Frac Sand Suitability Part II

The goal of this Project was to study the transportation routes from frac sand from the mines to a railroad terminal.  These transportation routes could result in significant impacts on local roads.  A network analysis was performed to determine the distance travelled on local roads and the cost of the impacts for each county in Wisconsin.

Data
The addresses of frac sand facilities were used in the network analysis.  I, along with three other class members, located these addresses through geocoding as well as aerial imagery.  Each class member created a feature class of their located mines;these feature classes were then merged into one feature class in ArcGIS.  X and Y coordinates could not be found for two mines.  These mines had to be removed in order to correctly perform the network analysis.  A railroad terminal dataset was provided by our professor, Christina Hupy.  This dataset represents locations within United States for transportation terminals such as bus stations, train stations, marine terminals, and other significant transportation nodes.  Street and county data was supplied by ESRI.  Each of these datasets were crucial to the success of the network analysis.

For this analysis, it was assumed that trucks travelled between each frac sand location and rail terminal 50 times (one-way) per year (100 trips total).  It was also assumed that the cost per truck mile was 2.2 cents.

Methods
A network analysis can be performed using dialog boxes in ArcMap, but this process can be tedious and time consuming.  To perform this network analysis, a model was built in ModelBuilder, an ArcGIS application.  ModelBuilder allows the user to input feature classes and tools into a model.  When all aspects of the model have been added and defined, the model can be performed.  The use of a model presents many benefits.  The process is performed in a single step, yet is editable in order to fix any issues or to change the output of the model.

In order to produce the desired results of this project, the "Closest Facility" network analysis tool was used.  Closest Facility network analysis created a route between a frac sand location and the nearest terminal.  It was used because it most closely resembles the interaction between these features in the real-world.  The model below represents the steps taken to perform the network analysis (Figure 1).

Figure 1- Workflow created in ModelBuilder to perform network analysis

Before the distance travelled per county could be calculated, the SUM Shape Length values had to be converted from meters to miles.  This was done through the field calculator tool using the equation "SUM Shape Length / 5,280."  Once the feature lengths were in miles, the field calculator was used again to calculate the cost per county using the equation "Miles * 100 * .022."

Results
The model resulted not only in a network analysis of the frac sand transportation routes, but also the cost and travel distance per county.  These results are displayed below in Figure 2.

Figure 2-Final results of Network Analysis

Conclusion
The counties that exhibited the most distance travelled were Chippewa, Eau Claire, La Crosse and Trempealeau.  These counties exhibited the highest cost as well.  It can be concluded through these findings that as the travel distances increase between the frac sand facilities and rail terminals, the cost also increases.  Although other factors can contribute to deteriorated road conditions, it can be implied through the results of this analysis that there is a significant relationship between the frac sand transportation, road deterioration and cost per county.

Discussion
The calculations of the network analysis outputs were equally as important as the network analysis. The results of the calculations and the network analysis can be used separately to provide information on the impact of frac sand mining in Wisconsin, but the combination of the results exponentially increase the value of the research.

The real-world replication of network analysis make this tool invaluable to geospatial technology.  It is a tool that not only replicated real-world networks, but also allows the users to define parameters to expand the capabilities of these results.  Many types of network analysis are available and can be used to produce different results.  It is important to know the ramifications of the type of network analysis used; if not used correctly, results may be flawed or inaccurate.