[MUSIC] Hello everyone, and welcome back. In this lesson, I'm going to introduce Fishnets and Voronoi polygons. Voronoi polygons, in particular, are important in interpolation, and you'll learn why in the next lecture. For now, we'll discuss creating zones using both of these methods. By the end of this segment, you'll be able to describe zones, and explain how fishnets and Voronoi polygons work. First, when I say zone, what do I mean? A zone refers to a polygon representing some sort of analysis unit. I frequently work with watershed polygons and aggregate data up to them. So, in my case a zone would be any one of those polygons. Other examples would be using data units from a census, state boundaries, zoning maps, fire risk areas, and others like that. But for each of those, boundaries matter to the data at hand, which is sometimes good for an analysis and sometimes bad. But what if we don't have a set of boundaries to use, or we're worried that those boundaries are influencing our analysis negatively? Well, we might want to create some zones of our own, then. This is where fishnets and voronoi polygons come in. They're two separate ways to create polygon boundaries, either independently or somewhat based on our data. The first method I'd like to talk about is the fishnet, which is relatively simple. Ultimately, fishnets are just a tessellation of rectangles, which is a fancy term for a repeating pattern of rectangles that fills an entire region. This is how it gets its name. It looks more or less like a net used for fishing because it appears as a grid of lines. ArcGIS has a geoprocessing tool to create these and it allows us to specify the origin location, as well as the size of the polygons on each side, and then how many rows and columns we should have in it. If this sounds at all familiar, that's because it's quite similar to how rasters are structured. But, this is all vector still, and as such, we still have an attribute table we can use to aggregate values within each fishnet cell or polygons to use to select and modify the attributes of items that fall within a particular part of the fishnet. In creating a Fishnet like this, we can tweak parameters like size and orientation so that it works well with our data. But it's otherwise independent of our data structure, which as I said, can be good at times if you're worried about things like the modifiable arial unit problem we discuss in a previous course. Another way to make zones is to create Voronoi Polygons which are often called Theissen polygons including an ArcGIS. Some of you may have heard of them from your own work. While freshening up on material for this lecture I suggest how many different professions have uses for them. They're about simply simple overall which is part of their beauty Imagine again a set of points with attribute values we want to assign to the area around them. We want to interpolate using a ten, but behind the scenes ArcGIS will need to create voronoi polygons anyway, they're the first step. If we want to create zones from our points, the simplest way to do so is to assign each location in the full region of interest to the nearest point. If we do that everywhere, and group all locations assigned to the same point together. We can create polygons representing everywhere that's closer to a specific point than to any other point. These are Voronoi polygons. To rephrase it, imagine creating boundaries around all the points in our data set that delineate where the space inside that polygon is closest to the point that was the source for the polygon relative to the other points. If you cross that polygon boundary, you're now closest to a different point. Each polygon represents the area closest to any one point. The end result is a little funny looking. I always think of it looks like a close up of skin cells. They have a geometric quality just from being packed in next to each other. But there's something smooth about them, too. With Voronoi polygons, we can create a sort of sphere of influence feature class. Maybe you want to know an approximate service area for a set of business locations, or the closest straight line distance location to resupply on fuel. Voronoi polygons can give you a good metric. You can also use them to find locations of least cost or hazard. If your points represent some set of hazards. The boundaries between the Voronoi polygons are always furthest from that hazard. Each of these examples can also be well served by true interpolation or by network analysis tools. But Voronoi Polygons are a nice shortcut that helped get the short cut down first. And then, as I said, Voronoi polygons are the first step in creating a TIN. But I'll tell you how that works in the next lecture. If you want ethodic experiments before you continue on, see if you can figure out how we conceptually make a triangular irregular network, or TIN, out of Voronoi polygons. Okay, that's it for this lecture. In this lecture, we discussed a few ways to create zones to use for aggregation that cover an area of interest. We briefly discussed using fishnets that aren't dependent on variables and interest and then moved into Voronoi polygons which create polygons that define areas that are all closer to the same source point than to any other point. I think Voronoi polygons are kind of fun, but they get even more interesting when we start working with tins in our nest. That's what's coming in the next lecture. See you there.