Network Analysis of Sand Mining in Wisconsin

Goals and Objectives
The purpose of this assignment was to continue work from previous exercises and perform network analysis.  The goal of the assignment was to calculate the routes a truck would take from each sand mine to the closest railroad terminal.  This information was then to be used to calculate a hypothetical equation to infer the costs of road maintenance for each county due to the increased traffic of large, heavy trucks.  

Data Sets and Sources
The mine locations dataset came from data downloaded from the following two websites:
http://www.wisconsinwatch.org/2012/07/22/map-frac-sand-july-2012/
http://www.tremplocounty.com/landrecords/
Many addresses from Wisconwinwatch.org were incomplete and needed to be manually geocoded.  A streets network dataset and counties shapefile was obtained from ESRI.  Additionally, a railroad terminal dataset was provided.    

Methods
There are four main steps to completing this assignment:
1.) Determine the closest rail terminal to each mine
2.) Determine the most efficient route from each mine to rail terminal in terms of time
3.) Calculate the total length of routes in each county
4.) Create an equation to estimate the maintenance cost each county will incur due to increased traffic of heavy sand truck traffic

To begin the project, the mine locations, railroad terminals, and streets network data was loaded into ArcMap and a new closest facility layer was created.  We were then instructed to load the facilities (mines) and incidents (rail terminals) and examine the results.  This did not generate the correct results, as the route from each rail terminal to the nearest mine was calculated.  The mines and rail terminals were then switched to calculate the correct results.  Model builder was then used to create a workflow (Figure 1.) that will calculate the route distance for each county.

The blue ovals represent input datasets, the yellow rectangles are tools, and the green ovals are output datasets.  The first step was to add another closest facility layer based on the streets network dataset.  The add locations tool was then used to load the locations of the mines and rail terminals.  The solve tool was then added and run to calculate the routes from each mine to the nearest rail terminal.  In order to export this route into a feature class in a geodatabase, the select data and copy features tools were added.  The intersect tool would be utilized to calculate the length of the truck routes for each county.  This tool computes a intersection of where the routes and counties files overlap.  Before this analysis could take place, the project tool was added to the model to put the routes and counties files in the same coordinate system.  The summary statistics tool was the final tool used in the model.  This tool computes specified statistics based on a field.  In this case, the sum statistic was used on the shape length field and was based on the county name to calculate the total length of routes for each county.  An equation was then created to calculate the total road maintenance cost for each county based on the length of the routes.  The equation would consist of the length of the route in each county, a number to convert the units from meters to miles, the number of trips each truck would take to and from the railroad terminal, and a hypothetical maintenance cost of 2.2 cents per truck mile.  This equation, done with the field calculator, was: cost=(shape_length field) (.000621371) (100) (.022).  The table was then copied into Excel for formatting.    

Results and Discussion

Map 1 displays the most efficient routes from sand mines to rail terminals in Wisconsin.  A bar graph displaying the data for each county was added to the map to aid in visual analysis.  As you can see, most of the mines are located in western Wisconsin, while most of the rail terminals are located in the southeastern portion of the state.  Because Eau Claire and La Crosse counties have the only two rail terminals in western Wisconsin, they have the highest route lengths and incurred estimated costs.  Other counties with high route lengths and costs are Chippewa, Monroe, and Trempeleau.  This network analysis could be important to these counties because they might want to consider placing a higher tax on sand mines to offset the incurred costs of increased traffic.    

Table 1 contains the total length of routes and estimated maintenance cost for each county.

Conclusion
Overall, I thought this was a very interesting and educational project that applied GIS analysis to a current issue in western Wisconsin.  I was able to calculate the most efficient routes from each sand mine to rail terminal, the total length of routes for each county, and create a equation to come up with a hypothetical cost incurred from the heavy stand trucks.  Model builder was a bit difficult to work with at the beginning, but ended up being a very efficient method of performing the calculations.  This streamlined the process because each tool did not have to be independently run.  Finally, I enjoyed obtaining a better understanding for network analysis and the functions and uses for it.  I was able to apply some of the skills to my research project on the locations of Minor League Baseball stadiums by creating a new service area to find the extent of the area where people can drive to the stadiums within 30 minutes.