DATA CARDINALITY
Problem
Politicians are entrusted with maintaining and improving the well-being of their constituents, and job creation is one measurement often used to assess a politician’s performance. My job is to map and analyze job creation in the various House and Senate districts in North Carolina based off survey data.
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Analysis Procedures
For this analysis, I will use the provided table of survey data as well as data of North Carolina Senate and House Districts from NC One Data and US ZIP code data from ArcGIS. After obtaining all of the necessary data, I began by loading the Senate and House District layers to the map, and using their projections for the remainder of the analysis. Next, I loaded the USA ZIP Code layer and used the “Select Layer by Attribute” tool to select only ZIP codes in North Carolina. I then exported the selected data to create a new shapefile consisting of only NC ZIP codes. Next, I opened the attribute table for the survey data and summarized the table to display only one row for each ZIP code, and summing the number of employees for each ZIP code to determine the number of jobs created in each ZIP code. After this, I joined the summarized survey table to the NC ZIP Code layer based on the CODE. Lastly, I joined the appended NC ZIP Code layer to the House and Senate District layers to create two shapefiles and two maps representing job distribution based on each district type.
Figure 1. Diagram of methods.
Results
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Figure 2. Original ZIP code data over a polygon of North Carolina.
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Figure 3. Selection of North Carolina ZIP codes.
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Figure 4. Tabular join of Jobs Created table to the ZIP code layer.
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Figure 5. Spatial join of Senate and ZIP code data.
Application & Reflection
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Problem Description: Remote sensing of nutrient deficiencies in field crops can result in excessively large amounts of data that are challenging to organize and analyze. In my research, I could use tabular and spatial joins to organize my data into a single file or layer. For instance, I could join ground truth data regarding my field plots with the attribute table obtained from my UAV-based data. I could also join data of known nutrient deficient plants with soils data. Both of these would make analysis of my data significantly easier.
Data Needed: For this analysis, I would need a table of my ground truth data, such as plant height as well as foliar and soil nutrient concentrations. I would also need my UAV-based data, such as an orthophoto and digital elevation model (DEM) of my research site. I would also need to convert my plot map into a polygon and/or point layer.
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Analysis Procedures: I would begin by using a tabular join on the ground truth data with the orthophoto. This would help me to spatially reference the ground truth data. Next, I could use a spatial join to combine points representing nutrient deficient plants in my research plots with soil types in my field. Both of these procedures would greatly simplify my analysis and better organize my data.