So, let's talk about Data Visualization. Data visualization is the craft of presenting information in a way that is concise, coherent and visually appealing. This allows readers such as your clients or internal stakeholders to understand the information quickly and easily, and subsequently make accurate and actionable decisions. By the end of this lesson you should be able to explain how data visualizations can help you relate your research findings and how well-crafted visualizations can help you communicate complex data or poorly crafted visualizations can hinder your message. So perhaps the best known proponent of data visualization is Edward Tufte, a statistician, artist and professor at Yale University. About 30 years ago, he wrote a groundbreaking book called "The Visual Display of Quantitative Information". Tufte's thesis is that good visualization can facilitate understanding, while poor visualization can lead to erroneous decisions. Additionally, according to a study, presentations with visual aids are 43 percent more effective than those that don't have them. Data visualization can be specially helpful when trying to communicate complex findings. Let's take a look at some examples. For example, let's take a look at this chart about the growth of Uber from fivethirtyeight.com. So here what they're trying to explain is, what is the difference in people that are using pickups or a taxi service or an Uber service from the same period in 2014-2015. Basically, whether or not Uber is making an impact on taxi service in Manhattan. See, it's a pretty simple line chart looking at four key areas, the growth in cabs in Uber, the decline of yellow cabs, the growth in Uber and then the growth in green cabs. Okay, makes sense. But, for those of you who may or may not know the intricacies of New York City, New York City has five boroughs or five specific areas. Manhattan which is best known as being the downtown or urbanized area and then four outer lying boroughs. Now, what the city wanted to understand is, where in the city were these impacts happening? For example, was the increase in Uber in Manhattan which again is the more urbanized area in New York City, or was it happening in outlying areas like The Bronx, and Queens, and Brooklyn, and Staten Island which are the four outer lying boroughs? So what they did is they took a graphical visualization here or you could even call it a heat map if you want. The areas in blue are where they saw a decline and the areas in red are where they saw an increase. And they looked at three specific maps: cabs and Uber, cabs only, and Uber only. So what's really interesting is to see overall in the first chart, cabs and Uber you're seeing growth all over. And in particular, there are some real deep red growth in sort of that lower right hand corner. Now you might ask yourself, what's there? Well, it's JFK, the airport. So that's a really important data point to recognize. Now, in the second chart they're just looking at cabs. Here you really see a change in the decrease of cabs to specifically the area of Manhattan. So that makes sense in understanding that cabs are really being affected by Uber in Manhattan. Lastly, they just wanted to look at the increase of Uber in all of these areas. And here again, you'll see an increase in Manhattan, an increase in some of the outer lying boroughs specifically Brooklyn and Queens, and then, of course, the really dark red spot near JFK which is a major airport in the New York City area in Queens. Right now by using three particular charts you're able to really delve more into the data to make a very clear conclusion that yes, Uber is having an impact on cabs in New York City and particulary in the borough of Manhattan, and to a lesser extent in some of the outer lying regions or outer boroughs. So let's take a moment. I've just presented a lot of complex information. So, why was it important to graphically visualize this information in the way that it was visualized? Well, by showing those three particular maps you were really able to see the impact that Uber is having on taxi service in the five boroughs of New York City. Now, compare that to the first line graph where you weren't able to understand what the impact of Uber was on those respective boroughs. So, what we can see from this is that sometimes having a good graphic representation can really help you communicate complex information, easy and in a concise manner.
