- [Instructor] Information design and data visualization are really about focusing an audience on what's most important and only revealing more detail as you need to. So really it's all about information hierarchy. One of the great ways to help get to information hierarchy is using the six Ws. So if you remember from grade school, the six Ws are really the best way to organize and think about any story. And in data visualization in particular, you're not necessarily going to use all six, who, what, when, where, why, and how, but thinking about all six and figuring out which one or two or maybe three are the most important ones and eliminating the ones that you don't need is a great way to get at the hierarchy and the information that you're trying to show to your users.
One of the more famous examples of visualization is John Snow's map of a cholera outbreak in London in the 19th century. And the idea here was that cholera at the time was thought to be an airborne disease, but John Snow's theory was that it was waterborne. And so he actually took a map and marked these little black marks to mark every death from this particular cholera outbreak, and you could very visually and quickly and easily see that everyone who was dying was dying around this one water pump on Broad Street in London. And so he was able to prove his point by focusing on the where, right.
Where are people living who are dying? Showing that it was all around this one water pump and he was able to convince the city to pull the handle off the pump which led to the cholera outbreak diminishing. So focusing on the where in this case led him to a very logical conclusion that using a map was a good paradigm. So if you always think about the six Ws, narrowing down to the one or two that are important, it'll really help you in your visualization projects. So I was doing a visualization looking at hospital pricing data and I started off by thinking about what question am I trying to answer. The question is really simple.
Where can I go for a specific treatment at a decent price and good quality? It's the logical question you might ask yourself if you needed to go and get your hip replaced let's say. So looking at the six Ws, I'm looking at a list and I say okay, I have a who, a what, and a where. It's pretty clear. So let's go through those. The where in this case is the answer that I'm looking for, right? It's built right into the question. Where can I go to get a certain procedure done at a good price and good quality? The what in this case is really the most important information.
It's how I'm going to judge the places I'm looking at. Again, where can I find a hospital to get a procedure done at a good price and a good quality. And finally, the who is what I would call the granular answer, right? Where can I get a good price at a good quality? And in this case, it's specifically where, right? So I might narrow it down to a city, but then I really want to know which specific hospital so we'll call that the who in this case. That's the granular answer. In the end I don't really care about when, why, or how, right? I don't care why a hospital is lower priced.
I don't care when I'm going to go. It has nothing to do with this data. It's really about the who, the what, and the where. The hierarchy of that then, if you think about it in sort of a structural standpoint, is I'm starting off with the what, right? I care a lot about the price and the quality. That's going to lead me to the where, right, maybe which city I can narrow it down to that has lower than average pricing and higher than average quality for the procedure I'm looking for. And finally that's going to lead me to that granular answer, the who. What specific hospital can I go to to get my hip replaced? So I did this visualization looking at a bunch of hospital pricing and quality data, and as I said before, the most important thing in this case was the what, right? I'm looking for low price and preferably high quality care.
So the default view for this visualization is looking at procedure pricing. So it's sorting by pricing. So I can see that Los Angeles is the most expensive place to get a hip replaced, $223,000. And if I really just care about price, then I can go all the way to the bottom and find the least expensive place, Appleton, Wisconsin, and if I only cared about price, maybe that's where I should go to get my hip replaced. But, I do also care about quality. So I might then go in here and sort by quality and see that there are high quality hospitals in Miami, in Florence, South Carolina, and Portland, Maine, and the green bar indicates below average pricing in Portland so that's a good candidate.
We find that promising. But in the end, I also might want to look at quantity of procedures performed. Who's done a lot of these which you would think probably correlates with quality. And I can see that in fact where I live, Boston, has done a lot of these, has below average pricing. So I'm going to click in there. I don't need to see it on a map, but it sort of led me to my home town which is kind of interesting. But if I click in there, I can immediately see the granular answer, right, the who. And each one of these dots represents a hospital. The large dot means they've done a lot. And green means above average quality and below average pricing.
And I can see New England Baptist Hospital has a pretty good price, above average quality, above average quantity, looks like a good place to go to me. Hierarchy really is everything in information design and the six Ws are a fantastic way to get started thinking about the information you have, the questions you're trying to answer for your audience, and they'll help guide you to the right way to organize your data.
- 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.