Let's have a look at vector intersection and union. These are tools for overlay of vector data, and we can look at how different layers interact with each other in space and look at combinations of them. They're really useful tools and I think you'll find them to be interesting. Let's look at it conceptually first. So, if we have a study area like this, and we may have different inputs layers such as farmland, and good agricultural soil, and we may want to say where are the areas that are both farmland and good agricultural soil. This is essentially the same idea. If you want to think of it like a Venn diagram, only applied to real space or geographic space, that's essentially what's going on here. Is that we're doing an intersect. So, that's our intersect. We're literally using a Boolean And, but we're applying it in space. So, this is the intersection in space of these two inputs. So, we have one input here, one input there. If you think of Venn diagrams like this, and you have an intersection there, it's exactly the same idea only now we're applying it to real geographic feature classes. I've put together this really simple data set because I want to show you how this works with some simple data first because I think it can get complicated fairly quickly. So,these are data sets that actually created in ArcMap, they're real GIS data, and one of them is called rectangles. So, I've got an attributes table for that where I've just labeled them as upper left, upper right, lower left, lower right. So, that's what those four are, and I've given each one of them an ID number. I also created a second feature class for circles and did the same thing. So, we have an inner circle and an outer circle and each one of those has an ID number as well, and now I'm going to use the intersect tool which you can find in the overlay tool box inside analysis tools, inside Arc toolbox if you're looking for it, or you can use the search tool to find it. When you do that, we can just inputs our rectangles and circles as our input features, and I'm going to create in this case a shape file that's going to just be called intersects. Nothing too fancy, pretty straightforward. So, here's my rectangles, here's my circles, and here is the result of the intersection of the two. So, what I'm doing here is showing a couple of things. One is that features are actually split where they intersect. So, now we have a new feature here for example, and the new feature here, and what I'm going to do is take away the other inputs. So, this is actually the result here I don't want to confuse things too much. You can see there's the attribute table, and what this is doing is it's providing us with a result that's a combination both in terms of space and attributes. So, here we have a combination of the attributes from both tables. Now, all I did was just change the color. So, it's easy to tell them apart, and what I'm hoping you'll see here for example as we have lower right inner, so that would be lower right inner. So, that is a combination of those two inputs, that's lower right rectangle and inner circle. The next one is lower right outer. That's a different combination of the lower right rectangle, but the outer circle instead of the inner one. So, that's a different record with a different combination of attributes for the first one versus the second one. So, it's a great way of being able to compare and analyze combinations of things in space. If you look at a slightly more realistic example, this is for a study area that is owned by the University of Toronto, that's just north of Toronto, and one polygon has been defined by one scientific group as an Area of Natural and Scientific Interest, or an ANSI, and so that's the attribute for that polygon and this is a different feature class, with a different definition by different scientific group which is that it's an environmentally sensitive area. So, just as an example, if we did an intersection of those two, that would show us where those two overlap in space and maybe those are extra important or extra sensitive in some way. So, here's our attribute for the second feature class, very simple. Now, I'm going to do an intersection of the two. So, I've got my two inputs here, and that's going to be my output there, and so that's the result. That's the intersection in space of the geographic areas that are both ANSI, and ESA. So, that would be the answer to my question is to what areas had been designated under both definitions. I just put this in for reference. So, this is not part of the result, but I'm showing you these are the inputs, the first part. So, these are what would be in the inputs but not in the output, because it's not part of the intersection, is not part of the result. Here's the attribute table. So, this is a combination of the attributes from both of those inputs. Now, let's have a look the union. So, again very similar to the idea of a union that you might have seen in the Venn diagram or something like that before, if we go back to our study area, and we have our farmland, but now we're seeing farmland or agricultural soil. Now, it's one or the other or both. So, if we do a union of our rectangles and circles, here's our rectangles, here's our circles, and here's the union of the two. So, couple of things to notice here is that we have more results than we did with the intersection. That's what we would expect because the union is more inclusive as one way of thinking of it. Why is that? Well, for example, if I just change the color of them, you'll see that we have lower right with nothing. So, lower right with nothing would be this. So, in other words by nothing I mean nothing from the circle data set. Lower left with no circle there, but if we jumped down a little bit you'll see lower right, inner here, lower right outer, there, and you get the idea. So, the one thing to point out it's just I don't know, it's not trivia but it's can be useful, is that we have all the results of our intersection included in the union. If you're not sure what you want to use or what combination you're looking for, if you do a union, you can always then query or select things from the result that are just part of that intersection if that's what you want. So, you can't just do intersection, but if you do a union, you get all the results of the intersection with the one thing that like bonus material of things that are also part of the union. If we go back to our ANSI versus the ESA, here's the ANSI, here's the ESA, and if I do a union now instead of an intersection, that's the result that I get. So, you can see the inputs there, and there's the output. So, that's a union of all of them and if I just change the colors as I did before, now all of those are included in the results of the union. A minor point but something that's worth mentioning is that when you do overlays you have to look at the results just like I would recommend with anything. But here's an example where things could be questionable. Like let's say you have the polygon that represents city limits for a point in time in this case for the year 2005. Then let's say you do an overlay with the same city, same city limits, but in 2012. You'll notice that there's a difference between the two. Now, is that difference because the city limits actually changed, maybe the city grew, or maybe the city council changed the definition of how they define the boundary of the city, or is it just a spurious error that was created because two different people tried to digitize or trace the same boundary, but did it two different ways, and so you can end up with these what we would call slivers. Which are areas that you want to know like are those real, are they supposed to be there, are they something that's part of the real landscape or the real definition, or is it just purely an artifact based on the fact that the two data sets were not lined up correctly when they were created. So, it's not like I said it's not a huge point, but whenever you're doing overlays like that, if you end up with these little slivers along the sides, it's worth having a look at those to make sure that they're what you think they are.