Now, I want to show you four examples that cover the combinations that I described in the previous video. The first one comes from the University of Maryland. It's part of a research project developed in the 90s, and it's called the Visible Human Explorer. The idea here is that there are different slices, images, that show the anatomy of a human body. And as you can see in the visualization, there are different views of the same images. One is actually an overview of the body, and it slices the body in one direction, whereas, the other one that you see on the top right, is a detailed of the body, and it's slicing the body in a different direction. As you move the cursor in one direction, you can see different sections on the image that you see on the top right. Now, this is an example of a multiple views visualization, where the information displayed in the views is the same, the visual representation is the same, the only thing that is changing is the particular view or direction that is used to represent one specific section of the body. In particular, the one that you see on the left is an overview, and the one that you see on the right is a detail view. This is a very common pattern that can be reused over and over again, with very different kind of data and situations. There is an overview that is showing the whole data set, and a specific view that is showing a section of this data set, and this is driven by the selection that is done on the overview. Now, this second example is the Matrix Explorer. This is coming from another group of researchers from Inria in France. The interesting thing about the Matrix Explorer, is that it visualizes network data, using, at the same time, two different visual representations. The one that you see on the left is a matrix visualization of the graph, and the one that you see on the right-hand side, is a node link representation of exactly the same graph. What is interesting about this work, is that these two views are coordinated. So, if you select some elements, say, in the node link diagram, you will see the same elements highlighted in the matrix, and the other way around. Why is this useful? Well, because there are patterns that are easily visible in one view but are not very easy to grasp or detect in the other view, and the other way around. So, that's one reason why this type of method is very useful. Anyway, the reason why I'm showing you this second example, is because it covers another combination among those that we have seen before. In this case, we have exactly the same data set, the same information, but we have radically different visual representation in the two views. The next one is a situation where we have different data in each view, but we are using the same type of visualization. So, this is coming from a cross filter demo, and the idea here is that, by selecting a section in one of these histograms, I can see how this selection is propagated to the other histograms. Each of these histograms represents one attribute of the same data set. In particular here, this data set is about flights, and we have different attributes, like time of the day, arrival, delay, distance of these flights, and the date of these flights. As you select a range in any of these histograms, you will see the same filtering reflected in the other histograms. So, once again, why is this interesting? It's interesting because it's a different combination that we have not seen so far. So, we have different sets of attributes in the views, so different data, but we're using the same visual representation. The last one is yet another combination. So, this is a street view that you can use in Google Maps. So, the map that you see at the bottom is the overview, and there is a little man figure that you can move or you can, yes, you can move in the map. And the image that you see on top is the actual photograph of the environment at the location in which the little figure has been moved. So, again, it's another example of multiple views with interaction. As you move the figure, you are actually showing a different picture that represents the specific location that you have selected. In this case, we have different information and we have a different visual representation. So, with these four examples, we covered all four possible situations.
