Data Cardinality

Identifying the Number of Jobs Created in Each Senate and House District in North Carolina

Problem and Objective

I have been asked to create two maps for the North Carolina state senators and house members. They want to know how many jobs have been created both by senate districts and the house districts.

Analysis Procedures

To create these maps I utilized the ArcGIS Pro software. I received a table with information on the number of jobs created per zip code area. I also had to use shapefile data downloaded from the North Carolina General Assembly website for both the House and Senate district boundaries. ArcGIS has a layer package of USA zip code area points that I also downloaded and utilized. Several steps had to be taken to connect all of this data. I used the summary statistics tool, the join table tool, and the spatial join tool to complete this project.

The first step was to add all of the data and the table with the job information to the ArcGIS Pro project. Once this was done, I had to create a new point layer from the USA zip code points of only the zip codes in North Carolina. I also had to create a new table from the provided table of only jobs created in North Carolina. From there, I had to calculate how many jobs were created within each zip code. To do this I used the table with the job information that was provided to me and I used the summary statistics tool. With this tool I was able to sum the field that had the number of jobs created and I could catergorize them by zip code. This created a new, simpler table. The next step was to use the join tool to join the information from the newly created table to the North Carolina zip code point layer. Next, I used the spatial join tool, with the target being the senate district boundary layer and joining the NC zip code point layer. When doing this step I also specified that the attribute field with information on the number of jobs created be summed when merging. Doing this gave me the total number of jobs created per house district. I was then able to repeat this last step with the senate district boundary layer to determine the number of jobs created per senate district.

Results

Application and Reflection

In completing this project, I learned how to properly use join and relate tools. I was forced to gain a true understanding of how these tools work and what their outputs are. Below I describe how these skills can be applied to solve a new similar problem.

Problem Description

A car dealership is interested in learning how may cars they have sold per zip code area in their surroundings. They have asked me to develop a map to display this information so they know where to target advertisements in the area.

Data Needed

The car dealership provides a table of where the buyer of each car lives. I also need town boundary data zip code boundary data. I can find this data from the TIGER/line geodatabase.

Analysis Procedure

I would join the attributes of the provided table to the layer of town boundaries. Then I would use a spatial join, to join the town boundaries layer with the zip code boundaries layer. I would then be able to calculate how many cars were bought per zip code in the area and display the information in a map layout.

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