There are many many sources of GIS data out there. So, I'm just going to walk through a typical example of finding some open data which means data that basically has very fewer or no restrictions on it. So, we can find some data download it, do anything we need to do to get it ready and then put it into ArcMap, and then we'll be able to use it. So, part of this is to show you how that works but part of it's also to talk about the decisions that might have to be made along the way. Okay. So, what I have here is the City of Toronto open data catalog and I'm going to look for some road data for the city. So, road data is very common it's something that you would want to use for lots of different mapping purposes even if it's just for as a base map or something to show in the background, for contacts, or maybe it has to do with navigation, there's lots of reasons why you might want to use roads, so let's see if we can find it in here. Okay, so, there's a search text box here so, I'm just going to type in "roads" and you'll see that the things that come up first our road restrictions, traffic road restrictions, wellbeing in Toronto transportation. So, I'll have to go to the second page, more on wellbeing Toronto. You'll notice that here, third listing down on the second page is this thing called Toronto centerline, and that's says boundaries, road restrictions for us, and land cover, so there's a few things here. So, it may not be obvious to someone at first but that actually is the road data for Toronto. The reason it's called centreline data is often when people are creating this data, they're tracing and digitizing it off of your photos and they'll trace along the central line of the road, literally the painted line that you see down the middle of the road, often will be used as the defining line for it, so that's the central line of the road and so they referred to it as central line data. It's just one of those things that's a bit of terminologies that may not be obvious to you at first, but that's the terminology that the people creating the data used and so that's the way they've chosen the list it. So, sometimes, these listings are more obvious or more user friendly than others, so I just want to point things out like that as it may not always be really clear. So, in this case, I happen to know that centreline data is road data, so, let's go with that. If I click on that, it will take me to a listing here that says the Toronto centreline is a dataset of linear features representing streets, walkways, rivers, railways, highways, and administrative boundaries within the city of Toronto. So, it's actually more than just road data, we're getting a lot of bonus features as well if you want. It says in addition to retaining historical archives, threaded archives are also retain that record splits and merges of address points of linear features in a very features. All features are linked and integrated. So, I think what they're saying there is that there's a record of changes that have been made to this dataset along the way. So, if new roads have been added or they've been split in some way, but that's all documented in some way and so that the features are linked, I believe they're saying here that includes topology which basically talks or has to do with how the features are connected to one another. Okay. So, then we get to the third part which is on data download, and you'll see there's three different options here. The first one here, says, Toronto centereline MTM 3 degree Zone 10 NAD27. So, you have to get used to looking at these things and decode them a bit because there won't necessarily be a lot of explanation. I happen to know that MTM is a Modified Transverse Mercator projected coordinate system that the City of Toronto likes to use for a lot of their data. I'm a big fan of it myself only because when you try to open that data in the GIS software like ArcMap, there is no preset coordinate system setting for MTM. There is for UTM which is Universal Transverse Mercator but not for MTM. So, I feel like, well, maybe there might be another option that's better for me, and sure enough if you look at the second option here, says Toronto Centreline WGS84-Latitude and Longitude and what that means is WGS84 is a datum, okay, that's fine, and latitude and longitude means that the data is in a three dimensional coordinate system where there's angles being used for the coordinates, it has not been prorejected it's not using a grid coordinate system like meters or something like that. So, why is that important? Well, the thing is that if it hasn't been projected yet, one is that I'm I'm quite sure that ArcMap or any GIS software will be able to open latitude and longitude data quite easily because you don't have to worry about it already having some preset coordinate system for projection that may or may not be in there, so, you can just use the regular 3D latitude and longitude. Second is that it's just something that I know that no one's messed around with it, it's in the most basic default, format in terms of the coordinates, it hasn't been projected and reprojected over and over again, so, I don't really have to worry about it too much, it's my go-to thing is. Is if I'm getting data from somewhere, if I can get it in latitude and longitude, then I know it's probably going to work no matter what it is I want to do with it. The third option which we're not going to do here is that you can view the data so what that does is opens up a web map browser, so, if you're not sure what the data is, you want to have a look at it first, you can do that, or maybe you don't want to download it, maybe you just want to be able to access it and view it, so they've they're giving you that option as well which is nice. So, I'm going to click on this and download the data and you'll see that it's called centreline underscore WGS84.zip. So, that's a compressed or zipped folder. I'll say, "Save". Now we can see this file in my Z drive or Z drive, and what I'm seeing here is this little zipped folder with a dot zip extension, and by the way, this is a little tip but I think it's super important for anybody that's working in GIS. Is in Windows, make sure that you check this box to be able to view file name extensions. Often, I find that the software, or the operating system, or Microsoft, or whatever thinks they're doing you a favor by hiding that information to simplify things, but I want to know what that is, I want to know whether it's a dot zip or a dot SHP, or dot GDB, whether it's a shapefile or geodatabase. So, having that information is important so, I would really recommend that you check that box, okay. So, I can click on this and see the contents of this folder, but if you're not familiar with zipped folders, all that means is that they've compress that into a smaller file to save space and make it easier and quicker to download. So, we can open up this zip folder in Windows and it will show you that data, and depending on the software and what it is that you're doing, sometimes you can access that without having to decompress it or unzip it. But I always feel like it's always better just to unzip it first anyway and then you don't really have to worry about whether it's going to work or not. So, even though I can see the data here, what I would highly recommend you do is right click on your zip folder, and say extract all. That will bring up a dialogue box that says select the destination and extract file. So, I'm going to just put it in the same drive and folder that I already have selected. So, now it's decoding that data if you want, uncompressing it, putting it so, it's creating a copy of that data that's not compressed anymore. If I go up one level here, you'll see that now I have a zip version, that's when I downloaded, and unzipped version which is just a regular file in a folder, that's the one we're going to work with. So, now if I open that up, you'll see that there's a bunch of files in here and one of the clues of the file format here is that it's an SHP file here, you see that? That tells us that it's a shapefile which is a simple old GIS data format, it's been around for many years and it's often used as a a default way, a default file format for transferring files or exchanging files with other people. So, even though it's quite old and simple, it's still preferred by a lot of people because pretty much any GIS software will be able to open a shapefile format. A couple of other things to pay attention to or to watch out for when you're downloading data, is often you'll see things like this which are README file, so, it says README there, README there. So, those are often little extra files with explanations about, it might be how to use the data, are there any limitations on it? Maybe some explanations about the metadata or the data dictionary, so, what did the abbreviations in the fields mean? How are the fields generated? How is the data collected? It all depends on the dataset and where it came from, but I would highly recommend that you have a look at that README data because often when you open the dataset, it won't really be clear what it is you're looking at a first without that extra information. For example, if I open up this first README file, it's just a simple text file, I'm just opening it here in the notepad app. Once again, you have to get used to being able to just look at these things and decode them or make sense out of them. So, what they're telling us here is that we have centreline ID, LFN ID equals linear underscore, name underscore ID, street name ID, blah, blah, blah. All these different things in here. So, depending on what you want do with idea, you don't necessarily have to know the meaning of all of them, but you have to know enough to be able to use it for what your purposes are, what you're interested in. So, you have to be able to quickly sift through this kind of stuff and say what is this? What am I looking at? Is it useful to me? So, one of the things that jumps out at me is that we have things like feature code. So that's the street classifications. So that might be useful, because often roads are classified in things like, it might be highway, expressway, local road, arterial road, laneway, things like that. So, we're looking for things that will help us tell those apart because we may want to give them different symbology in our map. We might want to make one set of roads red, another one black, different thicknesses. So, what I'm looking for are things that will help me determine that. So, then there's something here called f code desk feature code description, so that's the description of the street classification that might be useful. So we'll pay attention to that, that might come in handy when we start to load the data. Let's look at the second ReadMe file here. This says, since we've tonal center aligned data comprises city tonal, center line, medium-scale, digital mapping data. This includes blah blah blah, goes on and on, data limitations. So, this gives us a warning about the accuracy of the data here, right? So for example it's saying it's represented a two point five meter or eight foot positional accuracy. So they're giving us some idea of the limitations of the data, they're telling us often this is a bit legalistic but it can be important is that, look, don't blame us if you try to use this data beyond the accuracy for which it was intended and they're telling you what it was designed for and how it should be used which is great. That's one very important thing to have. We have linear feature definitions, so things like highways designed for fast long distance travel with restricted access to sustain high speeds and so on. So they're giving us these categories like arterial road, collector road, lane local road and so on. So that again, it may not be obvious to us especially if you don't work for a city. How they define these boundaries or these roads or what they mean, what the differences between this and that. so it's very helpful to have this kind of extra metadata, to help us sort through that kind of information. So now I've opened up ArcMap, I'm going to open the data that I collected, or downloaded. So now I've got ArcMap open and I'm going to add the downloaded data that I unzipped, the TCL data or tonal center line data and let's have a look at it. Okay. So, I'm going to go to my Z drive, that's where I have it located and nothing's happening. So, a little tip there is that I had ArcMap running before I downloaded the data and so when I went into the Arc catalog pane looking for the data, it hadn't refreshed, it didn't know that it was there yet. So all I did was I hit the F5 key or you can right click and say 'Refresh' either way. So that it's basically kind of telling Windows to trigger it, to say can you update this list of files for the program I'm using and that's what happened. So that's all I did there. Okay. So we have our center line folder and inside that there's the shape file, so it has that Dadas HP extension that means it's a shapefile, and it's a WGS84, so that means this latitude and longitude and all I'm going to do is just drag that onto my map and there we go. So that data has not been projected, so I always notice it's withdrawn on and depending on where you're mapping or how familiar you are with it you might notice this or not, is that this should be almost a right angle and same here and the fact that the whole thing looks a little bit kind of skewed, is because it hasn't been projected yet. That's a topic for another segment but it's just something that I noticed. We have successfully downloaded the data and we now have it inside of ArcMap. So let's have a little quick look around, I always do this when I get a new dataset, is I just kind of explore it a little bit and see what I can find. So, looks good. I automatically kind of zoom into the city or the- for some reason I almost always automatically zoom into where I am, so this is the University of Toronto area and really it's just a matter of kind of looking around, making sure that it downloaded correctly and the next thing I would often do is open the attribute table and just get a sense of what it is that I'm looking at. So, there's a bunch of different fields in here, there's a lot of data that's nice to see and this is the one I'm interested in here is the FCODE_DESC or description. So, this is telling me the things I want to know in terms of the different types of road features that are inside this dataset. So for example if I wanted to, I could go into the properties symbology. Now I could just give them all the same symbol, I could just say I'm going to make them all black, like that. What I want to do here is give different categories of line, a different symbology. So now that I know that I can use this FCODE_DESC, and I can in this case I add all values and so what it's done is it's read through that attribute table and found all the different types of features that are in that dataset. So there are access roads, passways, collectors, collect ramps, creeks, tributaries and so on. Now you don't have to keep all of these, for example I might say, you know what, I don't really want the creeks and tributaries in there, I can just remove that from the list if I want, say maybe the same four ferry routes or juice statistical lines, whatever it is that you want or don't want to include. Now, if I just say Apply, now I have a map with a randomly decided or selected set of symbology, that the software has just decided this is what I'm going to give it. I don't know about you, but I find that this isn't very useful, it's hard to see, but one thing I did want to point out here at least, is that so now we have a list in our table of contents for each of these different types of road features. So I could go back in and change the symbology. I won't do the whole thing, but I just want to show you is that for example, an express way I might want that to stick out. So I can just double-click that and say I want that to be a really thick big red line, I could say let's go down a little bit a major arterial, might be a big black line like that, a minor arterial might be a thinner black line and you can go through the whole thing. But the idea here is that you can now assign different symbology to different types of features inside the same dataset. So now we've downloaded the data, we made a decision about which type of format we're going to download, so the fact that it was projected or not projected, it was a latitude longitude, it was a shape file, I was able to add it into my software and now I'm able to use this dataset like I would with any other.
