Lab 7: Network Analysis

Introduction:

                This lab was created so that we can be exposed to network analysis. The scenario that we are using coincides with our semester project. One issue with Frac sand mining is that it has a very large impact upon roads. In order for sand to get from the mine to a rail terminal, sometimes it needs to be transported along public roads using a large truck. These trucks can get very heavy and when you have many of them leaving a mine every day, it can start to take a toll on the roads. Imagine the costs to the counties to repair these roads and think about the counties that have multiple mines that all take the same path to the nearest rail terminal. This lab will take an in depth analysis of the potential cost of repairing the roads used by these transport trucks. Keep in mind throughout this analysis that the costs to repair the roads and the number of trips per year are hypothetical.  

Methods:

                To start this network analysis we needed to obtain a road network so that we can use the network analysis toolbar. We got this data from ESRI street map USA. We also got the mines, rail terminals, and county outlines from the Wisconsin DNR. The mines that we used in this analysis are only those who do not have a rail terminals included with the mine or are further than 1.5 kilometers away from an existing rail line since we could infer that a spur would have been built. These mines were found from our python script that we created earlier.

                This lab required that we conducted our work through the use of a model builder. We activated the network analysis extension which allowed us to conduct a closest facility tool. We added the mines as incidents and the terminals as facilities. Since we are using model builder, we have to import every tool instead of just clicking on them from the toolbar, so we added the solve tool so that we can run the analysis. Then we had to select the data and copy the features so that from model builder we could save it to our geodatabase. Now that we have a feature class of our routes we have to calculate how many miles of those routes are in each county. We added a field so that we could calculate how many miles were in each of the routes. From here we added the summary statistics tool so that we could summarize by county how many miles there were and then using the field calculator we put in the expression:

Total Miles per County *50 routes a year (theoretical)* .022 (theoretically takes 2.2 cents per mile to repair the road)  

This gave us our final answer after we converted it into dollars of how much each county would theoretically spend on repairing their roads per year due to Frac sand mining. 

Data Flow Model created in ArcMap showing the different steps in network analysis.

Results and Discussion:

                We can see that these costs add up really quick.  Remember these results were calculated using theoretical values and in my opinion the results would be even higher since for many of these mines we are going to see substantially more than 50 trucks going in and out every year. Chippewa County according to my analysis would have the highest cost at $3657.46 and Winnebago the lowest at $17.01. Imagine that Chippewa County had a substantial more amount of trucks on the roads every year and how those costs would increase exponentially. 

Map created in ArcMap showing the mines used in the analysis across Wisconsin, their rail terminals and the routes taken to them.

Table showing the final statistics for each county affected by sand transportation.

Graph showing the relationship between the counties and the cost estimates per year on those counties.

Graph showing the relationship between the counties and the number of routes per county.

Conclusions:

                This lab was very beneficial to learning network analysis and also to understanding the impact that industry (not just sand mining) can have on an area. I am sure you can do many more in depth analyses into frac sand transportation and even look at the rail network and how sand mining has affected the rails and the relationship of commuter trains.