Frac Sand Mining Project

Previously, I have geocoded all the mines and now its time to take the next steps; setting and running network analysis.     
After going through the set up of the project, now is the time we actually run our data and analysis it to see what kind of costs counties can incur from sand mine trucks running loads on the roads itself.

To run our project properly, gathering a few shapefiles was necessary. From ESRI folder, the Wisconsin counties, roads, and railroad terminals shapefiles, were collected for use. Next I imported the merged mines shapefile that was created in the previous steps.

Figure 1

 

     The first step in the process was to create a closet facility analysis. This is found in the network analysis toolbox. It will ask you what your incidents and facilities are. For the incidents I imported the merged mine shapefile, and for the facilities I imported the railroad terminal. One option that needed to be checked was to make sure that the process was taking the mines to the railroad terminals and not from the railroad terminals to the mines. This network analysis created the routes the trucks take from the mines to the facilities based on time or distance, in this case it is based on time. Next, taking this route, using the identity tool was needed to give the routes/roads a spatial identity it needed so the proper analysis can be given to the roads in each county. What the identity tool doing is it is taking each route and breaking it down into what county it is located in. If the route crossed counties, it separates the route at the county boundary and calculates the mileage for each individual county. Using figure 1, we can see the data model that is ran. You can see each individual inputs and outputs for each of the steps that is needed to create this model. The final step in the model is the identity step. Using this identity output, I then was able to get the total county mileage by using a summarize function. Now the calculation can be preformed to see the cost of the the sand mines trucks have on county roads. Using the estimated cost of 2.2 cents a mile and 50 trips, which is there and back so 100 was the multiplier, taking the total mileage per county at 2.2 cents a mile at 100 for the trips, the estimated cost was calculated.

Figure 2

 

     Figure 2 has the map and graphs of the calculated data for the project. Looking at figure 2, La Crosse, Trempealeau, Eau Claire, and Chippewa Counties were in the top 4 by a long shot. La Crosse's estimated cost is a little over $2,250 per 50 trips.

     The overall impact of the sand mines is unknown at this point, for every action there is a reaction. This project is looking at one of the potential impacts that sand mine companies can have on the public sector. Living next to a sand mine, there is more than 50 trips in a day. So using this equation  there would need to be another multiplier for the number of days in operation to get the true cost. The county government knows the cost of the replacement of the road or improvement of the road so it can hold the loaded sand mine trucks accountable. This estimate can help factor in the true cost of the damage to the roads that is from the sand mine companies. 

     On a personal note, as a supporter of the sand mine and a taxpayer, I believe the sand mine companies should be held responsible for the deterioration  of the roads that are not rated to hold a fully loaded sand mine truck. I do not think it is fair to the taxpayer that they have to pay for the road that is damaged because a vehicle that is overweight for the road is using it. 

*Due to blogger not being able to accept PDF's, I am unable to put the figures on the website until I am back in Eau Claire and able to convert the PDF to a JPEG.

** It is now updated with the appropriate images