Data Cardinality

As part of the assignment, I was given the results of an industrial extension jobs survey (item 1 in the Assignment Data section) and was asked to report to Senators and House member the exact number of jobs created in their districts. EMPLOY_SUM is the variable or field of interest in the data table provided to me, which represents the number of jobs created by a company. The senators and members of the house want to know of the overall performance of neighboring districts. The best way to communicate this is through two maps. The main purpose of this assignment is to join tabular data to geometry, namely two spatial data based on a common attribute. Integrate and analyze data in various format visualize this data on a map, while also being able to apply appropriate analysis techniques for different types of decision-making objectives.

Strategies: For this problem, I used ESRI's ArcGIS Pro 2.8 along with the Geocode Toolbox's Create Address Locator and Geocode Addresses tools. For this project, the instructor provided number of jobs created in their districts. “EMPLOY_SUM” data within in an Excel spreadsheet that served as the main input data. I used vector shapefiles of House 18 USSupCT (House Districts), and Senate 18 USSupCT (Senate Districts) polygon layers were used as reference data. For Data cardinality context, I also obtained “USA Zip Code Points” data from ArcGIS online. To complete this assignment, I used tools such as Add join, export table tool and spatial join.

Method To complete this assignment firstly, the excel spreadsheet had to be converted into a table. After that, a polygon layer for House 18 USSupCT (House Districts) and a polygon layer for Senate 18 USSupCT (Senate Districts) had to be added to the map as well as a layer for USA Zip Code Points. To map my findings based on attributes from all tabular data, I opened the attribute tables for all my data. I calculated a summary statistic of the number of employments for each zip code within the table (many to one relationship). Next, I performed add a join, by using the calculate field tool to match the variable type of our data to the zip code point layer. After calculating the fields to match the variable type I added a join to combine their attribute table sum of jobs per zip code to the ZIP codes point (one to one) no new feature class was created. The last step to completing assignment and obtaining maps that displays information on both the House 18 USSupCT (House Districts), and Senate 18 USSupCT (Senate Districts), and the number of jobs, I performed a Spatial join. (Many to one) by matching each sum of jobs created per zip codes to the senate or house layer.

Process diagram

During the completion of this assignment, I learned about the how to effectively map collected data represented on an excel spreadsheet using tabular data as a reference data as well as the meaning of data cardinality and why it is important to understand which relationship type is present. Additionally, I learned how to recognize a text data left justified from numeric field right justified. Finally, learned about the meaning of a spatial join which is a tool that joins the attributes of two layers based on the special relationship between the features in these layers.

New Problem Description: Compare with income level and number of restaurants in Raleigh in each Zip code to analyze if there is a relationship between the two and see which zip code has the most restaurant.

Data Needed: Tabular data containing number of restaurants per zip code in Raleigh could be done thru a survey presented in an excel spreadsheet, polygon layer containing field income level and zip codes from U.S census website.

Analysis Procedures Firstly, the excel spreadsheet will be to be converted into a table using Excel to table then. I calculated a summary statistic of the number of restaurants for each zip code within the table (many to one relationship). Match field type in my table to the field type in the polygon layer attribute table. Use add join tool to combine their attribute tables based on Zip code field., I performed a Spatial join. (Many to one) by matching each sum restaurant per zip codes to the income level polygon layer then symbolize accordingly.