Working with Attribute Data using the FAA Airman Database Part 2

 Working with Attribute Data Part. 2


This post is a part 2 to the previous Working with Attribute Data post. We will incorporate the FAA Airman Database and understanding how tables can be joined using common fields. By using table joins, we will perform attribute based queries and generate new feature classes with those queries. We will also work with importing shape files into the geodatabase. 
  • Establish a folder connection by right clicking the folder and selecting add a connection
  • We will be using Census data that was provided to me by my professor 
  • Copy the cb_2018_us_state_5m shapefile into your folder for this project

This is a shapefile, but as we know, shapefiles cannot be used for many forms of analysis, and their size is greatly reduced when brought into a geodatabase. We will now want to import this.

  1. Open geoprocessing tool and search for "Feature to Feature"
  2. Import this into the geodatabase and name it stateboundaries
Once the stateboundaries is loaded in, it will need to be reprojected. 

In order to re-project the features:
  1. Under analysis tab, click tools
  2. Search for "Project"
  3. Set input as stateboundaries. You can select a feature class that has the lambert conformal conic by selecting that layer in the output coordinate system list. 
  4. For Output, be sure to add Lambert on the end so you know that one has been re-projected
  5. Once it has been re-projected, you can remove the original from contents 
Figure 91: Stateboundaries In Lambert



We are now going to do some analysis with the airmen points. Because this analysis takes some time with such a big data set, we are going to start with working within just Indiana. We are going to use a location query by using the "Select By Location" tool. There are 3 different ways to use the select by location tool.

  • Intersect: The features in the input layer will be selected if they intersect a selecting feature. This is the default.
  • Contains: The features in the input layer will be selected if they contain a selecting feature.
  • Within: The features in the input layer will be selected if they are within a selecting feature

We are now going to engage in some spatial analysis. To start, we are going to select the Indiana boundary feature and the Indiana airmen feature.

  1. Within the geoprocessing tools type "Kernel Density". This is the simplest tool for finding where points are clustered.
  2. Run the tool using the default settings.

Figure 92: Kernel Density For Airman In Indiana

We are now going to do a hot spot analysis. It is kind of like kernel analysis, but using information gained in a spatial join. We are again just going to use Indiana. We need to extract only Indiana information from our polygon feature classes. 

  1. Perform an attribute query of all Indiana counties and create a feature class. 
  2. Use the "Clip" tool to clip out the Indiana counties
    1. Input Feature: uszipcode
    2. Clip Feature: Indiana state boundary feature file
    3. Name: Indianazipcode
Figure 93: Indiana Counties Clipped 


You want to do the same clip operation for the fishnet_10km feature class


Figure 94: Fishnet_10km Clipped


Now that we have prepared our data, we can do a spatial join operation. Within the Geoprocessing tools tab, search for Spatial Join. 

  • Target Feature: Indiana counties feature class
  • Join Features: Indianaairmen feature class
  • Output feature class: Indianacounties_joined
  • Join operation should be one to one
  • Match operation should be intersect

We are going to do the same operation for the Indiana zip codes feature class, and the Indiana fishnet feature class.


A spatial join is a type of table join operation where one layer's attribute table are appended to another layer's attribute table based on the relative locations of the features in the two layers. This gives us the join field count which we are going to use to perform a hotspot analysis. The hotspot is similar to the kernel operation, but now we are looking and the number of points within a given area such as the county, zipcode, or 10km square.

Search for "Optimized hotspot analysis" with in the geoprocessing tools window
  • Input: your joined 10km fishnet.
  • Output: Indiana_fishnet_10km_hotspot
  • Analysis Field: Join_count

Now that we've learned these 4 different types of analysis, we can create a map showing them all as a way to portray all the airmen in Indiana.

Figure 95: Indiana Airmen Spatial Analysis



So before we were just working with Indiana as a way to practice and learn with a smaller dataset, but we can run the same spatial joins and analysis on the entire continental United States. Just follow the same processes as before, but instead of using the Indiana clips we made, use the continental United States. Just make sure everything is projected in Lambert.


Figure 96: Continental United States Analysis


The map in the top left shows the density of airmen with type P license. You can see the dense areas along the Eastern, Sothern, and Western border. There are a few spots in the Midwest as well. The map on the right shows the density of airmen with a remote pilot license. The density follows a similar pattern to the pilots with P type in terms of location, but you can see some areas aren’t as dark signifying less people. Finally, the bottom map shows a hotspot analysis of all airmen in the continental US. You can see it is evenly spaced through the country, but there is a large string in the north eastern coast.



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