[MUSIC] Welcome back. Now we're going to get into some GIS concepts and terms. Even though terminology can sometimes be really boring. I think this is where GIS starts to get really fun. You're going to learn GIS-specific information here that you can take in and use when you start using out GIS. In this lesson we're just going to go through these terms here. Points, Lines, and Polygons. Attribute Tables. Vectors and Features. Rasters and Imagery. Geoprocessing Tools. Geodatabases. Shapefiles. Map Documents. And Layers. There is a lot more terminology that you can learn for GIS but these are the fundamentals that I'll get you started. Vector and Feature Data. Vector and feature is relatively interchangeable, so you can call either one of those. The key defining characteristic here is that it's categorical data. It's not numbers one through five with decimal places, it's land, sea, ocean, that kind of stuff, descriptive characteristics are the primary way that you would use vector data. Vector data also has something that we would call an attribute table. It's a data table, sort of like an Excel spreadsheet, if you're not used to data bases, that is attached to each vector or featured data set. And every record in that data table corresponds to features in the GIS space. So if you have something like watersheds, which is displayed in this graphic, each watershed there has a corresponding row in that data table that tells us information about that watershed in particular. The columns are all different attributes that we want to know about in those watersheds. To build vector data you need points, lines, and polygons. Those are the primary vector data that you have in GIS. So points build lines, lines build polygons, with points being connected by an edge to make a line and lines being connected all the way around back to the origin to make a polygon. Points are dimensionless. They are represented with size, but they're infinitely small in practice. Lines are one dimensional. They move in either direction. And each segment of a line can have an attribute record, just like each point can have an attribute record. And then polygons are by default two dimensional and each polygon can have an attribute record. One thing to note though is we can add more dimensions to each of these. We can add a z attribute to them so that they have height data. And then we can also add time data so that. This attribute or the polygons with the lines or the points have more or different information through time and so you can time enable your layers. Raster data is the counterpart to vector data. We talk about GIS data as being either vector data or raster data. There are a few other types but principally these are the two that you're going to use. Raster data you've probably seen before, it's the type of data that your digital camera takes. It's best used for imagery like digital images or continuous surfaces, and what I mean by continuous surfaces is where vector data describes ocean, sea, land like I talked about. Raster data can be those numbers filling one to a million and any decimal place in between. So each location can be really related to the ones next to it because they vary continuously. Raster data is composed of a regular grid of pixels with fixed cell size. So let's deconstruct that a little bit. If you want to think of a regular grid, just draw a Tic-Tac-Toe box for yourself and if all the squares are of equal size you have a raster there. And if you write different numbers in them that's how raster get their values and if I write a five in the top left box of the tic-tac-toe box. That whole area that that raster cell represents has the number five as its value, whatever that number means in this case. If it's elevation, maybe it's five meters tall. And then the one next to it might have a six and so that ones six meters tall and the one below it might be eight, something like that. And so Rasters do that through the whole space that the Raster covers. Imagery is also raster data, just like we talked about. But it's oftentimes multiband raster data where you have multiple values per cell which represent the colors of the color spectrum. So red, green, and blue each have their own values per cell. Rasters can have attributes just like vector data. But it's a little bit different, because in rasters, the attributes are per value not per pixel so if two cells have that value five, they each take the same attribute table record. So you can add more information on what it means for a cell to be a five in that case. But you don't have that per cell, that per feature attribute record like you have with vector data. So that's raster data. Now we want to process our data. How do we do that? We do that with geoprocessing. Each GIS package does this slightly differently, but a lot of them have a variation sort of like ArcGIS, which has geoprocessing tools in Arc toolbox. And that's a slide out panel you'll see when you start using ArcGIS, and it's full of of algorithms to process or transform data. Everything from really basic stuff of data management like deleting data sets, which isn't totally straight forward sometimes because of the data formats, to complex analysis that involves statistical clustering of your data. So that's where you do all of your processing of your data. They're the work horses of GIS. And, just like programming or physics or chemistry. Hopefully that's not a four letter word for anybody. You use your data as a starting point and you start chaining these tools together to make your work flow to make your analysis work. And, in doing that, you got the output that answers those questions. Geodatabases are where we hold our spatial data. You can hold it in other ways, but geodatabases are nice containers to bring all of your spatial data together. Geodatabases are a general concept, there are multiple types of geodatabases that you can have. You may hear about file geodatabases, personal geodatabases, or spatial light geodatabases ArcGIS can use all of this and you can use them in other systems too sometimes. Geodatabase helps us bring together both feature classes and rasters. Feature class is being vector data. But also our tabular information, our relationship information and our annotation information and even more. Shapefiles which you may have heard of if you've done a little bit of work with GIS systems before Shapefiles are often talked about as if they mean vector data in general but they don't. Shapefile is a specific ESRI format. It's really common, it's a really important format because it's old enough and widely published enough that it's used as an interchangeable format between different GIS systems. So Shapefiles can be as few as three different files to make up a Shapefile and as many as seven files on the disk. So if you happen to send them, need to make sure to grab all of those. So they're a little unwieldy. The other thing is that in being old, they rely on old standards like dBase IV for their data tables, and that makes them pretty limiting in terms of what you can do in your attribute tables. I find them to be slow and large compared to other data formats, but they're incredibly useful and incredibly important because that's how you often can send data to others you're working with. If the other people you're working with are using ArcGIS and also have the knowledge to use the geodatabase you can send it to them that way. But Shapefiles are pretty important for interchange otherwise. Map documents are the work space we use to view and analyze our data. They're where we create maps to send to other people. They're where we our Geoprocessing work files and we load our data into these. But it's only referencing it on the hard drive, and it represents it in the map document. It displays the information. And we can change it's color and how it appears, but that doesn't change the underlying data. That's just for this map document because that's how we're viewing it. We could save that map document, create a new one using the same data and view that data, differently. They're the backbone more or less of your GIS. You're going to work from these in most cases. To build our map document we use layers. Layers are representations of our datasets from our geo databases in our map document. They have appearance at this point. We can see them and we can stack them one on top of the other. To show them together and assess how they look in relation to each other. If you've used Photoshop or illustrator, it's very similar to those pieces of software. So to sum up all of these terminology, we have vector data and we have raster data, those are two primary data formats. Vector data is best suited for categorical or discrete data. And rasters are best suited to continuous data. Attribute tables are and especially important for vector data and to attach additional information, but we can also attach them to raster values. And then geodatabases commonly store our data on disc. And then map documents and layers allow us to represent our data visually for cartographic purposes or to provide a workspace for analysis of our data.
