Network Analysis

Goals and Objectives
The goal of this assignment was to learn how to perform network analysis and use model builder to automate the process. In order to apply network analysis to the theme of frac sand mining, we assessed the cost taken on by several counties in Wisconsin. This is a direct result of the increased traffic from overland shipping of frac sand between min and rail terminals. The method of network analysis is to help better understand the impact of frac sand mines on the county roads.

Methods
In order to use the network analysis tool, I first had to prepare the feature classes that are going to be utilized. The data that was being prepared was obtained from the Wisconsin DNR. The data I am going to work with involves sand mines that have to ship their sand by truck to the nearest rail terminal. The specific mines that were selected had to be currently active, and within 1.5 kilometers from a rail terminal. In order to select those specific mines the process used here was a python script. My results gave me 44 total mines in Wisconsin.

The next step of the lab is where network analysis is used to obtain the map routes to find the closest facility. Here the street network data set from ESRI street map along with the rail terminals in Wisconsin were used during the network analysis. I then learned how to use the closest facility tool by inputting the correct parameters such as the "incidences" are mines and "facilities" are the rail terminals.

Figure 1. Data flow model using model builder

After that step was completed, model builder was used to create a flow chart to figure out the distance and cost of each county caused by the haul trucks going to and from the mines and terminals (Figure 1). First I used the "Make closest facility layer" tool and the "add location tool. The mines were set as the incidences and the rail terminals were set as the facilities which allows me to set up my network analysis. I then had to use the "solve" tool by routes to determine the closest station to each mine. After that, I used the "select data" and "copy features" tools to create a feature class from the routes that had been solved. Next, the "project" tool was add to change the coordinates system to NAD 1983 HARN Transverse Mercator in order to get the correct measurements for later calculations. The "summary statistics" tool was used to create a table with the route distances broken down by each individual county. Once the routes were separated by counties, I could then create two different fields and calculations. The first field added was the total distance of the routes in each county. This was calculated by taking the SUM of the Shape Length of the route and multiplying it by .000621 (the amount of miles in 1 meter) to get the total distance in miles. The second field I added was to find the amount of cost each county had to accumulate annually due to the higher amounts of haul truck traffic. This step was calculated by multiplying 50 trips a year, 2 trips back and forth, total miles, and .022 for 2.2 cents a mile. The equation looked like this: 50*2*[total_distance]*.022
The numbers used for these equations are all hypothetical.

Results
The results from the model builder have been exported and used as excel data (Figures 2 and 3). In excel I created a chart that is shown below for a better understanding of the model that had been previously run.

Figure 2. This chart shows the amount of miles traveled annually in each county by the sand haul trucks to and from the nearest rail terminal

Figure 3. This chart shows the annual cost each county sustains while having sand haul trucks drive to and from the nearest rail terminal

Figure 4. The final map showing the closest routes from mine to rail terminal along with the hypothetical cost of maintenance of each county

Conclusion

This lab gave me the abilities to learn how to perform network analysis on applications used here in the state of Wisconsin. Even though these results were very unique and accurate it is all hypothetical to the actual world. This can be greatly utilized for future use and application for specific jobs.

Discussion
The data shown above is actually quite different than what I was expecting to see. The counties did not suffer very much from annual use of the roads due to the higher amounts of sand haul trucks from sand mines to rail terminals. The worst county that suffered was Chippewa county at around $600 but that would be minor compared to how many miles are traveled for the use of sand haul trucks.

Sources
Wisconsin DNR
ESRI basemap