When mapping the real world we can use the vector data model or we can use the raster data model. So, let's have a look at the raster model. Here we have an example of a raster data model. This is a digital elevation model, or DEM, for part of the city of Toronto. An elevation is a continuous surface that exists across the entire surface of the planet. So, we would consider that a continuous phenomenon and that's something that lends itself well to being modeled or represented in a raster data model. So, if we zoom in here, you'll see that, if we're getting close enough, that this is actually made up of individual squares, also referred to as cells. That's kind of the typical GIS term that we use is cells. And that each one of those cells represents an area on the ground that's been captured for that elevation. So here, this particular area on the ground in the little black square represents an elevation of 136 meters above sea level. So, with a raster grid, we have rows and columns that can be square or rectangular, but we have these rows and columns of cells and each one of those cells has a value. So, here we have a cell that's with an X value of two and a Y value of three, meaning that we have two there. So, it's two over and the X comes first when we're recording coordinates, and then it's three down here, so that's three. And so that's the three there. So, we have our X value here or Y value there. So, each cell has a value. Here we have one for 136, that represents 136 meters above sea level, and every one of those cells has a value, and those cells can be either integer or real. What's the difference between the two? These are the things that you do need to know about. Integer values do not have a decimal place and real values do have a decimal place. That's the easiest way to remember it for now. In this particular data set, when it was created by someone else, they decided that it was not necessary to have decimals because the level of precision didn't warrant it. And so we have values that are rounded up to the nearest meter and so we can represent those as integer values. If we had something else where it was more precise, we could say to the tenth of a meter or something, then we would want to use a real type of data. Every raster data set has an attribute table associated with it. And these always have at the bare minimum a value and a count. So, the value, is the value of a cell in that data set. The count is how many cells have that particular value. So, here we have a value of 138 and the count here is that there are 6,248 cells in that data set that have a value of 138. So, if we just go down to another one, of value of 141, there are 8,140 cells that have a value of 141. So just some basic statistics to tell you the distribution of the data is that how many cells have a particular value and what those values are. Raster data is really just storing numbers in cells. What those numbers actually represent can be a lot of different things. That could be a satellite image in which case it's storing how much light is being reflected off the surface of the earth to a sensor. And so if there's more light it has a higher number, or less light it has lower number. It can be a digital photo which is really exactly the same idea only from a camera. It can be a digital elevation model where those squares represent elevation on the ground, or it can be a scanned map where it's a paper map that's been scanned with some kind of sensor. And really, three of these four, the image the photo and the scanned map, are all just forms of sensors that are capturing information based on how much light is being reflected or what color that light is. But all I'm trying to get across here is that, you can think of this in a lot of different ways. Raster data is just a grid of cells or squares that have values and those can be used to represent elevation or light or whatever it happens to be.