Exercise 7 - Network Analysis

Introduction - This exercise was focusing on network analysis. We used the example of sand mine locations and finding the fastest route to the closest rail terminal for transportation. We then wanted to estimate the cost that moving all that sand would have on local counties.

Methods - We first wrote a script which can be explained in detail in the python script blog post from October 2014, as well as below. We used this script to select the mines that were farther than 1.5 kilometer from a railroad because those are the mines that we would be trucking their sand to rail terminals.

Python script to select mines farther than 1.5 km away from railroads.

Next we went into ArcGIS and turned on Network Analysis to find the routes between mines and rail terminals. We took a feature class of rail terminals in the country and selected only the ones that used truck and rail. We then took a feature class of street across the country and used Network Analysis to find a route from each mine to the closest terminal. To do this we set the mines as the accidents and rail terminals as the facilities. Once I have the routes as shown in the map in the results section of this blog, I can make a model to find the distance and cost of each route. The model shown below first makes the routes for each mine and then I project the map into 1983 Wisconsin HARN TM (meters) and add the counties feature class. I then use intersect to find the distance of roads traveled by sand-bearing trucks and using summary statistics I find the distance for each county. Next I add and calculate a field to switch the distance from meters to miles. Assuming that each mine makes 50 round trips a year and that each mile costs the county 2.2 cents I add and calculate a field that gives me the cost for each county (100 trips * 2.2 cents * distance traveled in each county). The model is shown below.

Model giving the cost of sand transportation for each county.

Results - The map below shows the locations of the mines in blue circles and rail terminals in green triangles. The routes are the quickest road that trucks can take to go from the mines to the rail terminals.

Map of mine and rail terminal locations with routes

The table below shows each county and the meters and miles traveled by trucks hauling sand and the estimated cost for the county to fix these roads. The cost was found by multiplying the road length in miles by 100 trips and 2.2 cents per mile.

Table of each county with meters and miles traveled by sand trucks and the estimated cost

The table below show the cost for each county with Chippewa County leading by quite a bit.

Discussion & Conclusions - I think that Chippewa County had the most cost due its location in the driftless region that is known for its great sand. Counties that are greatly affected by these trucks and the damage they cause to their roads should think about implementing a tax for these companies to pay in order to use these roads and offset the cost for the counties. However, the cost doesn't seem to high even for counties that are most affected.

Sources
Street data from ESRI streetmap USA
Online Conversion to help with the equation for converting meters to miles.

(See Python Blog Post for Python potion of this exercise.)