Network Analysis: Capabilities of Processing and Transportation

Background and Objectives:

                As a continuation of our semester long Fracture sand mining project, student are asked to evaluate transportation routes using the Network analyst tool in Esri’s ArcMap. As the project moves forward, we prepare data on the significance of impact on the roads from trucking sand to rail terminals. This is a necessary step towards our end goal, because it provides significance and a quantifiable value of the impact on the local infrastructure. The trucking involved with sand mining produces a large effect on local roads because after the process of extracting the sand from the ground it must be transported to a processing facility or fracture mining location for use. For this portion of our project we are looking specifically at the impact on the roads network. To begin to prepare our data for use in the Network Analysis, we must consider the following things:

Which mines will be using trucks only to transport their sand to a rail terminal?

What is the most efficient route to the rail terminal?

How many total miles does each truck drive in individual counties?

What will the cost of the impact on the roads network be for each county?

Procedures: 

Each mine possesses different capabilities of processing and transportation. For example some mines have processing facilities and rail terminals on site, while others, can only transport via truck on roads. In our project, we only concern ourselves with the impact to roads and highways in Wisconsin. To begin, we used python script to help us complete the network analysis. In python, we wrote a script that created shapefiles with pertinent information. This could have been done regularly in ArcMap sessions, but we used this opportunity to further our script writing skills. As displayed in figure 1, the resulting script supplied us with mine locations that are active, are further then 1.5 kilometers from a rail terminal, and don’t have a privately owned rail terminal on location.

   Figure 1: Python script for running data preparation steps (query) create .shp files for project

                                            

Next I displayed the active mines with no rail terminal in ArcMap. Within the ArcMap software is a native program called Model builder. For simplification of workflow and correct data management we used model builder to use the tools necessary for a completed exercise. After looking at the data in ArcMap, we used Network Analysist, which contains a tool called “Closest Facility”. Closest facility creates a route from incidents (active mines) to the terminal (rail facility) using a street network accessed in ArcMaps. 

Figure 2: Model built for the workflow for the network analysis of fracture sand mining in Wisconsin

This tool allows us to make a line segment out of the route, which in turn supplies us with a number value for the length of road traveled to get the sand to the rail facility. Continuing to build our model, new fields were added on the data table to produce total distance traveled per county, and a cost per county from the road impact. In our hypothetical project, we made up a number for the cost of each mile traveled by road would amount to 2.2 cents. This number is most likely not accurate, but it serves a purpose to help students become familiar with doing cost analysis on transportation routes.

Results: 

Figure 3: State of Wisconsin with counties showing cost in dollars of fracture sand trucking in a gradient of blue. Routes to the closest facilities are displayed in light blue lines from mines (Yellow) to facilities (green).

After the model ran, a final table was produced that contained the total amount of miles driven in each county, and how much hypothetically this would cost. Looking at the distances and cost, it varies quite drastically. This is to be expected as most of the sand available for mining is located in the central portion of west Wisconsin. An important ramification of this project was projects like this have real world consequences. Just recently a group of GIS professionals produced a project with great similarity to ours for the county of Chippewa Falls. The group ran network analysis and risk factors, in much greater detail. As a student this is of great importance for me to gauge how educational projects can be designed with real world implications and geography is a very powerful tool to use.

                Another aspect of this project to consider in discussion is the routes that are taken, and the trueness of the cost calculations. Looking at figure 3, the trucks take their own distinct route, for the most part. For many of the routes, portions of the road driven are not distinctly only one truck route. As trucks come towards a main rail facility they overlap routes. Because we are calculating the cost off distance, this overlap could have a great effect on the overall value of the output. To check to see if the calculations too into effect individual route completely, or it allowed overlap I needed to look at the routes feature class directly. Highlighting routes one by one and looking at the line created I could see if the routes were composed of a true distance, and they were. A final data table shown in figure 4, shows all of the counties that have a cost associated with fracture sand trucking. 

Figure 4: Final table created showing cost and distance traveled in each county by trucks.

The implications of this final table are of vital importance, and could possibly play a role in later decisions for local governments. Figure 4 shows counties like Eau Claire, Chippewa and Barron county incur a great deal of use of roads from sand mining operations. As these number are hypothetical, they hold no inherent value to any governmental body, but the idea behind the information is whats important. Running analysis like this on transportation networks have help save vast amounts of time and money. GIS proves valuable tool or making decisions, and if implemented properly can gain many meaningful insights to operations that may not have normally been obvious. 

Conclusions: A formal report of findings is required to discuss findings. Throughout the process of this project, students are learning "best practices" for GIS programs. Structuring decisions based off data integrity and aiming for meaningful answers push students to be thoughtful in how they compose their analysis. Using programs like Model Builder and Python allow students to simplify workflow and naming structure in a way that facilitates more composed answers. Outlining the perimeters of the project allow for transparency with data analysis. This particular exercise utilized Network Analysis tools to route trucks to a closest facility for rail transportation. This tool allowed us to calculate a rough hypothesis of the cost individual counties experience from trucking fracture sand. The implications of this information could impact decisions on local infrastructure spending, taxes, and traffic flow. As a training GIS analyist, projects like this, which have real-world counter parts are a great practice for possible career projects later in life.