Embedded Files
Problem
Legislators in the North Carolina General Assembly want to be able to understand the results of a job creation survey geographically across the state. Furthermore, legislators need to see the results by legislative districts so they can see which districts gained jobs, the number of jobs gained and how it relates to neighboring districts.
Analysis Procedures
Aggregating this data through several steps was required to solve this problem. Three sets of data were used including a spreadsheet of survey responses, a United States ZIP code layer package from Esri and shapefiles for the Court Ordered NC House and NC Senate Districts from 2018. I used Select-By-Attribute to reduce the amount of data in input files. These files were joined by common attributes and ultimately spatially joined. The data was analyzed using ArcGIS Pro with the NAD1983 State Plane 3200-meter coordinate system for North Carolina.
The NC Job Creation Survey data included employment by NC residents crossing into surrounding states for work. After converting the survey to a table, I narrowed down the data to only those new jobs based in North Carolina. I repeated a similar process for the USA Zip Code layer to narrow it down to NC Zip codes only. To better understand the survey data I was dealing with, I aggregated the data by summarizing all new jobs by NC Zip Code yielding 45 Zip codes with new jobs. At this point, I noted that both sets of data had a common field of “Zip Code” formatted as text. Using this attribute field, the two sets of data were Joined and re-summarized to check that the Join had worked appropriately. This data was then exported as a new feature layer.
I chose to begin combining this data with the NC State House Districts polygons through a Spatial Join where the join operation was set to “Join one to one” and the field map was set to Employment Sum with the merge rule of sum. I configured the symbology in the resulting layer to fill the districts in a color-coded manner by using Graduated Colors and setting six key values to display the data.
Starting back at the Spatial Join function, this process was repeated using the Senate Districts polygon data to produce a similar map showing new jobs by NC Senate District.
Results
Click images for a larger version
State House members can easily find their district and the number of new jobs added to it.
By finding the darkest shades of purple, the user can find where the largest number of jobs were created.
Application and Reflection
Aggregating various types of data to produce new information and visualizing it is what GIS analysis is all about. From years of working with scientific data and seeing spreadsheets with thousands of rows and hundreds of columns of data the only time it’s made much sense is when visualized in a 2D or 3D display.
Problem description:
The US EPA requires companies that emit air pollutants to perform environmental assessments on the deposition of pollution particles. Their current models for regulatory permitting are algorithms that source data sets, input from a user and report back with a large data table. Visualizing this output and integrating it with other spatial data such as the location of sensitive populations (schools, elder care facilities, hospitals, etc.) can provide insight in to potential problems from long term exposure and develop strategies for minimizing impact.
Data needed:
The data that would be needed to for this analysis would be the deposition distance and rate data from an air quality dispersion model such as AERMOD. In order to gain this output, the dispersion model would require climate data including hourly weather reports for a number of years, the hourly averages of pollutant emission from all sources of concern including the the velocity and height at which the pollutant is being emitted and a digital elevation model of local area. This would be all be referenced to census tract resolution land parcels. Data available from county level managers on land use be required for the final report.
Analysis procedures:
The gridded data table from the dispersion model would be applied to a grid to create a vector image. This vector image would then be georeferenced and using the Interpolate points tool would be converted into a raster showing deposition across a broad area. A spatial join would be used to join the deposition data to census tract data. This new data set would then be overlayed on land use data to determine potential sources of contamination to susceptible populations such as schools or to determine deposition amounts on farm land or in water bodies.
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