Day 12

Today

For Next Time

Data Journalism and 538

Today we will be learning more about data visualization.  The specific lens we will be looking through is that of data journalism.

Data journalism is a journalism specialty reflecting the increased role that numerical data is used in the production and distribution of information in the digital era. It reflects the increased interaction between content producers (journalist) and several other fields such as design, computer science and statistics. From the point of view of journalists, it represents "an overlapping set of competencies drawn from disparate fields".

In this exercise, you will be creating your own data visualizations that would accompany one of the articles on 538, a data journalism website.

To get a sense of the types of articles posted on the website, let's check out this sampling:

What's even cooler (and I think I've said this a few times in class), is that 538 has a Github repo with a lot of the data that they use for their stories.

Today, with a group of 3 other students, you will be creating your own data visualizations for a story on 538.  We will end class with (brief) presentations by each group to the rest of the class.

Frameworks to Think About Data Visualization in the Context of Data Journalism

Digital Splash Media has an article with a list of the four features that define a great visualization:

Functional – The shape of the graphic is adapted to the questions the visualization should help answer.

Beautiful – If it is not attractive, readers won’t stop to read and interact with it.

Insightful – Put your data in context. Insight is the discovery of unexpected or relevant information in any data set. A visualization is created to give readers access and insight to data that they would otherwise not have. Many infographics lack context, meaning that they are not very insightful or relevant. A number on its own is meaningless, it becomes relevant in context.

Enlightening– The information the visualization reveals shapes the perception of the reader.

Communicating Information Accurately

Let's drill down a bit into the functional aspects of visualization identified above.  A nice writeup of some basic design criterion is given here.  One thing that I like is that it gives some examples of data, and goals that a data visualization should strive for.  For instance, let's check out section 35.2.

The example pulls out the following high-level goals:

Aesthetics

Giving you frameworks to think about aesthetics is admittedly outside of my comfort zone.  However, I do have a few resources that might be helpful in finding your own personal style.

Story Telling

Telling stories with data is a multifaceted process.   Here is an amazing writeup by Jonathan Corum, science graphics editor at the New York Times.

In addition to some of the themes discussed in this article, another dimension of story telling is emotional impact.  A famous example is this visualization (which we saw in class) on gun violence.  Alberto Cairo has a nice writeup discussing this specific visualization and some larger points around these sorts of emotional data visualizations.  I think this section gets at the meat of some of the key issues:

The goal of "U.S. Gun Deaths" is not only to inform, but also to demand a discussion about gun control. It is a smart instrument for persuasion: "Here you have the numbers," the graphic implies. "They are worrisome. Shouldn't we talk about them?"

I'll concede that the word "persuasion" sounds suspect nowadays. We tend to identify it with the shady techniques marketers develop to sell us products and services. But rational persuasion lies at the core of rhetoric, and rhetoric has a noble and long tradition in philosophy and science. It just needs to be handled with care, perhaps even more so when we use it in visual displays of information.

In a recent article about a visualization workshop sponsored by The Guardian4, the instructors described a majority of scientific output as "clarity without persuasion." They meant that researchers are trained to deal with data, and to present them in the clearest, most efficient way. But they are not good at making strong cases based on their findings. They are very careful, and for good reason: Seeing too much in your data can be dangerous.

Periscopic's project is not a scientific display, obviously. It can be better described as a visual Op-Ed, something that a knowledgeable journalist might build when trying to find the balance between what a researcher would do—get your data first; publish them—and what an opinion writer should do—promote public conversations about relevant issues. The challenges mentioned at the beginning of this article lie here: Where is the middle ground between the scientific approach and the journalistic one? To what extent is it appropriate to discuss politics around a visualization based on "just" graphically encoded data? Is it clear to a general audience that what they see is the work of professionals who actively shape data to support a cause, and not the product of automated processes?

Exploring Data Journalism In Class

Choose Your Article

With the people at your table choose an article from 538 that has accompanying data.  I would start with their Github repo so that you only look at articles that have accompanying data.

Read the article by yourself and then discuss its contents with the folks at your table.  Some questions to consider:

Create Your Own Visualizations

At your table, create some new visualizations for this article.  I see two ways this could go:

To help you choose which visualizations to create, you should identify some explicit goals.  These goals could be to increase clarity, increase emotional impact, etc.  Make sure to keep these goals in mind as you go about working on this exercise.

As a suggestion, I would split into two subteams of two that would each work on the tasks above.  Before 2:40 take a screenshot.  Edit this shared Google Slides presentation and insert your visualization, your name, and the title of the 538 article that you worked on (there's an example in there for you to use as a template if you'd like).  Make sure to group together visualizations from the folks at your table.

You will then have 3 minutes per table to present to the class (I know this sounds really short, but let's see how it goes.  Please, please think about how to present your work in a succinct fashion).  A possible structure: