Methodical Snark critical reflections on how we measure and assess civic tech

State of research: data visualization


Data visualization is all the rage in advocacy circles. Activists and development orgs are doing it all the time, often without formal mandates or training. This tends to go unquestioned, because  it’s easy to adopt a “good enough” approach to peripheral activities from the trenches of campaigning, and because visualization and design are things that a lot of us like to think we’re naturally good at.

But this is also a field where a ton of research has been done, and lots of evidence on which to base our decisions. Who knew?

Kennedy Elliot, visual journalist at the Washington Post, provides a whirlwind intro.

I’m a graphics editor at The Washington Post, which means I’m part developer, part journalist. Graphics editors like me often rely on common wisdom and experiential knowledge to inform our decisions about design and visualization choices — which is incredibly valuable.

For the last several years, I’ve wondered about what we actually know from scientific studies about how humans perceive graphics. I’ve collected things here and there, but when I started to get into the thick of it, I realized how extensive this body of research really is. There is a lot that I’m leaving out.

This is an extreme distillation of these studies. In reality, these are robust studies, many with multiple experiments each, loaded with meaningful nuance that I do not have time to address. These are very simplistic representations of one or two core findings. I encourage that you read them in their entirety and consider their complete findings.

In future posts, I’d like to dive in deeper to each section and present a bit more context for these findings. Look out for more!

This is a great overview of where to go and what we know. I’d like to see more links to the actual research though, not to mention distillation of the established heuristics. This probably exists somewhere in the visualization community.

h/t @flowingdata

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Methodical Snark critical reflections on how we measure and assess civic tech