Sand Mining Suitability: Network Analysis

Goals and Objectives:

The purpose of this exercise was to calculate the best possible route of each sand mine to railroad terminals.  From there it was possible to calculate the estimated cost of shipping the sand from the mines to the railroad terminals based on a hypothetical cost per mile of 2.2 cents.  All of these steps were tasked to be performed in ArcGIS model builder.

List of objectives:

1. Load features and network analysis in ArcMap
2. Calculate routes based on closest facility network analysis
3. Calculate total amount of miles driven per county
4. Calculate cost per total county based on miles driven

Methods:

The first step in this lab was to gather up all of the features required, including the 2013 street map network data set from ESRI.  The features that were used were non-railroad sand mines that were more than 1.5 km away from any railroad and railroad terminals.  A simple closest facility analysis was run with mines as the incidents and railroad terminals as the facilities.  Solving this closest facility analysis came up with the best route from each mine to a railroad terminal according to the network data set.  Then, in order to calculate the amount of miles of driving per county, tabulate intersection was run.  This tool took the county boundaries of Wisconsin and computed the total length of the routes within them.  From there cost was calculated using the simple measure that each mile was driven 50 times a year by each truck and each mile cost the county 2.2 cents.  The equation used was (distance in miles)*50*.022.  All of these processes were run in model builder (Figure 1).

All of the processes run in this exercise were required to be run using model builder.  (Figure 1)

Results:

After the routes were created using the make closest facility layer tool, a map was generated to show the various best routes assigned by the tool (Figure 2).  From there more quantitative analysis could be run regarding the amount of miles driven in each county and the cost (Figures 3-5).

This map shows the various routes generated by the closest facility network analysis tool.  Most of the results weren't very surprising, though it was interesting that one of the routes actually exited Wisconsin and traveled through Minnesota.   (Figure 2) 

This graph was created to further illustrate the hypothetical cost
that could be incurred each year on each county per the
calculations performed and previously mentioned parameters
(Figure 4)

This table shows the various, total lengths driven
in miles to get the sand from mines to railroad
terminals and the total cost to the county per year.
(Figure 3)

The total, hypothetical costs incurred per county due to trucks traveling to rail terminals was mapped.  It can clearly be seen that not all counties are affected as frac sand mining is mainly prevalent in Western Wisconsin.  (Figure 5)

These results have several interesting aspects to them.  Many sand mine trucks appear to be taking the same routes to get the to rail terminals (Figure 2), this could put a very heavy load on these roads in particular and they may need special attention.  However, the amount of hypothetical yearly cost is relatively lower than expected (Figures 3-5).  This could be due to inaccurate assumptions of 50 trips a year and 2.2 cents per mile.  More research and analysis will need to be run in order fine tune the knowledge regarding potential costs of trucking the sand to rail terminals.


Conclusion:

Network analysis is a powerful tool that can be relatively easy to run; particularly with many network data sets already established.  Using network analysis and the closest facility tool was able to calculate the best routes from sand mines to rail terminals for sand transport trucks to take.  From here total mileage and cost per county were calculated using several joins and different calculations.  Some of the results were surprising such as the low yearly cost, however these are just hypothetical calculations and may not have much weight in the real world.  This information should be compared with colleagues in the class to determine whether similar results were achieved.