GIS II: Network Analysis

Goals & Objectives

The goal of this lab was to perform network analysis in order to calculate the impact of frac sand trucking on local roads as a part of our semester-long GIS II project.  

Background: 
Before beginning this lab, I completed some background reading to inform myself on the effects of fracking truck transportation upon local communities by reading the white paper "Transportation Impacts of Frac Sand Mining in the MAFC Region: Chippewa County Case Study."  As the text explains, frac sand mines are often located in remote areas in the country, which means that many of the resources used for the mining process (water, sand, chemicals, drilling equipment) need to be transported to and from the mining site.  This transportation has adverse effects upon local roads, which has caused local governments to explore ways to mitigate this damage.  Chippewa County, for example, has developed a series of road upgrade maintenance agreements (RUMA) to deal with recovering road damages, funding maintenance, and grading cross improvements (Figure 1).  

It is important for counties to be aware of the level of heavy truck traffic so they are able to plan accordingly for repairs.  Different types of sand mining operations have different transportation impacts as well, all of which require varying levels of cost and mitigation efforts (Figure 2).  Wisconsin legislation states that counties must be reimbursed for damage incurred on county highways, so knowledge and tracking of damages can be financially beneficial to county governments.  

Figure 1: list outlining aspects of a quality RUMA (white paper)

Figure 2: table detailing impacts of different frac mining operations (white paper)

Methods

At the beginning of this lab, I wrote a Python script (view here under Python Script #2) to select the mines to be used in my network analysis.  This script selected mines that were: 

• active
• did not have a rail loading station
• not within 1.5 kilometers of a railroad

After this, I utilized Model Builder in ArcMap to create a model (Figure 3) that achieved these objectives: 

• determined to which rail terminal each mine should travel
• determined the most efficient route from each mine to a rail terminal
• determined the length of each route (and cumulative lengths within each WI county)
• estimated hypothetical costs that each route/county would incur

To achieve the first two objectives, I utilized the Make Closest Facility Layer tool to specify a route from each mine to a rail terminal.  Then, I projected these routes as well as my counties feature class to a Wisconsin projection.  After this, I used the Tabulate Intersection tool to carry out an intersection between the routes and counties boundaries feature classes.  Then, I added two new fields to the resulting table, namely a Roads Length and a Costs field.  In the first, I used Field Calculator to calculate the total length of roads affected by trucks in each county.  In the second newly created field, I used hypothetical values to calculate a cost estimate.  In this hypothetical scenario, I assumed that each mine had 50 trucks travel to the rail terminal and back again, with it costing 2.2 cents per truck per mile. It is important to note that these values are purely hypothetical and do not represent actual transportation costs by any means.  The final attribute table displayed route lengths and estimated costs by county (Figure 4).    

Figure 3: network analysis model in ArcMap

Results & Discussion

The results of my methods can be seen below (Figure 4, Figure 5).  The table and map clearly display the scope of frac sand mine trucking and its effects, which are especially prevalent in Chippewa County, Eau Claire County and Barron County.  It is interesting to consider these routes after reading the aforementioned white paper; each county government has a say in how these routes and roads are used and maintained by frac sand mine trucks.  In Chippewa County, RUMAs are implemented to ensure that roads are kept up and frac sand mine companies are held accountable for the damage they incur on the roads.  Such results could prove useful to county governments in negotiating compensation from frac sand mining companies.  

Figure 4: output table from model displaying road length & cost by county

Figure 5: final map showing truck routes

Conclusions

Prior to completing this lab, I had primarily only considered the negative effects that frac sand mining has upon humans and the environment.  This lab, however, caused me to think about the ethics involved in fracking.  Public roads are free to be used by everyone, but when certain vehicles' intensive use causes damages, should they be held accountable?  According to Wisconsin law and my own convictions, yes.  The process behind calculating the effects of such damage and the monetary compensation required can be done via network analysis in ArcMap.  

Sources

National Center for Freight and Infrastructure Research and Education. (2013). Transportation Impacts of Frac Sand Mining in the MAFC Region: Chippewa County Case Study.  Retrieved from http://midamericafreight.org/wp-content/uploads/FracSandWhitePaperDRAFT.pdf 

Network Dataset analysis performed using data from ESRI Street Map USA.

GIS II: Data Normalization, Geocoding, and Error Assessment

Goals & Objectives

The aim of this lab was to gain experience in normalizing raw data, geocoding addresses, and conducting error analysis.  As this lab is an extension of the GIS II semester-long frack sand mine project, the study area was on Trempeleau County and nearby counties in western WI.  Data used in this lab was provided by the WI DNR.  

Methods

This lab involved multiple phases, as detailed by Figure 1.  

Figure 1: work flow of geocoding process

Normalization

Firstly, I normalized the raw data which was provided by the WI DNR in an Excel table (Figure 2).  The normalization process involved separating PLSS descriptions from street addresses, as well as placing other information into the appropriate columns.  After finishing this, the table was ready to be imported into ArcMap (Figure 3).  

Geocoding

Next, I used the geocoding service provided through ArcGIS Online to geocode my mines.  Since some mines had street addresses, some had PLSS descriptions, and some had both, I used two different techniques to locate the mines.  

I first took a look at all of the addresses that had been automatically geocoded by ArcGIS to see if the locations seemed accurate.  For the mines that had street addresses given by the WI DNR, the automatically geocoded locations were often nearby the actual locations. If the given address seemed inaccurate, I utilized Google Maps and/or the WI DNR website's map of frack sand mines to try to identify the correct location.  Then, upon finding a likely location, I manually selected the address.  

If a mine only had a PLSS description and no street address, the automatically geocoded location was always incorrect and needed to be manually located.  To do this, I imported PLSS base data including townships, sections and quarter-sections.  Then, I was able to identify the mines and manually select the correct address.  

Error Analysis

After geocoding all of my frack sand mine locations, I compared my geocoded locations to my classmates' and the actual mine locations.  This process involved using the "Near" tool in ArcMap and creating a map and error table to display the differences in locations (Figure 4 and Figure 5.  

Results

Normalization

Figure 2: raw data provided by the WI DNR

Figure 3: normalized data ready for import into ArcMap

Geocoding & Error Analysis

Figure 4: map depicting geocoded locations

Figure 5: table displaying differences in geocoding results;
"null" denotes that no other locations existed for comparison

Discussion

Though most of my geocoded locations were more or less accurate, elements of error were also present, as seen by Figure 4 and Figure 5.  The three locations that were most inaccurate (from the actual locations) were mines 209, 210 and 305.  Using Lo's chapter entitled "Data Quality and Data Standards," I took a look at the reasons behind these errors. 

Error in the first two sites, 209 and 210, could be explained by attribute data input error due to inherent causes.  The addresses were 125 19 1/4 St. and  2559 5 1/4 Ave., both comprised of multiple numbers which could have easily been slightly jumbled (i.e. 125 19 1/4 St vs. 1251 9 1/4 St.) during initial input by the WI DNR.  Furthermore, this error could be explained as feature classification or coding error as a result of operational causes--ultimately a gross error.  Since both 209 and 210 were inactive mine sites, they were more difficult to identify and could have led to human blunder when geocoding.  

Error in site 305 is most likely the result of human blunder and is best classified as gross error.  This site only had a PLSS description and not a street address provided, so I had to utilize PLSS base data to try to locate the mine.  I found this process much more difficult since only a general location was provided, not a street.  

Ultimately, we can know which locations are actually correct by using the latitude and longitude data provided by the WI DNR.  This data is the most accurate data on-hand, though some gross error could have occurred during the DNR's data collection process in the field. 

Figure 6: error table (Lo, Chapter 4)

Conclusion

Through this lab, I learned the intracacies of the complicated process known as geocoding.  Though geocoding is an important aspect of spatial analysis, it is important to be wary of inherent and operational errors that may occur and affect your final outcomes.  I think that a better result could have been achieved in this lab if we had worked with the other five people who were geocoding our same mines to develop more consistency in methods.   

Sources

Wisconsin Department of Natural Resources. Retrieved April 4th, 2016 from http://dnr.wi.gov

Lo, CP. (2003). Concepts and Techniques in Geographics Systems. Retrieved April 4th, 2016.