The goal of this assignment was to learn how to perform network analysis, and use model builder to automate the process. In order to apply network analysis to the over-arching theme of frac sand mining, we assessed the cost incurred by several counties in Wisconsin as a result of increased truck traffic from overland shipping of frac sand between mine and rail terminals.
Although the calculations were performed using hypothetical numbers, the workflow is still the same as if the calculations were performed with proper values.
Methods:
In order to estimate the damage incurred per county, it was first necessary to determine the routes between the mines and rail terminals. This was done using the closest facility analysis, a technique that uses a street network dataset to determine which facility is closest to a specific incident, and is also used for everything from emergency service dispatching to pizza delivery. to input the streets network dataset, and the Add Locations tool to load the locations of the mines and rail terminals. The mines were added as "incidents", and the rail terminals were added as "facilities". Next, the solve tool was run to calculate the route. The "Select Data" and "Copy Data" tools were used to add the calculated routes to a geodatabase. Before any further calculation could be performed, it was necessary to project the routes into a projected coordinate system, so their lengths wouldn't be calculated in decimal degrees.
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| Figure 1: The completed data flow model. |
In order to calculate the total road length per county, I used the "Intersect" tool to separate the routes into individual sections per county. The Summary Statistics tool was used to obtain a table featuring the total length of the routes per county. These lengths were output in meters, however, so some calculations were needed before cost-per-county could be determined. First, the Add Field tool was used to add a float column named "Distance_Miles". Next, I used the Calculate Field tool to write an equation converting the distances from meters to miles and adding the calculated values to "Distance_Miles". Once the distances were calculated, I added another float field "Annual_Cost" to hold the estimated cost value. I used the Calculate Field tool to write an equation so "Annual_Cost" = "Distance_Miles" * 50 (hypothetical number of trips per year) * 2 (to and from the terminal) * $0.022 (using a hypothetical cost per truck mile of 2.2 cents).
Results:
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| Figure 2: Haul distance per County. |
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| Figure 3: Hypothetical Annual Cost per County |
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| Figure 4: Hypothetical Annual Cost per County. |
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| Figure 5: Frac sand mine locations, rail terminal locations, and the routes between the two. |
Discussion:
It was extremely important to use a float field for the calculations, as the default long integer cannot hold decimal values. Other students were having computational difficulties as a result of choosing the wrong field type.
The data showed very different results from what I was expecting, as counties' roads weren't only suffering damage from traffic from mines within the county, but also from mines in surrounding counties. The extra traffic on Chippewa County's roads from mines in Barron County potentially double the cost incurred by Chippewa County (Figure 5).
Conclusions:
This lab provided an opportunity for me to learn how to perform network analysis. Although the results are completely hypothetical, the workflow is accurate.
Sources:
Mine location information, County Boundaries, and was acquired from the Wisconsin Department of Natural Resources.
http://midamericafreight.org/wp-content/uploads/FracSandWhitePaperDRAFT.pdf
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