For the last couple of years, I’ve been meeting pretty regularly with Max Graze, Amy Cesal, and Alan Wilson to explore and better understand data visualization style guides. There’s a lot to learn here about best practices, templates, and organizational principles! We even created our own website where... The post DataViz Style Guide Blog Series appeared first on PolicyViz ..read more

An age-old data visualization debate reared its head again recently when The European Correspondent (EC) published a new graphic titled “Terrorism’s bloody toll,” which looked and felt strikingly similar to Simon Scar’s 2011 award-winning visualization “Iraq’s bloody toll.” This situation raises an... The post A Tale of Data Visualization: Inspiration, Imitation, and Tribute appeared first on PolicyViz ..read more

For the past year, I have conducted a weekly “data physicalization” project with my colleagues at the Urban Institute. Each week, I would devise a new research question and give my colleagues the opportunity to input their own data using toothpicks in foam, stickers on the wall, wooden disks on a map, string tied together, dried beans in glass tubes, and more. Together, we would create a data visualization, be it an isotype chart, bar chart, scatterplot, histogram, or other illustration. Because the project led me to new insights on how people approach visualizing data, I started to think abou ..read more

Over the past three years, Eric Balash, a Tableau Visionary and Tableau Public Ambassador, has been running the “Back to Viz Basics” (B2VB) project. The idea is to create a community of people new to data visualization and provide them with a guided, bi-weekly prompt to create graphs, charts, and tables. Each week, Eric and his team publish a relatively simple data set and ask participants to, quite simply, create and share a graph. The graphs start simple—line charts, bar charts, pie charts, scatterplots, and then move on to more bespoke or non-standard charts, like beeswarm charts and Sankey ..read more

With many of my data visualization clients or classes, I like to begin with a simple exercise: draw a line down the middle of a piece of paper. On the left side, write down a list of the things you identify as characterizing a good data visualization; on the right, a list of things characterizing a bad data visualization. Invariably, similar words and phrases show up. On the good side:  “clear,” “simple,” “legible,” “good colors and fonts,” “good labels,” and “accessible.” On the bad side: “biased,” “cluttered,” “vague,” “confusing, “overly complex,” and more. But one word always catches ..read more

The Washington Capitals made it into the NHL playoffs last night after a 2-1 win over the Philadelphia Flyers. Fans have observed the Caps huge negative goal differential–the difference between the total number of goals the team has scored and the number of goals they have allowed–as a rarity for playoff teams. And when you look closer at the data, that is certainly the case. I downloaded a decade’s worth of NHL standings data from ESPN and calculated each team’s total points as a share of possible points (2013 was a lockout year and 2020 and 2021 were COVID years, so those seasons had fewer t ..read more

Some of my data interests are spurred by my work at the Urban Institute, others by things I’m personally interested in, like sports or politics. But sometimes, my kids inspire my work. My son inspired my exploration of timing of goals in NHL when he kept scoring in the last minute of periods when we played hockey on the Xbox. Today, he has inspired me again. A couple of years ago, my son got really into Formula 1 (F1) racing. And when I say “got into” I really mean it—do you know a lot of 14-year-olds who will wake up at 6 a.m. on a Sunday morning to watch a race in Bahrain or Qatar? Yeah, tha ..read more

I’m sure you’ve heard it said that a graph needs to be “immediately recognizable” or “understood at the snap of your fingers.” After all, “a graph is worth a thousand words,” right? But is this assumption true? Can you simply glance at a graph or chart and immediately understand it? Let’s do an experiment. Sure, you see bars going up and going down. Maybe you noticed the gap where the fifth bar should be. But what is it showing? Different countries? States? Years? Months? You can’t tell—we need some labels along the horizontal axis. Okay, that’s better. Now I know the data start in 2000-01 a ..read more

Late last year, I presented my work on the Urban Institute’s Do No Harm project to analysts at a state department of health. (I’ve changed specific numbers in this post and am not naming the state or hospitals to keep the information confidential.) Following my presentation, one of the analysts who works on stroke care across the state told me about some of the data that had caused a bit of a stir in their data community. At an early 2023 meeting, the department’s data team presented information about a worrying trend in stroke care at a specific rural hospital in the state. Hospital ..read more

“Where should I place the labels on my graph?” It’s question that comes up in many data visualization discussions. Although the decision about where to place your labels is largely an aesthetic preference, I do think there is an objective logic you can follow. Let’s start with this simple line chart of the share of people in the labor force by generation—a graph that I saw in Axios and in Philip Bump’s newsletter. In this basic chart, we have a legend at the top of the graph. There’s nothing inherently wrong with using a legend, but it’s disconnected from the data. As I argue in my Better Dat ..read more