The main goal of this lab was to become familiar with using network analysis. This tool was used in combination with a semester long process on the effects of frac sand mining in western Wisconsin. This segment of the project was used to analyze the effects that trucking the sand has on county roads. Since the county pays for the upkeep of the roads, it is important to know exactly how much it costs to keep up the roads due to the heavy use by sand mining trucks.
Goals and Objectives:
In order to get a list of mines used for analysis, a python script had to be written. This script was used to query out the mines based on a defined criteria. After the desired mines had been selected, they were then used within the network analysis window. Using the network analyst toolbar, a route was generated showing where the frac sand trucks transported the sand. Hypothetical parameters were then set up in order to create an equation. This equation was then used to evaluate the cost of damage the sand transport trucks are causing on local roads.
Methods:
The data that was used was the obtained from previous course assignments. This included the rail terminals, frac sand mine locations and a new street data set from ESRI. Network analysis was used in order to locate the routes that the frac sand hauling trucks would take on the different county roads. Once the different locations were added into the model, the solve tool was then used to create the different routes. Once the routes were created, they needed to be selected and were then copied into the desired geodatabase using the copy features tool. The results were not projected into the correct coordinate system, so the project tool was necessary in order to display the results correctly on the map. The main goal was to see how much it damage the frac sand trucks were causing on the county roads. Three new fields were then created in order to complete this task. The results were based off of hypothetical numbers provided within the lab instructions.
There were three different equations used to populate the newly created fields.
1.) [Shape_length] /1600
2.) [Miles] *100
3.) ( [trip_miles]*2.2)/100
The first equation converted the distance of each route from meters to miles. The second gave the total distance the trucks traveled based on 100 trips taken. The final equation shows the final cost of all the trips on the county roads.
There were multiple different tools used in this particular model. The different tools included, Make Closest Facility Layer, Add Locations, Solve, Select Data, Project, Copy Features, Add Field, Calculate Field, Join Field and Summary Statistics. All of these tools are displayed below in the Model Builder Workflow (Figure 1.1).
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| Figure 1.1 The final data flow model was used to generate the results for the cost of damage cause to the county roads. |
Results and Discussion:
This exercise was designed to use network analysis to show the shortest routes for the frac sand transport trucks to use. The final results show the roads the trucks would use. Some counties have a higher concentration of routes, which will have a larger impact the the roads. Figure 2.1 shows the final results of where the routes are located.
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| Figure 2.1 This map illustrates the final results of where the routes are located across the state of Wisconsin. With one route being in Minnesota. |
The table (Figure 3.1) and graph (Figure 4.1) below, show the total cost of damage cause on the roads. There is a great variation between the number of routes in each county and the cost each of the counties has to pay.
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| Figure 3.1 Shows the cost of damage the frac sand trucks cause on county roads. |
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| Figure 4.1 This is a table illustrating the total number of routes through each county and the cost associated with the routes. |
Conclusion:
Before this assignment I had never used network analysis and only used model builder on short exercises. Both of these tools proved to be very useful for different reasons. Model builder allows to keep your data very organized and see where you may have made a particular mistake. Although the results from this that are purely hypothetical, it still showed how useful network analysis can be in a real world application.
Sources:
Wisconsin DNR
ESRI Database
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