Exercise 7

Goals

This blog post will go through the processes and results of the second part of exercise seven. The first part of exercise seven was covered in the recent python blog post. The goals of the second part were to perform network analysis. The given scenario was as follows: Transportation of frac sand from the mines to a railroad terminal will result in significant impact on local roads. In this exercise, you will route the sand from the mines to the nearest railroad terminal. We will then estimate the number of trips the trucks will take and the cost of the traffic on the local roads. 

Methods

First off, the data that was used in this exercise came from ESRI. They provided a street map in order to do the network analysis. 

Another note before we get into the methods, All of the data and cost results of trips to and from the railroads is hypothetical. The network analysis is simply being done to give us experience using network analysis.

All of the methods done in this exercise were done using model builder. This allows for an organized data flow model so that it is easy to visualize all of the processes being done. The first thing that needed to be done was create a Closest Facility Layer. Once the layer was added, the mines and rails were then put into the layer. The mines were classified as the incidences and the rails were the facilities. Once those were added, then the closest facility  was solved to show where the closest railroads were to each mine. 

Once the routes were copied into a new feature class, a tabulate intersection was preformed to basically join the tables of the routes and the Wisconsin counties. Once the new table was created, a field was added to convert the total route distances per county from meters to miles. There was then another field added which calculated the total cost per county of all of the trucking distance traveled. For the hypothetical situation, we assumed that each truck route is run 50 times to the railroad per year. This is multiplied by two to account for the travel back to the mine. We also then assumed that the cost per mile of a truck driving is 2.2 cents. And with this calculation, we were able to add another field that represented the total cost per year for each county. Figure one below is the model created to preform all of the tasks in exercise 7.

Figure 1

Results

The results of this exercise are represented by a final map. Figure 2 below shows the final map. I have also provided the final table showing the data that was output by the model above. Figure 3 shows the table. The final figure, figure 4, shows a graph giving another visual representation of the counties costs per year. We can look at all of the charts and data and see that Chippewa County has the highest yearly cost for trucking sand. This means that they have the most traffic from heavy trucks on their roads. This heavy traffic can result in the need to repave or redo streets in order to keep them usable. 

Figure 2

Figure 3

Figure 4

Conclusion

I would like to again point out that all of the data and calculations used in this exercise are purely hypothetical. That being said, it is very interesting to be able to apply network analysis to find something out like how much traffic areas are receiving from sand mine trucks. It shows that network analysis has a wide variety of uses. As the results show, Chippewa county had the highest hypothetical cost per year. If we take that knowledge and look at the final map, you can see why that may be. There are plenty of mines in the county but the rail lines that need to be used to transport the sand aren't located in a very convenient location.