Mosaic and Aggregate are two raster tools that come in handy a lot early in a raster project when you're preparing your data. Often when you get raster data from some source, they may be cut into tiles. This is a common strategy because raster datasets can be quite large, and if you have a tile say provided in this case, it could be from a provincial agency or federal governments, there might be huge amounts of areas that they're trying to provide for raster format. So, what they do is they say, "Well, these are such massive datasets, maybe what we'll do is we'll cut them into separate tiles." That's what we have here is four different files that are all representing digital elevation models for different parts of the city of Toronto. So, often what happens in these situations is you want to be able to treat all of these four files or tiles as one dataset. That's what the mosaic tool is good for. Is if you want to do some slope analysis or train analysis of some kind, if you're working across multiple data sets like this, it's not going to give you a one unified result unless you mosaic the datasets together first. It's pretty straightforward, you have a tool here called mosaic to new raster, I've listed here the four different tiles that I have as my inputs, and I'm going to have one raster output. So that's the geodatabase that is going to go into, that's going to be the name of the actual raster dataset. I've decided to tell it what spatial reference I'm going to use last UTM zone 17 which is good for Toronto, and I'm going to give it a 32-bit unsigned pixel type. I'm not going to go into too much detail about that here, essentially, that gives it lots of room to have large values. If I use something like an 8-bit, for example, it would only be able to store values from 0-255, and since I know have elevation values that are higher than that, I'm going to use a bigger storage container, if you want that has more bits available, so it will be able to handle larger numbers. So, there's not a lot to this, here's my 4-input tiles and here's my output. So, now I have accomplished my mosaic, I have one new dataset where the whole thing is being treated as one thing, that is one thing now. So, if I want to do any kind of analysis pass this, it's much more convenient for me to be able to have this as one new dataset. The aggregate tool is used to lower the spatial resolution of a dataset. A common scenario for this might be that you have data say, from two different satellite sensors or two different sources of some kind where one might be say at 10-meter resolution, another one might be at 30-meter resolution or whatever it happens to be, and so, the aggregate tool is a way of being able to take these input cells and aggregate them into output cells. So, here I've got a simple input raster dataset, and what the aggregate tool is going to do in this case is I've told it to use a three by three block. So, it's going to look at each one of these blocks, and it's going to apply a function to it to decide what should the value be for that one larger block is going to be my output. So in this case, if I've set it to use the maximum, and there's different options available for this depending on what the data represents and what's most appropriate, but here the maximum value in this one block is one, so we have one larger block cell as the output, so it's now three times as large as the original data, and it has that one majority value of one. The next one over, we have a couple of threes and fours, so the maximum value is four, so that's what we get for this one. It works it's way through all of those, and that's what it produces as the output. So just to show you with a real dataset, this is a digital elevation model, this is the original data at 30-meter resolution. So I can use the aggregates tool here, this is my input which is the TEM mosaic that I created, and I'm going to create an output that's at 90-meter resolution. So, the cell factor is going to be three. What that means is it's now going to take three inputs cells and make that into one output cell. The way that I'm going to have to do that is to take the average of those, which I think makes the most sense here since we're talking about elevation. So here's my input 30-meter, and here's the output that's 90 meters. So, all I've done is I've adjusted the spatial resolution, I've made it lower, it's a coarser resolution, and you may think well, "Why would I want to do that?" Like I said, it really depends on what other datasets you're using. Often when we're working with roster, we want our spatial resolutions to match so that our cells line up correctly, and we can do our analysis in a way that makes the most sense. Just as a second example, I'm going to use the same DEM mosaic, but this time I'm going to use a cell factor of nine, so my special output resolution will be 270 meters just to make it a little easier to see. So here's my input of 30 meters, and here's my output of 270 meters. So, that's it for mosaic in aggregate, they're not really super difficult to understand, I hope, but they come in handy a lot when you're preparing data for your project.
