Database Cardinality Issues in ArcGIS

Problem:

Politics are complicated, and most constituents would like to see that their district is making progress. An extension industry survey was done to determine the amount of jobs created by industries in North Carolina. The politicians would like to see how many jobs were created in their districts. This involves taking several companies in the same senate and house districts and creating a sum of new jobs in each zip code.

Analysis Procedures:

Database cardinality refers to the relationship between tables of data. The industry data was in a tabular format, so in order to analyze it geospatially, it is important to join the tabular data to spatial data (in our case, by zip code).  The process of connecting tabular data to spatial data is critically important in solving many GIS Problems (Fig. 1). The tabular data was added to ArcMap. The job survey data was then summarized by zip code (so a many to one cardinal join could be set). The zip code field in the survey table was then joined to a zip code points file via a table join. This new layer was spatially joined to the NC Senate and House spatial files, summing up the total of new crops created. Symbology was then adjusted to display the counts on a map. The Procedure-Log can be found here.

flowdiagram

Figure 1: Workflow diagram for database cardinality.

Results:

M1_NC_extensionjobs_by_housedistrict

Figure 2: Job creation by North Carolina House Districts (click for a larger picture).

M2_NC_extensionjobs_by_senatedistrict

Figure 3: Job creation numbers by North Carolina Senate District (click for larger photo).

Application & Reflection:

Problem description: The skills and concepts from this exercise are useful for answering any research question that has a spatial component. Survey data and address files are commonly kept as a spreadsheet or database and will often have a field that could be spatially represented (e.g. county, area code, zip code, etc.). These database files can be joined to the research data giving the data a spatial context. As previously mentioned, I am in the process building a sheep dairy just north of Raleigh, NC. I identified the two metro areas that our marketing needs to target (the Triange, and Greensboro).

Data needed:  I went through and created a list of every high end grocery store, brewpubs, wineries, and gift shops where gourmet cheese can be sold.
Analysis procedures:  A many to one cardinality was used to determine the number of potential sales outlets per zip-code. This can then be spatially joined to county wide or metro-wide shapefiles to determine which market is potentially more lucrative to start our marketing efforts.

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