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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.
As if we didn't already have enough data to think about, there is one more source of information that we have to know about if we're going to be proficient in ArcGIS. This time I want to talk about metadata, which is quite literally data about the data. Data about data means that in addition to the things that we're recording, there are all kinds of information about the who, when and why we're collecting it. And this can be valuable to know down the road. Let's take a look at some metadata in our catalog. Inside of our data files folder, I've got this Washington state folder, and inside of there we've got some points here in this dairy2010.shp file.
Now right now I'm previewing the geography of all of these points. But if we switch over to this description tab here, we can find out some additional information about this dataset. The metadata provides a summary that tells us that this point location is the milking parlor for dairies in Washington. This is interesting because a dairy farm can be a really large piece of land, so that this particular data set very deliberately decided on a more precise way to consistently locate farms across the state. It also further describes the way that attributes were assigned to a farm down here. Where it says that small farms included 199 mature animals.
Medium farms included up to 699 animals. And large farms were over 700 animals. Now just looking at the attribute table if I switch back to the Preview tab here. And switching this preview from geography to table. We can see over here on the right that the dairy size attributes, large medium and small, but it doesn't tell me how many animals are on the farm. And so now that we've explored the metadata, we know what these attributes mean. Let's take a look at another one. I'm going to go into this Washington State file here, and take a look at the Washington shape file. Here we have a column called cardo-sym, which probably stands for cardographic symbology.
Now here we have attributes of four, nine and if I scroll down, we'll see some ones in here as well. To find out what these mean, let's go ahead and check that metadata. I'll go over to the Description tab and we'll see that we don't really have much to go on here. It gives us a summary, and it also gives us a quick description about what this data set is, but it doesn't describe what that column means. Now there are a couple different standards for how metadata is collected and stored. By default, ArcCatalog only displays a brief description. To get additional details, we need to go up to the Customize menu here, and go ahead and check out the ArcCatalog Options.
Inside of that there is a tab devoted to metadata. We'll take a look at that, and here we have this metadata style drop down list. By default the switch is to this Item Description which is a brief description about what this file is. I'm going to click this drop down list and choose a different profile. This North American profile here. This will give us more details about each file. I'll go ahead and say Apply. And then say OK. And now I just need to switch over to the Preview tab, and then come back to the description to refresh the metadata screen. Now see I take a moment here to re-update, and I'm going to go ahead and say no, I don't need to upgrade the Metadata file at this time.
And now I can go ahead and scroll through and see a lot of additional details about this particular file. We can see its extent, and which resource constraints and so on we have. Let's keep scrolling down and see if we can find out what that CARDO_SYMB column meant. And right here we have this field description, Cardo Symbology. And we can see that the list of values, one, means the Washington state land areas. The value of four means all water areas, and the value of nine are land areas outside of Washington state. So we might be able to use that to help symbolize this outline later on. Let's take a look at another one.
So metadata's obviously a great thing to have but unfortunately metadata doesn't automatically come with all geospatial data files. If you're creating your own data files, then it's up to you to remember to put in the extra effort and provide this valuable resource. Let me show you how to add metadata to a file. First, let's go ahead and change that display back to the simpler description so you don't have so many fields to fill in. We go back to Customize > ArcCatalog Options. I'm going to switch the profile back to the Item Description. And say OK. Now, I'm going to go up into this Seattle folder here, and take a look at this Heritage Trees file here.
If I go ahead and preview it, we'll see that it just has an attribute table. If I go down to the preview. I don't have the option to preview geography. In fact, this is just an attribute table. This doesn't have any shape components to it at all. Now if I go to the description, and you'll see that there's no summary information whatsoever. So let's go ahead and fill in some of these details. First, I'm going to go ahead and double-click on this data files info text file to get those descriptions. This is a file that I've created for you that describes where all of the data files in this course come from, so you can go ahead and explore those sources on your own. If I scroll down to the bottom, we'll see the City Data.
And down here if I scroll a little further, we've got the heritage tree CSV file. And a quick description of where it came and what it is. So I'm just going to copy this line outta here, locations of the Heritage's Tree program, and we'll copy that whole line here. And I'll switch back over here into our catalog. Now I'm going to go ahead and press this Edit button right up here on the top and that'll open up the Metadata Editor and I can put in a few tags here. So maybe this is Seattle, trees, and any other keywords that you might want to search by. We're going to scroll down a little bit, and in the summary section, I'm going to just paste in that text that I had just copied from the text file there.
And we can scroll down a little bit further, and maybe in this credits section here, I can put in the website where it came from. So I'll switch back to that data files info and we scroll down again. I can find the website where it came from, data.seattle.gov and there's the URL here. So I'll copy that. And we'll place that in the credits. And then at the very bottom, we get to specify what scale levels this data's appropriate for. Now your probably not going to be mapping Seattle trees on a global scale map, so let's zoom this in a little bit. And we'll say that this data set is probably valid for something maybe in the range of a city map down to the size of a building or so.
So that's a good set of metadata. We can go ahead and save it. And now we can take a look at our metadata. If I look at the description here. I can scroll down ,and I can see that now we have a summary description of what it is, and even a website link that goes straight to the site that this page came from. So metadata when it's available is an extremely valuable resource. And you should always look for it when you get a new data set to make sure that it meets your needs. And when you come across a dataset without metadata or when you create your own Geospatial data files, take a few minutes to fill in some of the details to ensure everyone knows who made the file and why.
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