Let's take a look at a couple of additional examples to show you how the hierarchical structure of temporal data can play a role in visualization. So, once again, we have sales data, and here I'm reusing one of the graphs that I've shown you previously about how sales change over time across all the years that are contained in the dataset. Now, the chart at the top and the chart at the bottom are representing exactly the same data. The only difference between these two is the level of resolution that I'm using to represent this information. So, in the chart at the top, I am using a resolution at the level of month. So, every single data point here represents the amount of sale in a given month, the total. The one at the bottom is at the level of weeks. So, as you can see, there are way, way, way more details, but there is also a lot of information. So, the only difference between these two, as I said, is the level of detail that I'm using. The fact that I can use different levels of details is due to the fact that time is hierarchical. So, I can go once again from months to weeks, I could have gone to days, hours, and so on. The implication of that is that there is a trade-off between having a lot of details, so seeing a lot of really fine-grained information, but at the same time also having too much information. This is a common problem in visualization, so, deciding at what level of detail information should be presented. Here is another example with cyclic data. Once again, I'm reusing one of the graphs that I've shown you before. The one at the bottom is the one that I've shown you before, and it's exactly the same idea. Even if I'm using a cyclic time, I can use different levels of resolution for cyclic time. So, I can go, for an instance, from weeks to months. Both of them are cyclic, but they are at different levels of resolution. Another very important consequence of the fact that we have time at different levels of resolution is that we can use the structure sometime to use different levels of resolution at the same time in a nested fashion. So, let me show you an example that is going to make what I just said much, much, much clearer. So, here we have once again, the same cyclic data that I've shown you before. So, we have every single data point represents sales in one day of the week. But now imagine that I want to see whether this pattern depends or changes across years. So, how do I do that? So, I split the line into multiple lines. Each one is colored according to one year, if you see the legend on the right hand side, and I repeat the same line and I draw them in a way that they overlap. So, think about what is happening here. I'm reusing time, two times, to represent different components in the same graph. So, I'm showing time in terms of years, but I'm also showing times in terms of day of the week. Here is another example where exactly the same thing happens. So, I'm using time on the x axis as month of the year, and every year is represented as a separate line, and colored differently to be able to recognize which year is which. So, this is a general purpose method, and as we will see later, it's a very common one and very useful one. For now, what you have to remember is that since we have hierarchical information, different components of this hierarchy can be used at the same time in the same graph.
