Network Analysis

Goals and Objectives
The goal of this assignment was to learn how to perform network analysis and use model builder to automate the process. In order to apply network analysis to the theme of frac sand mining, we assessed the cost taken on by several counties in Wisconsin. This is a direct result of the increased traffic from overland shipping of frac sand between min and rail terminals. The method of network analysis is to help better understand the impact of frac sand mines on the county roads.

Methods
In order to use the network analysis tool, I first had to prepare the feature classes that are going to be utilized. The data that was being prepared was obtained from the Wisconsin DNR. The data I am going to work with involves sand mines that have to ship their sand by truck to the nearest rail terminal. The specific mines that were selected had to be currently active, and within 1.5 kilometers from a rail terminal. In order to select those specific mines the process used here was a python script. My results gave me 44 total mines in Wisconsin.

The next step of the lab is where network analysis is used to obtain the map routes to find the closest facility. Here the street network data set from ESRI street map along with the rail terminals in Wisconsin were used during the network analysis. I then learned how to use the closest facility tool by inputting the correct parameters such as the "incidences" are mines and "facilities" are the rail terminals.

Figure 1. Data flow model using model builder

After that step was completed, model builder was used to create a flow chart to figure out the distance and cost of each county caused by the haul trucks going to and from the mines and terminals (Figure 1). First I used the "Make closest facility layer" tool and the "add location tool. The mines were set as the incidences and the rail terminals were set as the facilities which allows me to set up my network analysis. I then had to use the "solve" tool by routes to determine the closest station to each mine. After that, I used the "select data" and "copy features" tools to create a feature class from the routes that had been solved. Next, the "project" tool was add to change the coordinates system to NAD 1983 HARN Transverse Mercator in order to get the correct measurements for later calculations. The "summary statistics" tool was used to create a table with the route distances broken down by each individual county. Once the routes were separated by counties, I could then create two different fields and calculations. The first field added was the total distance of the routes in each county. This was calculated by taking the SUM of the Shape Length of the route and multiplying it by .000621 (the amount of miles in 1 meter) to get the total distance in miles. The second field I added was to find the amount of cost each county had to accumulate annually due to the higher amounts of haul truck traffic. This step was calculated by multiplying 50 trips a year, 2 trips back and forth, total miles, and .022 for 2.2 cents a mile. The equation looked like this: 50*2*[total_distance]*.022
The numbers used for these equations are all hypothetical.

Results
The results from the model builder have been exported and used as excel data (Figures 2 and 3). In excel I created a chart that is shown below for a better understanding of the model that had been previously run.

Figure 2. This chart shows the amount of miles traveled annually in each county by the sand haul trucks to and from the nearest rail terminal

Figure 3. This chart shows the annual cost each county sustains while having sand haul trucks drive to and from the nearest rail terminal

Figure 4. The final map showing the closest routes from mine to rail terminal along with the hypothetical cost of maintenance of each county

Conclusion

This lab gave me the abilities to learn how to perform network analysis on applications used here in the state of Wisconsin. Even though these results were very unique and accurate it is all hypothetical to the actual world. This can be greatly utilized for future use and application for specific jobs.

Discussion
The data shown above is actually quite different than what I was expecting to see. The counties did not suffer very much from annual use of the roads due to the higher amounts of sand haul trucks from sand mines to rail terminals. The worst county that suffered was Chippewa county at around $600 but that would be minor compared to how many miles are traveled for the use of sand haul trucks.

Sources
Wisconsin DNR
ESRI basemap

Sand Mining in Western Wisconsin

Introduction

Frac sand mining in western Wisconsin has been occurring for more than 100 years. This non-metallic resource is very abundant in Wisconsin and has suitable characteristics to be used for glass manufacture, golf courses, and more recently to obtain petroleum products by hydraulic fracturing. Frac sand is quartz that has specific qualities such as grain size, rounded, well sorted, and withstands high pressures.  After the frac sand is taken out of the ground, it is washed, sorted using some sort of sieve process, and dried to be shipped elsewhere. Most of the frac sand mining facilities today are located near railroads and major highways because there is so much abundance. Shown below in figure 1 are the locations of sand mines found in Wisconsin along with the sandstone formations outline in the state (Wisconsin Geological and Natural History Survey, 2012).

Figure 1. Frac sand mines and sandstone formations in Wisconsin

Much of the desired qualities for frac sand mining discussed above lie in certain formations in western Wisconsin. The formations that contain this frac sand include the Wonewoc, Jordan, and the St. Peter sandstone formations. These formations were formed during the Cambrian and Ordivician when shallow marine seas had covered western Wisconsin. Figure 2 shows a geologic map of the Midwest with the Cambrians Wonewoc and Jordan formations in red and the Ordovician St. peter sandstone in yellow (USGS Geologic map of North America Adapted from the map by J.C. Reed, Jr. and others 2005)

Figure 2. Major formations in Wisconsin for frac sand mining

Issues with frac sand mining in western Wisconsin

With the development of frac sand mines there are also problems that must be dealt with to proceed with the removal of the non-metallic resource. First, the companies must get permits from the city and state governments to allow the mining to continue. There are always concerns when it comes to the environmental aspect of mining.  This includes air emissions that are released during extraction, blasting, crushing, processing, and transportation of frac sand. Another issue that needs to be looked at after the mine is finished or used up the resource, they must think about reclamation processes required by the DNR. The DNR can however provide assistance to help create a good reclamation plan in order to create a more sustainable area.

GIS usage for further exploration

During this semester, my skills in GIS will be put to the test while I try to solve problems and the way in which I solve them with the frac sand industry. I will be using my skills to analyze data that I can acquire through various sources to better understand some of the environmental hazards created during frac sand mining. In this case, western Wisconsin will be my area of interest for frac sand mining and its environmental hazards.

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 February 27, 2017.

http://midamericafreight.org/wp-content/uploads/FracSandWhitePaperDRAFT.pdf

USGS. (2012). Frac sand in WI. Retrieved February 27, 2017.

http://wcwrpc.org/frac-sand-factsheet.pdf

dxm

WDNR. 2016 (last revised). Industrial Sand Mining Overview. Retrieved February 27, 2017.

http://dnr.wi.gov/topic/Mines/Sand.html

WDNR. 2012. Silica sand mining in Wisconsin. Retrieved February 27, 2017.

http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Exercise 6: Normalizing, Geocoding, and Error Assessment

Goals and Objectives

This lab required tedious normalizing of excel data about sand mine address locations that were not completely filled out correctly. The goal of this lab was to take those address and geocode that set of data given to us. We used this data to achieve a greater understanding of how normalization is key to providing correct reference data. The more accurate your data starts out with, the easier it is to geocode and locate the mines on the map.

Methods

The first step into this lab was to obtain the sand mine data that was given to me, and normalize the data in an Excel spreadsheet. I had to add new fields such as Street address, zip-code, and state in order to work with the geocoder (figure1).

Figure 1. Excel data given to me and normalized on the specific mines to geocode and locate

After normalizing the data was finished, I could now bring the table into ArcMap and use the geocoding tool. I had to set up the geocoding tool by selecting the correct columns, for example, Address to field Adress, City to the field City, and so on. The geocoder used was the "World Geocoding Service" to find and match the address. Even though there was a high percentage of matched locations, I still went and located and moved over half of them. I was able to geocode most of the addresses but some of the mines were only in the Public Land Survey System (PLSS) (figure 2). These mines that were only in the PLSS had to be manually located by using the description within the excel spreadsheet and the Wisconsin DNR data. In order to manually place the points, in the Interactive Rematch window for geocoding, I used the "Pick Address from Map" to relocate a specific point for it to be correct (figure 3). 

Figure 2. The results window after geocoding the sand mine data.

Figure 3. Interactive Rematch window where the "Pick Address from Map" tool is located near the bottom.

Lastly, I compared the results of my geocoded addresses with those of my classmates and those of the actual locations given to us by the WDNR. This required the use of the merge tool to input multiple datasets into a single new output dataset. Next I created specific layer by using query to specifically locate the mines I was assigned so they match up with the mines of my classmates and the mines of the actual locations. To find the distance between my specific mines, classmates, and the actual, was found using the Near tool and the average error was found using Statistics within the Near_Dist field.

Results

Map 1. Map of western Wisconsin Locating the differences in the mines that have been geocoded and those of true location.

In the map above, there was both location errors of a large magnitude and some that were less than 200 meters off. This is also shown in the data in Figure 4 below. Comparing data with my classmates had a larger average error to location distances than the actual locations (Figure 5). 

Figure 4. The queried mine locations between my own and the actual mine locations to show the error distances.

Figure 5. The queried mine locations between my own and some of my classmates mine locations to show the error distances.

Discussion

Multiple reasons come up to why there is such a difference in the distance between the geocoded points and the actual mine locations. In Lo they are listed out and discussed in a table. Gross errors is one that comes up but should not occur with this data because they are all assumed as sand mine locations. However, there could possibly be some systematic errors found within the data. A reason for this is the fact that not all of the mines were shown on the base map making it harder to be as accurate as possible. There could also be some random errors which would happen from either making a simple mistake or moving a point that was not supposed to be moved. This lab was a great lesson in learning how the different errors can really change the data results. Its very important to have accurate data when your using real life decision making skills. 

Inherent errors can be a large source of the errors in geocodings. This is when errors are made while digitizing the data the fact that each dataset was created differently by each of my classmates can mess up the data and therefore be misunderstood. Operation errors were also a considerable part of the lab because most of the students may have used the data incorrectly or made a mistake when inputting or using the data.

In order to get the correct points compared to the ones that are incorrect, a great way to go about it would be to obtain a complete list of all the addresses of each mine. Without this information however, as a group we could go through each point individually and discuss where the correct address is located on the map. Another helpful measure is acquiring the latitude and longitudinal data for each of the mines.

Conclusion

Normalizing data, understanding the data, and accurately using the data is extremely helpful when geocoding in ArcMap. In order to make the process easier, having a set of rules to the data would allow for a smaller chance of error later on. 

References:
Lo, C., & Yeung, A. (2003). Data Quality and Data Standards. In Concepts and Techniques in Geographic Information Systems (pp. 104-132). Pearson Prentice Hall.