The goal of this lab was to use network analysis in ArcMap along with model builder to continue our study of the impacts of frac sand mining in Western Wisconsin. Mines have a wide variety of ways in which they transport their sand including air, rail, truck and usually a combination of two or all these methods. Almost all the sand mined in the Wisconsin is transported out of the state by rail and many companies have begun creating their own railways to speed up the transportation process. Those who do not have a rail system though require the use of public roads to move their sand to the nearest rail depot. Using the Python Script #2, we were able to separate the mines who use trucks to transport their sand to rail facilities to gain a better understanding of the impact that the transportation of this sand has on the public roadways.
Through the use of ArcMap’s model builder and network analysis tool we were able to determine the most likely paths that trucks would be traveling in order to go from the mining site to the closest rail facility. Since these trucks are traveling to and from the mine site to these rail stations they are have an impact on the public road ways, but to what extent we do not know. To understand this in monetary terms we will use a hypothetical value of 2.2 cents per mile to calculate the cost of added maintenance on the roads where sand transportation is prevalent by county. In addition to this we will be making the assumption that each mine takes about 50 trips to the rail facility which is also a hypothetical, arbitrary number. Through the use of model builder and the network analysis tool we will gain a better understanding of how much damage these trucks are having on public roads.
Methods
We used the mine data we produced using Python Script 2. This sorted the original data we received by the Wisconsin DNR and selected the mines which are most likely to have the greatest impact on public roadways.
To begin the network analysis I used the mine locations we selected in the previous lab and the streets network dataset from ESRI streetmap provided to us. Next, I used model builder to calculate the cost per county required to repair and maintain the public roadways used by mines to transport sand from the mine to rail facilities (Fig. 1). I started by adding the "Make Closest Facility Layer" tool, making the mines as the incidents and the rail stations as the facilities. I also added the "Add Location" tool to my model in order to set up the network analysis. In order to run the tool and determine the shortest distance between the mines and the rail facilities, I had to add the "Solve" tool. I did need to take this data and make it into a feature class of the best routes as calculated by the network analysis, to do this I used the "Select Data" and "Copy Features" tools. Since the calculated route was in a geographic coordinate system it needed to be projected, using the "Project" tool, into a projected coordinate system. In this case I used the NAD 1983 HARN Transverse Mercator (feet) in order to allow for the calculations to be accurate.
To calculate the total amount of distance traveled by trucks I used the "Tabulate Intersection" tool in order to take the Wisconsin county boundaies and computed the overall distance of the routes within each of them. Next I needed to create a new field and within the table to show this distance per county. Then I created another new field where I calculated the cost of the damage to roads by multiplying the distance field by 2.2 cents per mile. The equation I used was: (distance in miles) * 50 * 0.022. The 50 represents the idea that each mile is traveled by the trucks about 50 times a day.
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| (Fig. 1) This is the model used to implement the network analysis tools for this exercise. |
The route data could then be shown on a map of Wisconsin (Fig. 2). In order to best understand the results of the model we ran previously we brought the data into Microsoft Excel using the table to excel tool in ArcMap. Once this was done I was able to graph the dollar amounts per county (Fig. 3) and apply that information to a map of Wisconsin counties (Fig. 4).
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| (Fig. 2) This maps shows the most efficient and likely routes taken by mine company trucks from the mining site to the nearest rail facility. |
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| (Fig. 3) The cost of road damage caused by mines trucking sand on public roads based on an arbitrary value of 2.2 cents per mile is shown in the graph above by county. |
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| (Fig. 4) This map shows the possible cost in damage to roads by mine company transportation by county. |
I was surprised at the range of cost per county but generally found that the values were a bit lower than I expected. There is certainly room for error since we are making the assumption that the number of trips being 50 per day. A simple change in this value could drastically shift the calculated costs. The routes could also be effected if we tried to incorporate rail facilities outside of Wisconsin as well.
It would be very interesting to know what the actual cost of average maintenance on public roadways to better understand how these mines effect roads. For instance, most of the cost is in a few counties and a great deal of this is caused from the longer distances which need to be traveled by trucks since there are fewer rail facilities.
A major issue with this model though is that the network analysis tool simply calculates the most time efficient route from each mine to a rail facilities but that does not mean that the trucks actually travel this path. Therefore there are some problems with this model but it is the best way to predict the routes and therefore understand the most likely impact that they will have on the roads.
Conclusion
The network analysis tool is very versitile and is fairly simple to use in ArcMap. Not only does it utilize street networks to create the most time efficient route but it can be used in a number of other applications. For instance, companies that deliver products can use this technology to determine the best route based on a number of different stops in order to find the most efficient route. This not only helps to minimize transportation costs but also time spent traveling from one stop to another. In this particular case study we are looking at the impact of semi-truck traffic has on the roadways based on the most likely routes they travel.
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