Exercise 7: Network Analysis

Introduction

For this assignment, the goal was to build upon some of the other assignments completed throughout the semester that looked at sand mine locations in western Wisconsin. To do that, a network analysis was used to figure out the distance from each sand mine without on-site rail access to the nearest rail terminal, then estimate a cost that each county would incur due to the excess use of dump trucks on those particular roads.

All figures and calculations in this assignment are hypothetical and should only be used for educational purposes.

Methods

This assignment was completed in two parts, which made completing all required tasks a bit easier to tackle. The first part determined which mines would be used in the network analysis that would be completed in part two. To do this, a python script was used to query and select mines based on a variety of criteria:

-The mine must be active.
-The mine must not have a rail loading station on-site.
-The mine must be further away than 1.5 km from any rail terminal.

The following script (figure 1) was used to first use SQL statements to select the desired mine features, then intersect the output layer with the Wisconsin boundary, and finally remove mines that have a rail terminal within 1.5 km of the site. (See Python Scripts post for more information on how the script works.)

Figure 1: Python script that selects mines based on specified criteria.

Once the desired mines existed in their own shapefile, they were brought into ArcMap and used for the second part of this assignment; the network analysis.

In order to run a network analysis, ArcMap needs to have a network brought into the map document, so the roads network from the ESRI Geospatial database was used. Then the other necessary layers were brought in such as a Wisconsin counties shapefile and the mine locations. A tool was then created which performed a network analysis on the data and returned the total miles of road each county could expect frequent sand transportation on (figure 2).

Figure 2: Tool made in Model Builder.

Lastly, to determine cost that each county could expect to be charged with due to sand transportation was calculated by bringing the data table output from the last step into excel. In figure 3, the formula for calculating the cost incurred by county per year was miles times one hundred trips per year, all times twenty-two cents per mile:

= ((miles*100)*0.022)

Results

Figure 3: Calculating cost incurred by county in Excel.

Figure 4: Final Map.

The counties that could expect to incur the most cost on their roads due to frac sand transportation are Barron, Chippewa, Dunn, Eau Claire, and Wood. These counties are green in figure 4.

Discussion

After looking at the results, it is clear that counties with few rail terminals but many mines nearby are the ones that would expect to have the heaviest and costliest road wear. Counties like Trempealeau, Jackson, and Clark, which have only one or no rail terminals at all, have multiple mines and are the sixth, seventh, and eighth most expensive counties for sand transport. Counties like Burnett, Douglas, Waupaca, and Winnebago are among the cheapest counties for sand transport. All four have mines or rail terminals near their boarders and have routes that are mostly completed in other counties. The patterns displayed in figures 3 and 4 are quite distinguishable and showcase the logistics of a predominant industry in western Wisconsin.

Overall, network analysis can be used in many different applications and is an incredible technology. Whether it's routing your drive cross country or improving efficiency and eliminating costs of a delivery service, network analysis can help users to understand the fastest way to get somewhere, estimate costs or times of routes, planning efficient routes with multiple way points, and the list goes on. So long as the data being used in the network analysis is accurate, the analysis produces quite accurate results as well.