During this lab I practiced preforming a Network Analysis, using frac sand transportation as the network. Within this exercise, I will determine routes from a mine to its nearest railroad terminal, as well as calculated the total cost each county will incur based on the amount of sand truck traffic on the local roads. This lab was broken down into two parts. In Part 1, I was required to run Pyscript to create my features for my geodatabase. In Part 2, I used these previous outputs to preform my network analysis, which I documented in model build.
*It is important to note the data values, such as the amount of trips or the costs per mile traveled, analysis within this lab are hypothetic values created by our professors. The dataset used during this is created by Esri (street map USA).
Methods
After run Pyscripter, I had a collection of output. I began by bringing theses into my map document in ArcMap. One of these was a feature class of mines, within Wisconsin, that are more than 1.5 km away from a rail terminal. Another is a feature class of rail terminals within Wisconsin that are capable of loading sand. After these classes were added, I turn on the Network Anlysis toolbar, which allowed me to begin creating a new closest facility layer. This required me to designate my terminals feature class as the facility (destination) and my mines feature class as the incident (source). From here I solved to find the closest facilities to each mine.
Next, I began using Model builder. The first item I added was the Closest Facility layer, and the Esri streets layer became the input. From here I added Add Location tool. This allowed me to specify my mines feature class as incidents. Next, I added an Add Location tool again, however this time I specified my rail terminal feature class as the facilities. From there, I added the Solve tool, which generated the closest facility routes. Next, I added the Select Data tool, which allows me to select the new closest facility routes that I just created. Next, by adding the Copy Features tool, I was able to make these selected features into a new feature class, which I named Routes.
From here I began to calculate the statistics of the model. The first goal was to calculate the total road length for the routes by county. The second goal was to calculate the costs each county should expect to have based on the hypothetic costs that every mile driven on a country road by a sand truck driver costs the county 2.2 cents. To approach these, I began with the first task. I ran the Project tool to project the output into WGS UTM Zone 15N. From there I located the WDNR county boundaries of Eau Claire, and projected this feature class into the same coordinate system. Next, I added the Intersect tool, which overlaps the Route and Countybundaries. From here, I ran the Summary Statistic to get a sum of the Routes shape length per county, as well as how many trips were taken on each route. Now I had a table with a "Sum_Shape_Length" which represented the total road length per county, however it was in meter. To convert this to miles, I added the Add Field tool, and named this field totaldistance_meters. To calculate what would go into this new field, I added a Calculate Field tool, and added the equation [Sum_Shape_Length] * 0.00062137. This number is the conversion factor of meters to miles.
To approach the second goal, I added a Add Field tool, to create a field called cnty_cost. This field represents the total cost incurred by each county. I then added a Calculate Field tool to specify the equation used to calculate the values for this field. The equation I used here was 100 * [totaldistance_meters] * 0.022. I used this equation because each mine is visited 50 time per year, however the trucks make a return trip as well, meaning each route will be driven 100 times per year. Therefore this number would then be times by 2.2 cents to determine each counties total cost.
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| Figure 1: Process ran in Modelbuilder |
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| Figure 2: This map displays location of mines, rail terminals, and sand truck routes from mines to the close rail terminal. Additionally, counties that contain a sand truck route are display with an estimated annual cost incurred at a rate of $0.022 per miles traveled by sand truck. |
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| Figure 3: This graph represents the amount of money each county will incur within a year. These counties all have sand truck routes running through them. |
Discussion
Referring to Figure 3, the top three counties incurring the highest costs are Chippewa County, Dunn County, and Wood County. These top three counties are represented at the darkest green counties within Figure 2. These counties, along with the other countries that experience sand mine traffic should be aware of the potential impacts associated with the increased traffic. It is possible drive time, road maintenance, traffic incidences, and noise pollution may increase within these areas. Prevention for these may need to be discussed amongst the county. Additionally, funding for these yearly costs should be discussed as well.
Conclusion
In this lab I practiced running a transportation Network Analysis. I used this tool to narrow down the Closest Facility route from mines to terminals. This output allowed me to extend to analysis to a cost analysis. To run a cost analysis, I was required to build a model in Modelbuilder. This allowed me document my process. With the outputs of this models, I created a map to display the trends in the date. Furthermore, I displayed the cost analysis in a graph. Both of these from of displaying data are helpful to determine which counties are experiencing highest maintenance costs.
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