Post 5: Network Analysis of Sand Mining Trucks

Introduction
The goal of this project was to perform network analysis to calculate the impact of trucking sand from mines to rail terminals on local roads in Wisconsin. The weight of the trucks and the amount of traffic would cause damage to those roads and this network analysis approximated yearly costs for repairing those roads for each county. The number of trips and the costs associated with this project were purely hypothetical and should not be used for actual projects. This White Paper case study served as the background for this project. The network dataset was provided by ESRI street map USA and the rail terminals and sand mine locations were provided by the Wisconsin DNR.

Methods
The network analysis was completed in ESRI's ArcMap using model builder (Figure 1). Before using model builder the address field in the sand mines feature class was deleted to avoid errors within model builder. The first tool used in model builder was 'Make Closest Facility Layer' which used the streets network reference layer as the input. The 'Add Locations' tool was used to set the sand mines as incidents and the rail terminals as facilities. Next the 'Solve' tool was used to create the routes from the facilities to the incidents. In order to export the route to a feature class a 'Select' tool and a 'Copy Features' to export it to the geodatabase created for this project. The routes feature class was then projected to an appropriate coordinate system and then intersected with the Wisconsin counties feature class so that the routes can be separated into the different counties. The 'Summary Statistics' tool was used to create a table (Table 1) for the feature class which summed up the distance of each route in each county. The final tools used in model builder were 'Add Field' and the 'Calculate Field'. A field was created and calculated so that annual cost of impact on local roads from sand mining trucks was added to each county in the table. The field was named Sum_road_cost and the calculation used was SUM_Shape_Length x 0.000621371 x 100 x $0.022. The calculation kept in account the conversion from meters to miles. It was multiplied by 100 due to the trucks taking about 50 trips a year and accounting for the trip back. Once the model was run the table was created and joined with a Wisconsin counties layer so that the cost per county could be mapped.

Figure 1. Model builder was used to create the information used in this project.

Results
The resulting table displayed the name of each county with roads affected by sand mining trucks, the frequency, the total length of roads in meters and the total yearly cost in US dollars.

Table 1. The cost per year was relatively low because the cost per mile was hypothetical and relatively low.

The resulting map displayed the cost per county using graduated colors. The map also displayed the railroad terminals, the sand mines, the truck routes used to transport the sand and major roads in the region. 

Figure 2. The counties with that would need to pay the most are Chippewa, Barron and Eau Claire.

The costs were only calculated for Wisconsin counties. Most of these counties lay in western Wisconsin, because that is where most of the sand mines are. If every sand mine in Wisconsin was included in this map there would be much more, but this map displays only the mines without its own rail terminal. 

Conclusion

Sand mining in Wisconsin has a huge impact on the western Wisconsin economy. It is important when approving new sand mines the cost vs. benefit of that sand mine. GIS can be a useful tool to analyze different costs of having sand mines. This project can help make decisions to approve only new sand mines that lie on rail road terminals to avoid these costs for these counties.