- [Instructor] So in some ways, this might be the most important movie in the course. How do you make a good visualization? You might ask yourself that. And I actually use that as an acronym to help me remember. Really what it comes down to is accurate, story, knowledge. If you can create visualizations that are accurate, and tell a good story, even if it's not a linear story, and provide real knowledge to your audience, then that's a good target to shoot for. So let's talk about accuracy. This was published by a national news network and as you can see it's tracking the unemployment rate under President Obama.
And, I'd like you to take a look at it for about five or 10 seconds, 30, however long you have. You can pause the video if you really want to think about it before I tell you the answer (laughs) And I'd like you to tell me what's wrong with it? So you might have noticed, that data point on the far right, in November, the most recent one is showing as 8.6% and yet look at where it is visually, it's on the same line as 9% and if you compare it to the 8.8% data point in March it's higher than that one.
So, this is just wrong. This is what it should've looked like. Can you see any other issues with this chart that might qualify it as being inaccurate? Take a look at the scale. The highest data point here is 9.2% and the lowest is 8.6%. The scale should have been set from 8.6% to 9.2%, right? From the very highest to the very lowest. Or maybe 8.5% to 9.5% like these red dotted lines show.
Or 8.7 to 9.3, you can play around with it a little bit, but showing all the way from 7.5 to 10. 5 is exaggerating to show a flatter line. It's accurate, technically, but it's misleading. This data comes from the Bureau of Labor Statistics, and this is how they provided it. Now if you look at one more issue here, take a look at the headline and the X axis label. This is supposed to reflect the unemployment rate under President Obama.
That's what this is saying. Which sounds like this is for the entire length of his term. But of course, this isn't. This is just for the year of 2011. So the data isn't really providing the unemployment rate under President Obama, it's the unemployment rate for 2011. So that's accuracy. Let's talk a little bit about story. In the last movie, So What is Data Visualization, I pointed out Charles Menard's visualization of Napoleons march on Moscow. This is very frequently referred to by Edward Tufte and others as one of these great classic examples of visualization.
And one of the reasons is it tells such a compelling story. Napoleon's army marched into Moscow and then retreated from Moscow, and lost something like 98% of it's army, 400,000 men. And while you can read that in a sentence and maybe you can see a bar chart that sort of reflects it, something about this really tells the story. You can see them moving to the East and the army just getting smaller, and smaller, and smaller. They hit the Berezina River on the way back and it shrinks by 50% yet again.
And look at how thin that line is by the time it gets back. Meets up with a few more of its forces, but what a compelling story. And you can also track the temperature and how cold it was, which helps explain what was going on. Finally, knowledge. Another classic example of data visualization, this is a map created by John Snow, who was a doctor in the 19th century and he had a theory that cholera was a water-borne disease.
At the time, people thought it was an air-borne disease. And when there was an epidemic of it, he mapped out where all the deaths were. You can see the little black lines next to the homes where people were dying from cholera. He was able to prove that they were all clustered around this one water pump here on Broad Street in London. He took this data and transformed the understanding of cholera for the entire world going forward. This imparted real knowledge. In fact, his work, including this map, was part of the origin of the field of epidemiology and public health.
If you're careful and accurate, and you tell a good story and you impart real knowledge to your audience, you can transform them in ways that just informing them will never do.
- Describe the process by which individuals’ interests are incorporated into data visualizations.
- Differentiate the use of the Ws in data visualization.
- Explain techniques involved in defining your narrative when visualizing data.
- Identify the factors that make data visualizations relatable to an audience’s interests and needs.
- Review the appropriate use of charts in data visualizations.
- Define the process involved in applying interactivity to data visualizations.