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Get up and running with ArcGIS, a true geographic information system (GIS) that allows you to dig into highly accurate geospatial data in a way other mapping applications can't compete with. It's great creating maps, analyzing data for land use studies and other reports, and preparing data for use in an application or database. Let Adam Wilbert show you how to display, analyze, and illustrate geospatial data with ArcGIS. He explores how to import data from multiple sources, manage it with the ArcGIS catalog, and then start making maps. Learn how to lay out your data in the ArcMap component; add symbols, scale bars, and legends; and get your maps out of ArcGIS and into the real world, whether it's for printing or export to another application.
Now that we know a little bit about raster files, let's explore a couple of them inside of our Catalog. I'm going to go into the data files folder connection that we made earlier, and inside of there into the global folder. And there's a raster image right here. I can go ahead and click on it. And in the preview tab you'll see that it's a color shaded relief of the world. Now raster files all share this gridded icon here. So, you can visually separate it from the vector files that we looked at previously. You'll also notice that raster files are treated like folders that we can expand to see the various data bands that make up this file.
So I can see the red, green or blue data band that compile into this color image here. Each band displays in a gray scale visualization, where dark areas don't have much of that color and light areas have high levels of that particular color. By looking at the various bands, we can start to see how each one might contribute to a certain analysis that we could perform. For instance, if I click on the blue one here there's very little blue in the areas with dense vegetation. Here in South America, and North America for instance. So we might use the blue band to help us isolate those particular areas.
In contrast, the blue band makes it very difficult to distinguish water from the desert here in Africa or from the Arctic Tundra up here. For that, the red band would be much better for it helps you identify the water features easily and helps you separate them from the landscape features. Let's take a look at a few more raster files that I have stored in the Seattle folder here. Inside the Seattle folder I have one called Landsat 8. This is a portion of a Landsat scene and when you click on it, it first asks you if you want to build pyramids. Pyramids are down sampled versions of the larger raster file that display only when you're zoomed out.
It helps draw the image on the screen faster and since the number of pixels in the image is far greater than the number of pixels on your screen, you likely won't notice a difference. As you zoom in though ArcGIS will substitute in the version of your raster with the higher fidelity until you're zoomed in far enough to see the original resolution image. You could think of building pyramids as creating a series of thumbnail images at different scales. Adding pyramids to your raster images does take up a little more room on your hard drive to save the multiple versions, but I think the speed increases that you'll see make it a fair trade off.
Let's go ahead and build pyramids when prompted to speed up the display of our data by clicking on the yes button here. And you'll see a little status bar down at the bottom as that process happens. Once the pyramids are built the image is displayed up on the screen. So this is a Landsat 8 scene. It's part of western Washington including the Puget Sound area here and the portion of Seattle right in here. This color composite image is a 30 meter image so each pixel measures about 100 feet across. If we zoom in using the tools up here on the tool bar, I'll click here and I'm going to zoom into this little bay right here which is Seattle.
We can start to see features of the city. We can start to see some of the docks, and maybe some of the larger ships out in the bay. But if I zoom in far enough you'll realize it is not really high enough resolution to help me out with any sort of city planning tasks. I'm going to go ahead and zoom back out to our world scale and note here that we can easily distinguish cloudy areas from snow areas. For instance the way that this data has been visualized, snow gets rendered in this bright cyan color and cloudy areas get rendered as this white color. So this is one image of a raster. Let's go ahead and zoom back out to our world view and take a look at another one.
Here inside of this air photo Lake Union Folder, I've got another air photo. And again, I'm going to go ahead and build the pyramids. So this air photo represents the north part of Lake Union in Gas Work Park in Seattle. Again we can expand to see the three bands of data here to see the colors red, blue and green in the data bands. Or I can click on this air photo lake union TIF file to see the composite color image. Let's go ahead and zoom in a little bit. Now air photos are often stitched together from multiple images taken as the plane passes over head. Or even on different days to remove clouds that might have blocked portions of the scene.
So you often get scenes like this one here where part of the image was taken on a day where the water was really calm, and the other part of the image was taken on a day where it had a little bit of chop to it. Now this is an example of a sub-meter resolution image. And if we zoom in even further, we can clearly identify cars but we can't really see any people in the scene. The next file I want to take a look at is this DRG Seattle north. I'm going to expand this open and click on it. And again, I'm going to build pyramids for it. A DRG Seattle North is a scanned USGS topographic map. DRG stands for Digital Raster Graphic, which is a typical designation for an image of this type.
You'll notice that you can try and expand this image, but the plus symbol disappears over here if you try and expand it. This is actually an example of an index color image where each pixel is colored from a limited number of colors like black, brown, white, cyan, green, or yellow and so on. Now when we load this image, you'll notice it's actually zoomed in to the same area that we were looking at in the air photo. So if I zoom out, we'll see that it's taking a look at Gas Work Park and the north part of Lake Union in Seattle. This map was literally scanned from a paper version, and if we zoom out even further, in fact, I'm just going to use this full extent button here, we can even see folds in the original paper map here where it was creased before being scanned.
Let me zoom in a little bit so you can see that there. So you can see the shadows from those fold lines. Finally, let's take a look at some elevation data. I'm going to go into this folder here called elevation and expand that. And there's a file here called DEM-Seattle-North. Again, I'll build the pyramids, and that'll load up the data set from the US Geological Survey. This is a representation of a continuous surface, and if we use the identify tool, just like in ArcMap, we can identify various features of this surface. So for instance if I click down here on this bottom left hand corner, we'll see that its pixel value is five.
This represents five feet above sea level as the elevation. And if I click on this portion right here, this gray area, I'll see that it ahs an elevation of forty-nine feet. Using an ArcGIS extension called Spatial Analyst, we can use these elevation values to render out a hill shade of the area, so we can visualize the topography. I've done that here with a raster here called hill shade. I'll build the pyramids for it and it'll load up a hill shade representation of the same elevation data. Now we can visualize topographic features of the landscape around Seattle. In fact you can.
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