So, we are dealing with past and future. The slopes of the lines show the relationship of CO 2 emissions to time. When it slopes up and to the right, emissions are increasing over time and when it slopes down to the right, they are decreasing. However, each viz has two slopes, one labeled “high path” and one “low path.” I assume the high path shows the highest estimated emissions and the low path the lowest, but this is not explained in the article.
Color: The viz makes it clear that gray is past and that hot pink indicates the current emissions trajectory into the future, orange indicates the pledged emissions trajectory according to the Paris agreement, and yellow the trajectory needed to stay below 2 degrees Celsius. You have to wade through to the third paragraph to learn that at least: 2 degrees Celsius over preindustrial levels (3.6 degrees Fahrenheit) is “the threshold that world leaders vowed to avoid in Paris because they deemed it unacceptably risky.” The labels at the bottom of each version of the viz also emphasize what each color means.
Shape/Size: This viz does not make use of shape and size (such as thickness of lines) to mean anything. However, it emphasizes the amount of emissions by showing the size of the filled area below the line slopes.
Now it’s time to consider the questions about change over time and comparison among groups. In these vizes, the primary comparison is among the three types of trajectories. So, we can answer both questions by comparing their slopes to each other. It’s clear that the only trajectory that leads to a significant reduction in emissions is the 2 degrees Celsius low path. All other trajectories show increasing or level emissions over time.
At this point, I have the answers to my initial questions. The world is not on course to meet Paris Agreement emissions goals much less 2 degrees Celsius goals.
5. What questions am I left with?
Asking yourself what you still want to know can prime you for future vizes on similar topics. Priming happens when exposure to one stimulus influences the reaction to a subsequent one, for example, by increasing how quickly or easily we process the subsequent one. After consuming the example vizes, I’m left wondering which countries are doing the best and why, and what policies and practices help them to keep their emissions in check? Indeed, by reading further in this same article and inspecting the subsequent country-specific vizes, I can start to find answers to these questions.
These five steps to consuming data vizes may seem like they would take a lot longer than seconds or even minutes. But if I had just explained how to ride a bike, it would seem much more involved than it actually is. Once you learn these data viz consumption habits, they become second nature and require much less conscious effort, just as you don’t think through body placement, speed, and balance each time you get on a bike.
Moreover, as discussed in the first article in this series, Let’s Use Florence Nightingale’s Secret Weapons, we are wired to perceive certain visual cues very quickly, seemingly instantaneously. These cues include position, color, shape and size. That’s why a chart, map, or graph using these visual cues can be “read” much more quickly than a spreadsheet showing the same data in the form of words and numbers. However, such cues don’t tell us everything. We have to apply some simple analytical skills to extract the whole story. And by keeping these five steps in mind, we can do so.