Video: Joining dataArcGIS makes it easy to bring additional data into your mapping projects through the use of joins. By joining data together, you can take advantage of attributes and geometries that you already have, and have the flexibility of adding additional information on the fly. There are two types of joins that we can use. First, we can join spreadsheet data to our GIS data layers through a common attribute field. If the same name is in both tables, then we can match them up to bring across the additional fields. The other way is to join data by their location, where we can take advantage of the geography portion of our geographic information system, and attach attributes based on the features that fall inside of other features.
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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.
- Understanding vector vs. raster data
- Modifying metadata
- Adding data to a map
- Importing data from online providers
- Labeling features
- Joining data
- Clipping data to a study area
- Working with map layouts
- Creating a legend
- Printing and exporting the map to a file
ArcGIS makes it easy to bring additional data into your mapping projects through the use of joins. By joining data together, you can take advantage of attributes and geometries that you already have, and have the flexibility of adding additional information on the fly. There are two types of joins that we can use. First, we can join spreadsheet data to our GIS data layers through a common attribute field. If the same name is in both tables, then we can match them up to bring across the additional fields. The other way is to join data by their location, where we can take advantage of the geography portion of our geographic information system, and attach attributes based on the features that fall inside of other features.
Let's take a look at both options. I'm going to first start with our counties here. I'm going to right-click on it. And explore the attribute table. All of the attributes that are currently in our county shape file, came from the U.S. Census, so it has lots of population information and other types of statistics. Let's go ahead and close the attribute table. I'm going to open up the catalog window over here on the right. I'm going to go into the data files folder and inside of the Washington State file. Also we have an Excel file that includes 2012 election results. Now, I can expand the Excel file to see the individual worksheets that are within that Excel file.
I'm going to click this 2012 results, and drag it, and drop it back over here on my table of contents to add that new data table to my ArcMap project. Now I can join the 2012 election results to my county file through the use of a join. To do that, I'll right-click on Counties, I'll go to Joins and Relates, and I'll say Join. Here I have a couple of options on how I want to join this data. I can join, based off of a spacial location, or the option that we want to join by attributes from a table. Here we're going to choose the field in this layer, that the join will be based on.
This it the common field that will appear in both tables. I'm going to choose the name field and we're going to match that to the 2012 election results based off the county field in that table. We'll go ahead and say OK to run the join and now when when I explore the attributes for our counties. Go back into the attribute table and scroll all the way to the end. You'll notice that we have some additional columns here that are Democratic votes, Republican votes, the percentage of Democratic votes, the percentage of Republican votes, and the Democrat margin percent, and the overall winner of the election.
Now we can use these attributes as part of a symbology. Let's go ahead and close the attribute table. I'll double click on county. In the Symbology tab of the layer properties, I'm going to click on Quantities here. The value field is going to be at the very bottom, the Democrat margin percent. So this is the margin of percentage of Democratic votes. Here we can see that the range is from negative 45%, in which case the Democrat lost, to positive 40%, in which case the Democrat won. Now typically, Democrats are symbolized by a blue color and Republicans are symbolized by a red color.
So we can see our symbol wrap is going the wrong direction. I'm going to go ahead and click on the symbol and say Flip Symbol Colors, here. And if you need to choose a different symbol wrap, use this color wrap drop down list here to choose one from the list. Now I've got it set up correctly, where red is the Republican winner and blue is the Democratic winner. I can say Apply to apply that to the map. And here we can see the differences between the eastern half of Washington and the western half of Washington. Counties that are highlighted in blue are strongly Democratic counties. And counties that are in red are strongly Republican counties. Counties that are in this green color are slightly Democratic.
Counties that are in orange are slightly Republican. And counties that are yellow are evenly split between the two parties. Okay, let's go ahead and close the Layer Properties here. And I'm going to turn off the counties and take a look at the next one. Here I'm going to go back to my catalog, and I'm going to load in the fish net file that we created in the last movie. I'll just drag and drop to add it to the map. That adds the fish net grid in the background here. Let's also go ahead and turn off the state boundary, and we'll leave the dairies and the fish net open. Now I want to join the dairy points to the fish net grid, so I can get a count of how many points fall in each ten meter grid.
In the fish net grid, I'm going to right-click, and go to Joins and Relates>Join. This time I'm going to choose to join the data from another layer, based off of spatial location. It's going to ask me which data layer I want to join, and I'm going to choose the dairy option, and I'm going to go ahead and down at the very bottom, choose where I want to save an output file. I'll click the Browse button and we can go ahead and save it into our working folder here, and I'll just call it Join_Output. And I'll change the Save As type to a shape file. That will create a new shape file called Join_Output in our working folder that includes the results of this tool.
Now go ahead and say Save and OK. After the tool runs, we get a new fish net grid added to the map called Join_Output here at the top. And if I right click on it and go down to Properties. We can explore it's symbology by going to Quantities. The value is going to be a count. This is the count of how many dairies fell in each location. And again, we'll choose an appropriate ramp and we'll say apply. And we'll get a heat map effect that shows me where high concentrations of dairies occur within Washington state. Now, if I want additional context, let me go ahead and close the Layer Properties, I can turn on my Washington boundary.
I'll switch my Table of Contents to list by drawing order. And I will drag the Washington boundary up to the very top, and we'll create a hollow fill. Here with no color, so I can see the outline of Washington state on top of the fishnet grid. And maybe I'll change the outline color too, while I'm here. There we go. So data is always being updated and edited, and sometimes you just want to map something that doesn't come with any spatial information, or sometimes you need a summary of complex data sets, and want to see the bigger patterns of what's going on. By using the two join methods, either join by attributes or join by spatial location, you can make sure that your maps are always up to date without having to permanently modify your GSI data sets every time something changes.
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