There are lots of different raster tools that are available in the software depending on what it is you want to do. One way to describe those tools or put them into categories is based on how many cells are used to perform a calculation or that are data tools applied to. So, in this case, we're going to look at tools that are called local operators. That means they work on one cell at a time. So, local function also sometimes referred to as local operator, those are tools that are applied to one cell at a time. Now, that can be one cell in one dataset, or it can be the same cell across multiple datasets. We can do various things to that. We can use math, and all things, but essentially, that's what we refer to as local operations. This is something that was a set of terms that was coined by a guy named Dana Tomlin, as part of his PhD research, believed it was at Yale, back in the '80s. So, he was the first person to think about, "Well, how can we have this set of classes or categories a way of describing different raster operations?" So, we came up with four of them, local, focal, zonal, and global, depending on how those cells are being used to perform calculations. Here, we're just going to look at local. So, for example, we could have an elevation dataset, where the elevation values are in feet, and we want to convert them into meters. So, our output is going to be elevation in meters, the function that we're going to apply is simply just multiply by a constant, with a conversion factor, in this case, by 0.3, and that will apply the conversion for us. So, with a local operator, what happens is that you have the operator applied to one cell in the input, and then that's applied to the output. So, we have our 30 here, we're applying the constant to that, and that's going to be an output there. So, in this case, 30 times 0.3 will be 9. So, that's 9 meters. This then moves over to the next cell. So, we have a 20 here, we apply that, that goes into there, and then we get our next value. This is repeated for every cell in the dataset, doesn't matter how many cells there are. The whole idea is that you're applying one thing to one cell at a time, and then moving on to the next cell, and repeating. For example, if we have a real dataset like this, this is a DEM for the City of Toronto, that's in feet. So you can see we have our elevation values there from 233 to 1,004 feet. I can use a Raster Tool called Times, which literally is the multiplication tool that we use. So, with raster, it's interesting, it's part of to me, the way we think about raster analysis is, every thing you want to do, is it's own separate Raster Tool. So, even just something simple like multiplying a dataset by a value has it's own thing. So, this is going to be the Times Tool that's going to multiply our input dataset, that's in feet by a constant that we're going to use to do our conversion, and that's going to give us an output dataset, which is our DEM in meters. So, here we go, we have our result here. This is the elevation model in meters, and so that's all there is to it. We've applied a local operator, in this case, a mathematical operation. We multiply each cell to convert it from feet to meters. One thing you might notice about this by the way, is that we have decimal values in our dataset. Depending on what you're doing with raster, sometimes that's okay, and sometimes it isn't. Some things for example, if you want to attach an attribute table to a raster dataset, you can only do that if you have integer values. With a decimal values or floating values, however you want to call it, often what happens is there's just so many of them in a dataset that for whatever reason, the software developers decided, ''We're not going to allow an attribute table when there's all those different values with decimals.'' So, if you want to have an attribute table associated with it, then you would have to use integers. There's other reasons why integers can be useful. So, just as a hypothetical, I'm going to show you how to convert from decimal values to integer values. So, another local operation that we could use if we wanted to round up those values, there's a tool called round up. So, we're going to take our elevation model in meters, and we're going to create one called DEM round. It's pretty straightforward, and I always suggest that you take a look at the little dialog box here that shows you or gives an explanation of what each tool does. If you're not totally sure, then just refer to that, and that'll give you a pretty good idea. Now, you'll see, we have the same elevation model. Of course, visually, it looks exactly the same. The numbers have changed slightly though. Is that now we have rounded up numbers between 71 and 306, but they're still decimal values. So, I can now convert them to integers if I want to. So, to do that, there's yet another Raster Tool. This is still another local operation. This is the int tool that literally will convert from decimal to integer integer values. That's being explained over here. So, we're going to take our DEM rounded, and we're going to turn those into integers. So, here we have our elevation model in meters. So, that's all there is to it in terms of that conversion, or local operation. You don't always have to do this of course, I'm just showing you how this works. Other local operator is the Reclassify tool. So, it's called reclassify. We're going to take our DEM integers, and what I'm going to do is isolate just the parts of the elevation model that are in a flood plain based on the definition of values between 77 and 100 meters. So, what I'm going to do is, everything that's below that or above it, I'm going to assign to no data, so there's no data there, no data there. Any missing values will be changed no data as well. Please don't take this as anything official. I just eyeballed this, it looks like, pretty much, this would capture what I would consider hypothetical floodplain for Toronto. But this is just a way of showing how we can isolate values assigned everything else no data, and just keep what we want. Again, it's an example of a local operation. Here in red, we have the result of that reclassify. I could've by the way, assign any value I wanted to that part that I've assigned as red. I chose to assign it as 1. Often, that's just a convention, is if you're keeping one thing and getting rid of everything else, you could have the thing you're keeping as 1, and everything else is 0, or instead of 0's, you can have no data. But I could have just as easily have made that a thousand or whatever. At this point, that number is fairly arbitrary, but by convention, 1's are often considered as true or 0's are false, or 1's are on and 0's are off, however you want to look at it. Okay. So, what I've got here is I've overlaid the values that have a class of 1 on top of a gray scale version of the digital elevation model. I edited a little hill shading just to make it a little more clear what I'm looking at. You can see that it actually does a fairly good job of isolating the main parts of the floodplain up, these or ravines in Toronto, we have the dawn in the Humber Valley, as well as areas along the lake shore. So, this is just an example of a reclassify operation. So, technically, everything on this image that's not a value of 1 in that dataset is no data. But because it's no data, and I've made it so that it's transparent, the raster layer that's underneath it, which is this grayscale elevation model, is visible to you. So, it looks like I've draped or put this red class on top of the grey scale version of the elevation model. Here's the same thing again, only now I'm using a natural color satellite image. Again, I've used a bit of dramatic hill shading just to make it look a little more clear. The edges of the ravines in Toronto aren't quite that dramatic, but I think it makes it a little easier to visualize. So, you can see the same thing here in terms of the floodplain boundaries. So, that's I just wanted to give you an example of some typical local operators, and what a local operator means.
