Data Cardinality

A report of the total number of jobs created in each House and Senate districts of NC State

Problem and Objective

Data cardinality involves exploring and modeling the numerical relationship between rows of one table and rows in another. The purpose of this analysis is to explore the result of an industrial extension jobs survey in NC State presented in a table. The result of this analysis is to provide a spatial report of the number of jobs created by all the companies within the different house and senate districts in North Carolina and communicated through maps. This will further inform the decision by each Senate and House member of each district about how their neighboring districts perform in terms of job creation and availability.

Analysis Procedures

Strategies: The strategies adopted for this analysis are explained as follows. I used ArcGIS Pro (Version 2.9) software. I used the following Modules/tools for the analysis: Select layer by Attribute tool, Spatial Join tool, Add Join tool, Summary Statistics tool, and Export Feature tool. Data and data types – The data include (a) an excel spreadsheet of the NC State job survey (b) a shapefile of NC senate districts (c) a shapefile of NC House district and (d) a layer package of USA Zip Codes points. Data sources – GIS 520 Fall Semester, NCSU, North Carolina General Assembly websites, and ArcGIS Online database.

Methods: First of all, I dragged the USA Zip Code package layer to the content pane. Using the Select layer by Attribute tool, I queried the feature using a conditional statement (i.e where State is equal to NC). Then from the content pane, I exported the selected NC Zipcode points using the Export Features into the file geodatabase. Using the Summary Statistics tool, I created a summary table of the total number of jobs created in each zip code. I used the Add Join tool to join the Zip code point feature with the Zip code Job creation table using their respective ZIP code columns (Text fields). With the active temporary joined table, I selected the valid sum of jobs using a query (i.e where the total jobs created is greater than 0) using the Select Layer by Attribute tool. From the content pane, I exported the selected points as a feature class into the file geodatabase using the Export Feature tool. By selecting the Senate polygon from the content pane, I created a new polygon feature representing the total sum of jobs created within each Senate district using the Spatial Join tool (where the Join feature is the point feature of the valid Jobs in each Zip code, the join operation is one to one, match option is intersect; output fields is total jobs column and merge rule is Sum). Repeating this same join technique, I created a new polygon feature with the total sum of jobs created within each NC House district using the spatial join tool by selecting the House polygon from the content pane. I labeled this feature using the total sum of jobs per district and other labeling properties (Symbols, Halo, Appearance, etc) were explored. Also, the symbology of the two features was explored for proper presentation of features on the map.

Workflow diagram of the analysis

Results

The map above shows the total number of jobs created by all the organizations in each Senate district of North Carolina State, USA.

Application & Reflection

Problem Description: As a forest landowner with two different plantations of Loblolly pine in NC state. The information about the timber productivity of these different plantations based on their respective locations is important for future investment and accountability. It will also help provide insights about land fertility due to differences in soil characteristics, access to water, elevation, etc. The purpose of this analysis is to explore the total volume of matured trees per plot that was harvested from each of these plantations at the end of a planting period given that the same number of plots and plot size is maintained for both plantations.

Data Needed: The data needed include: (a) an excel file of tree records for Plantation A (b) an excel file of tree records for Plantation B (each containing tree ID, volume (ft3), latitude and longitude information), (c) Polygon shapefile of an equal-area rectangular plot for Plantation A (d) Polygon shapefile of an equal-area rectangular plot for Plantation B and, (e) an excel file of tree characteristics (heights (feet), diameter (inches) and tree ID for all trees).

Analysis Procedures: The excel table for both plantations is saved as standalone tables using the excel to table tool. The attribute table of the tree points will be inspected to ensure that the column/field for volume is in numerical type. Using the XY Table to Point tool, display the location of individual trees on the map for both plantations. The tree characteristics (heights and diameter) of individual trees are matched to their respective point locations on the map using the Add join tool where the Input table is the Tree points, the Join table is the excel tree characteristics, and the join fields are the Tree IDs. Using the Select Layer by Attribute Tool, select only trees with a 4-inch diameter and above (where the Diameter is greater than or equal to 4). The total number of trees per plot for Plantation A will be obtained using the Spatial Join tool where the target features is the Plot polygons, the join feature is the tree points, the match option is Intersect, the search radius is Decimal degrees, the output field is the Volume and the Merge rule is Sum. The same process will be repeated for the Plantation B polygon layer. The result of the analysis is two polygon features representing the total volume of trees per plot in Plantation A and Plantation B. The symbology and labeling properties of these two polygons will be explored and the label will be added.

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