GIS II Project ~ Phase 3 Network Analysis

Introduction:

     The function of this exercise is to develop some basic network analysis skills by working with the mine data developed in Phase 2.  During this project, I will load the geocoded locations of the silica mines in Wisconsin along with given state road data and locations of state railroad terminals.  I will run an analysis along the road network to find the most efficient route from the sand mines to rail terminals: shorter routes are desirable because a shorter route means lower time and gas requirements of getting the sand to market which adds up very quickly over the life of a silica mine.  After completion of this task, I will parse out these optimal routes by county and estimate the cost to each county for infrastructure maintenance related to the transportation of these operations.


Methods:

     As described in the intro, the first step in this process will be to import the mines, roads, railroad terminals, and counties into a GIS.  Fortunately, all of the data has been compiled (in the previous exercise) and is projected appropriately, so it  will merely be relocated to a central geodatabase for use in this project.  Once all of the base data is in the desired place, the network analysis can begin.


fig 1: the results of the route tool, interesting but not helpful to us today.

     Using Arc, the network analyst toolbar first needs to be initiated before beginning, and I recommend the toolbar be used to easily navigate the tools as opposed to scouring the toolbox for each step of processing.  The first tool that will be run is "new route" analysis to find one potential path that would include all of the mine locations as "stops."  This was mostly done to explore what the tool was useful for, but the result will not be of any use to us as our goal is to find paths from each mine to the nearest rail terminal, not a single route which connects each mine. That route would not be useful for very much.


     In order to determine the optimal routes from mines to rail terminals, the Closest Facility tool will need to be used, this process is described in the data flow model below (fig 1).  This tool uses the location of a set of "facilities" and "incidents" within a network such as a roads feature class.  Once it has determined those locations, the tool can consider additional parameters to the analysis such as whether or not the roads being used have a hierarchy , for example if one would rather drive on a highway than backwater streets.  Once the facilities (terminals), incidents (mines), network (roads) and optionally barriers have been set, the tool can be run.  Upon completion, the tool will provide a "Routes" dataset which is a collection of the fastest paths from each mine to each terminal.




fig 2: the final data flow model for this project.

     Once the routes have been found, the next task of the project is to determine the cost of each mine to the county for road maintenance.  The first step here is to parse out the route data by county, which can be accomplished easily enough by intersecting the routes feature class to a county feature class and then running the statistical summarize tool.  This gives a feature class of each county's routes used by the network analysis.  Then, in the attribute table which contains the data for each county's routes, a new (float) field was created which ran each route summary through an equation that was determined to roughly estimate the cost to each county of each mile of road.   This entire process was completed with the Model Builder tool, and this was the first project on which I worked with the Model builder.  The final model is given as figure 2.

fig 3: the calculation of cost per county

     The equation given was 100*[Miles]*0.022. The coefficients were determined as follows: each mine can expect to make 50 trips in a truck to each terminal (and 50 trips back), and the cost to the county is 2.2 cents per mile.  Therefore, the annual cost to each county in Wisconsin for road maintenance because of these silica mines is described as 2.2(miles), or as expressed in the Arc as described above (fig 3).  With the cost to each county estimated, the final maps for this project could be developed and are provided in the results section below.

Results:

fig 4: the results of the closest facility tool

fig 5: the results of the final analysis with cost per county symbolized.
 

Discussion:

     The results of this analysis may have been skewed by a couple of factors.  First and foremost, because of the scope and goals (educational) for this project a number of assumptions were made.  The number of trips that each truck was sent on to the nearest terminal was 50 per year, which seems extremely low.  Also, many mines have rail lines that pass through them in to which they are connected and therefore they do not need to drive to a terminal, costing the county in some cases millions of dollars.  Finally, it may have been unfair to use a different source and set of methods for Trempealeau County's mines than for other counties', as the TC mines were pulled from a geodatabse where every single mine was listed and addressed in the county which probably artificially raised the costs of this county in the analysis significantly.

     I learned to highly value the model builder in this project.  It helps to keep track of what I'm doing and organize my project to help give a clearer vision of what needs to be accomplished as the analysis continues. I also gained an appreciation for county webservers which provide accurate, high-quality information freely to the public.