Network Analysis

Goals and Objectives: The goal of this exercise is to get a better grip on how network analysis is used in a real world scenario. The scenario we had to find the shortest transportation routes for sand mine trucks to the nearest railroad terminal. The constant back and forth driving of these trucks damages the road and it cost the residents of that county's money to fix those roads. Using the network analysis tools we can find the shortest distance and how much the damages cost. be sure to note the number of trips and cost is hypothetical.

Methods: The first operation was to create a python script that would select sand mines that were active, were not connected to a railroad terminal, and were 1.5 km away from a railroad. Once this was completed it was saved to the created geodatabase for this exercise. Next we added the street network dataset to our ArcMap and made sure to turn on the Network Analyst Extension. The results from the script and all the rail terminals were then added to the map. Next using the network analysis toolbar we created routes that were connected to the closest facilities. The closest facility tool helped determine the closest rail terminal to each sand mine. The next step was to use Model Builder to create a workflow that would help generate the results we would need to find the shortest route and cost. First we needed to add the streets to the closest facility tool. The result then had locations added using the mines_norail_final and trucks_rails_terms2. Once solved the routes are then selected and the copy features tool is used to save the selected features to the geodatabase in my work folder. I projected the routes along with the Wisconsin County Boundaries using the Wisconsin NAD_1983_HARN. After intersecting the two projected results I did a summary statistics to find the distance of route within the counties. I then converted the meters into miles using the equation [SUM_Shape_Length] *.00062137. To find the cost I took the distance result from the newly created field and used the equation [Distance] *2.2 *100. The result ends up showing the distance in meters, miles, and the cost per county.



Figure 1: The model builder used to calculate distance and cost for the counties

Results: The results shown below are the python script that was used to generate the mines. The table is what was created using the Model Builder. The map below shows the shortest distances to the rail terminals and the cost to each county. The results show that Chippewa, Dunn, and Wood County are the most effected by the cost. It is interesting to see Eau Claire has no sand mines but with routes going through it there is a nice price to pay for the trucks transporting the sand.

Figure 2: The python script used to select certain mines.

 

 

Figure 3: The resulting table from model builder showing the distance and cost.

 

 

Figure 4: The map showing the shortest routes and cost per county.





Conclusion: Working with Network Analysis was new and exciting. I was able to take the mine locations and find routes that would get the trucks to the closest rail terminal. This would help keep the trucks off other roads and make it cost less for the counties to repair. It is still unfair for the counties that would have to pay for the trucks damage to the road even though the county has no sand mines in them (i.e. Eau Claire, La Crosse). As I said before these results are hypothetical so we can not be sure that they are accurate to a T. 

Sources: WI DNR, ESRI Street Map USA