Sand Mine Trucking Network Analysis

Goals and Objectives

This assignment's goal is to utilize python to create feature classes which can then be used in later processing. Once this section is completed, Network Analysis are used within ESRI's ArcMap to perform cost analysis of trucks driving sand mine output across the roads of Wisconsin. The closest facility function is used within Network Analysis to describe the distances trucks must drive to reach rail terminals for further shipping. All of the input values are arbitrary and only meant for obtaining a working knowledge of Network Analysis. This sort of analysis is extremely important, and organizations such as the National Center for Freight and & Infrastructure Research & Education out of the UW-Madison have done extensive research on this topic within western Wisconsin.

Methods

This first part of this assignment was to designed to create a python script which creates output which can be used later in Network Analysis. Figure 1 below describes the script. The script created feature classes which compose mines which are active and are not near a rail transportation system. This script:

  • Imported system modules
  • Set local variables
  • Added field delimiters
  • Ran SQL queries to create new feature classes from selections
  • Created a feature class based off of locations to rail systems


Figure 1: Python script used to create feature classes which would be used in the next section.




The second section of the activity is designed around utilizing network analysis for a variety of functions. It uses ESRI's street network to find the closest rail terminal to each sand mine. These routes are then exported as a new feature class. Once this is done, the total travel distance is added up per county by miles. This is done using a spatial join between the exported routes and a Wisconsin county shapefile. The joined shapefile is also exported and then a new field is added to run a field calculation. This is completed to get an idea of the cost effect sand mining truck transportation has on a county's roads. The monetary estimate per mile is 2.2 cents per year. This is multiplied by 50 trips per year, and each route is counted twice to account for return trips by the transportation trucks. All of the steps are outlined in the model below (Figure 2).


Figure 2: Model created to run all of the network analysis functions of this assignment.


Results and Discussion


The table below describes all of the calculated distances each road is driven on to reach a rail terminal, one way. It also displays the cost of the driving in dollars in the last column. One can see that the cost varies greatly throughout the different counties which are affected. Places like St. Croix, Winnebago, and Burnett are all crossed as the routes go across Wisconsin, so the cost of driving on these counties is very negligible. Chippewa County, however is effected the largest, with a total cost of $721.00 per year. This can lead to significant losses by the county in road quality. Things like this display that the sustainability of mining operations can have far reaching effects which are not always accounted for.


Table 1: This table displays the distance and cost values which affect each county that has sand mine transportation across it.


The map below displays the results of sand mining cost on Wisconsin roads. As one can see, the western portion of Wisconsin is where a majority of the sand is mined for fracking. This is due to the last glaciation period depositing large amounts of fine, well rounded sand which is perfect for pulverizing the earth in fracking operations. This distribution also has affected the rail terminals for Wisconsin. There are only a handful of rail stations near these mines, so each mine which does not have a rail terminal on site must transport their sand to these off-site terminals to have it shipped off. This can cause great stress on the roads, and the estimated numbers above prove that the wear and tear can have serious variations in cost depending on the levels of usage.




Figure 3: This map displays the mines, rail terminals, and the cost each county is estimated to pay each year just from sand mine transportation has on their roads.



Conclusion


Network analysis is an incredibly powerful application which has broad reaching effects across GIS usage. Companies like Fedex and UPS utilize ESRI's tool to create the fastest routes possible, and this assignment shows that extremely diverse applications of the function are available to the everyday user. Using python to create a script, and then running the output through through a model is just another example of how many tools are there for GIS applications. One can see through this assignment that the transportation of sand mine across the state has a significant effect on the roads of individual counties.


Data

Wisconsin Sand Mine: Wisconsin DNR
Wisconsin Counties: US Census Bureau


Rail Transportation Locations: US DOT

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