Raster Analysis of Frac Sand Mine Locations

Introduction

This assignment was the last step in a semester long project involving Frac Sand Mining in Wisconsin. In order to complete this assignment, multiple geoprocessing tools involving raster datasets needed to be used. Multiple models were built, including frac sand mine site suitability model, sand mine risk model and the final result is an overlay of both models to find the most ideal site for a frac sand mine. 

Goals and Objectives:

There were multiple datasets used in this assignment. Many of the data sets were already downloaded from a previous assignment in class. In order to find the most ideal site for a mine, five different raster datasets were analyzed. This included geology, land use land cover, distance from rail road terminals, water table depth, and a digital elevation model (DEM) of Trempealeau county to find the slope. The layers needed to create a risk model included streams, farmland, zoning, schools and proximity to wetlands.  All of these data sets were acquired from the Trempealeau County database used in exercise five, from earlier in the semester. 

Methods:

The first step in this assignment was to clip Trempealeau County by a smaller section. The purpose behind using a smaller section of the county was to speed up the time it took to geoprocess all of the results.  

Figure 1.1 Shows the first model used to create a model of suitable
areas for frac sand mining based on land use and land forms.

This first model (Figure 1.1) was used in order to find the most suitable location for implementing a new frac sand mine. Some of the features had to be projected and another had to be converted into a raster. Without all of these datasets converted to a raster file, the analysis would not have been able to take place. Once all the datasets were reclassified, raster calculator was needed in order to overlay all the results and find the most suitable location to implement a new mine. 

The figure below (Figure 2.1) shows the second model used to create a model showing where frac sand mines should not be implemented. This model used much of the same tools as the first model, but was slightly more extensive. 

Figure 2.1 Shows the model used to create a model of suitable areas for mining based on environmental and community impacts.

The final result of both models combined is shown below (Figure 3.1). 

Figure 3.1 Shows the two models put together.

In order to run all of the tools shown above, different criteria had to be developed. Multiple classes had to be developed in order to run raster calculator effectively at the end of the model. All of the values input into the table were up to the students and what they thought was appropriate. There could be multiple variations due to the open criteria presented to the students. The table that I created shows values that are fairly conservative. If a sand mine was to be created, it would be created in the most optimal location. Figure 4.1 (below) is a table showing the different classification for the criteria used in creating these different models.

Figure 4.1 A table showing the different criteria and parameters used in creating a suitability model for a frac sand mine. 

Results

The results from the data flow models were very interesting. The figures below show the results from the models. 

Figure 5.1 A map showing which areas are the most suitable for implementing a new sand mine based of of land use
and land forms.

Figure 5.1 (above) shows the results of areas where frac sand mining is suitable taking land use factors into consideration. The red color represents areas where the suitibility is exceptionally high. The yellow areas show areas that are the not the best, but could still used for frac sand mining. The green color shows areas that should not be used. Since the criteria that was used for this particular project tended to be strict, there are small amount of land that are highly suitable for mining without affecting many different things.

Figure 6.1 (below) shows the areas that are suitable for mining taking environmental factors into consideration. Green represents areas that could be used for mining. Yellow shows areas that would be suitable for mining if they needed more land. Red shows areas that should not be mined due to their possible impact on the environment.

Figure 6.1 A map showing the areas most suitable for a sand mine base on environmental and community factors.

Figure 7.1 (below) shows areas the are the best to implement a sand mine. This map is the result of overlaying not suitable sites, and suitable sites for a sand mine.  Red represents areas where frac sand mines should be implemented. Yellow shows areas that are average quality and green shows areas that should not be included for the possibility of a mine creation. 

Figure 7.1 A map showing the final results from the two model results overlaid.

There are many areas that are green, but not very many areas that are covered in red. Theoretically this would mean that mines should be created in limited areas. This is not always the case, and sometimes these mines are created in less than ideal locations. Without taking many different factors into consideration, the creation of a mine can cause great effects on a particular are.

Conclusions:

This assignment was the final segment in a semester long project. Although the numbers used in this exercise were generalized and theoretical, the procedure was the most important part. It gave the student exposure to working with raster data and allowed them to analyze the data in a real world application. Frac sand mining will continue to take place in the future, so these models are extremely important. Without taking many different factors into consideration, the creation of a mine can cause great effects on a particular area.

Sources:

Trempealeau County Geodatabase

ESRI Database

Network Analysis: Frac Sand Transportation

Introduction:

The main goal of this lab was to become familiar with using network analysis. This tool was used in combination with a semester long process on the effects of frac sand mining in western Wisconsin. This segment of the project was used to analyze the effects that trucking the sand has on county roads. Since the county pays for the upkeep of the roads, it is important to know exactly how much it costs to keep up the roads due to the heavy use by sand mining trucks.

Goals and Objectives:

In order to get a list of mines used for analysis, a python script had to be written. This script was used to query out the mines based on a defined criteria. After the desired mines had been selected, they were then used within the network analysis window. Using the network analyst toolbar, a route was generated showing where the frac sand trucks transported the sand. Hypothetical parameters were then set up in order to create an equation. This equation was then used to evaluate the cost of damage the sand transport trucks are causing on local roads.

Methods:

The data that was used was the obtained from previous course assignments. This included the rail terminals, frac sand mine locations and a new street data set from ESRI. Network analysis was used in order to locate the routes that the frac sand hauling trucks would take on the different county roads. Once the different locations were added into the model, the solve tool was then used to create the different routes. Once the routes were created, they needed to be selected and were then copied into the desired geodatabase using the copy features tool. The results were not projected into the correct coordinate system, so the project tool was necessary in order to display the results correctly on the map. The main goal was to see how much it damage the frac sand trucks were causing on the county roads. Three new fields were then created in order to complete this task. The results were based off of hypothetical numbers provided within the lab instructions.
There were three different equations used to populate the newly created fields.
   
     1.) [Shape_length] /1600
     2.) [Miles] *100
     3.) ( [trip_miles]*2.2)/100

The first equation converted the distance of each route from meters to miles. The second gave the total distance the trucks traveled based on 100 trips taken. The final equation shows the final cost of all the trips on the county roads.

There were multiple different tools used in this particular model. The different tools included, Make Closest Facility Layer, Add Locations, Solve, Select Data, Project, Copy Features, Add Field, Calculate Field, Join Field and Summary Statistics. All of these tools are displayed below in the Model Builder Workflow (Figure 1.1).  

Figure 1.1
The final data flow model was used to generate the results for the
cost of damage cause to the county roads. 

Results and Discussion:

This exercise was designed to use network analysis to show the shortest routes for the frac sand transport trucks to use. The final results show the roads the trucks would use. Some counties have a higher concentration of routes, which will have a larger impact the the roads. Figure 2.1 shows the final results of where the routes are located.

Figure 2.1
This map illustrates the final results of where the routes are located across the state of Wisconsin. With one route being in Minnesota. 

The table (Figure 3.1) and graph (Figure 4.1) below, show the total cost of damage cause on the roads. There is a great variation between the number of routes in each county and the cost each of the counties has to pay.

Figure 3.1
 Shows the cost of damage the frac sand trucks cause on county roads.

Figure 4.1
This is a table illustrating the total number of routes through each county and the cost associated with the routes.

 Conclusion:

Before this assignment I had never used network analysis and only used model builder on short exercises. Both of these tools proved to be very useful for different reasons. Model builder allows to keep your data very organized and see where you may have made a particular mistake. Although the results from this that are purely hypothetical, it still showed how useful network analysis can be in a real world application.

Sources:

Wisconsin DNR

ESRI Database

Data Normalization, Geocoding, and Error Assessment

Goals and Objectives:

The purpose of this lab was to become familiar normalizing data tables. Data about frac sand mines were obtained from the Wisconsin DNR. The format in which the address was recorded was not consistent. Different address types were listed, while some did not have an address at all. The goal was to normalize the table in order to geocode the locations of the selected mines.

Methods:

Geocoding the mines was a two step process. First the tables had to be normalized. Once they were normalized, geocoding could take place. Many of the addresses had the necessary information for geocoding to be successful the first time. The few locations that did not run successfully required further searching for their locations. Using the PLSS data provided for the location, that was used to locate the general area of the mine. Once the area was located, aerial photographs were used in order to locate the mine a on a map. A street could be located and an address could then be written for that location.

Results:

Each student was given a selected set of mines to geocode. Their mapped mines would then be used to compare their results with the rest of the class. The purpose of this exercise was to show the different variation in where the points would be mapped, based on the data the students received. An emphasis was put on the importance of data quality and standard formatting to eliminate, or reduce error.

The table below (Figure1.1) shows the data table that the students received from the Wisconsin DNR.
This figure illustrates the inconsistency in the format for addresses to the mines. Without normalizing the table, ArcGis would not be able to geocode these different locations. The student had to manually manipulate the data. New fields were created in order to separate the different parts of the address into their own fields.

Figure 1.1 shows the data table before it is normalized. 

 The table below (Figure 2.1) shows the data table after it has been normalized. The address is broken up into separate categories needed in order to geocode the addresses. The address field from Figure 1.1 needed to be broken down. The resulting fields needed were, PLSS, State, City, Zip Code and Street. These new categories allowed for geocoding to occur.

Figure 2.1 shows the data table after normalization has occurred.

Figure 3.1 Shows the final map of frac sand mines.

The map to the right (Figure 3.1) shows the end result of where the other students mines are in location to where my mines were mapped. Since the mines do not completely overlap, it shows how there was error that occurred in the process of normalizing the data table. Since there was not one consistent format, the mines ended up in different locations.







The table below (Figure 4.1) is the end result showing the distance between my mines and the mines that the other students mapped. The point distance tool was used in order to get a distance between the different mines. As the table shows, there was great distance between the different mines. This illustrates just how inaccurate data can be without consistent formats.

Figure 4.1 shows the final table of distances between the mines mapped.

Discussion:

There are two different types of error associated with geographic data. The two types of error are inherent and operational. Inherent error occurs due to the nature of what geographic data represents. There will never be a perfect representation, so there will always be a slight amount of inherent error. Also with this type of error, it can occur when data is transferred between different storage devices as well as changing the coordinate system. This applies directly to this project. The data was stored in decimal degrees for distance. That is not the easiest form to measure distance. The data had to be projected into a new coordinate system. This allowed for the distances to be measured in a more recognizable unit.
The second type of error is operational. These errors typically occur during the collecting or using of geographic data. It is also sometimes called processing errors due to the fact that this error shows itself during while it is being worked with.

Results:

This lab showed the importance of normalizing a data table and proper formatting of the data. If there is not a standard procedure for storing data, it can create problems. People that access the data may not realize how accurate or inaccurate it may be. Without standards to go by, it will be hard to eliminate error or reduce it to a minimum.


Sources:

Wisconsin DNR
Tremealeau County Data

Python Scripting

Intoduction:

This script was used along with exercise 8. Different criteria was set during the exercise, but they were all looked with the same importance. In a more specific setting, different factors would have a higher importance than others when building a suitability model. This script was written for that purpose. It weighed a particular factor higher than all of the others. The student was able to choose which factor that they wanted to use. For this particular script, prime farmland was used as the more important factor. Figure 3.1 show the completed script.

Figure 3.1 The script showing which factor is more important than the others.

Introduction:

This particular script was used to help find the specific mines that the class needed for analysis. The end product had 41 one mines left. It made sense because of the limitations that were set while writing the script. Only active mines that were farther than 1.5 kilometers away from a railroad were mapped. The results will be used to to analyze the effects on county roads. Figure 2.1 shows the completed script used to complete the task.

Figure 2.1 A script written to select active mines within a certain distance from rail road terminals.

Introduction: 

Python scripting is the language used for ArcGIS programming. It can be very helpful executing different tasks in ArcGIS. This particular script was used to clip all of the downloaded data to Trempealeau County, WI. Figure 1.1 shows the final product.

Figure 1.1 A script used to clip all of the data downloaded to Trempealeau County, WI

Data Gathering

Introduction:

This lab is the first step in the semester long project involving the suitability/risk of frac sand mining in western Wisconsin, specifically Trempealeau County. The main goal of this assignment was to become familiar with downloading data from many different sources. It also provided the base data that will be needed to complete the rest of the project throughout the semester.

Methods:

The first site used to obtain data was from the U.S. Department of Transportation. The railway network was downloaded from this particular site. It was not needed in this particular assignment but will be used in the future to display the location of sand mines in relation to current railways. The next two data sets came from the USGS National map viewer. The first one downloaded was the land cover data set. The second data set to be downloaded was the elevation. Since this is a national database, the assignment called for only Trempealeau County data to be downloaded.

The next file to be obtained was the Trempealeau County geodatabase. This was obtained directly from the Trempealeau County website. The final piece of information that was needed was the soil survey. This was obtained from the USDA NRCS website.

Once all this data was downloaded, it had to be unzipped in order to be used. Since most of the datasets were at a national scale, the data had to be manipulated. A python script needed to be written in order to complete this task. The script was written to create multiple images clipped to Trempealeau County from the multiple data sets that were downloaded.

Data

Figure 1.1 shows the results of the different data sets that were downloaded. They are all clipped to Trempealeau County.

Figure 1.1

Each one of the data sets has metadata associated with it. Certain sets of data have more metadata than others. This is very important information for the user. It shows how accurate the data is and how reliable it may or may not be. For each data set we had to find the scale, effective resolution, minimum mapping unit, planimetric coordinate accuracy, lineage, temporal accuracy and attribute accuracy. Below (Figure 1.2) shows the results of this. 

Figure 1.2

Conclusion: 

So far we have downloaded most of the data that will be needed to complete this project. It is all being stored in a geodatabase for this project. It will be interesting to see what the final results show about the suitability of frac sand mining in Trempealeau County.

Sources: 

http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html http://nationalmap.gov/viewer.html http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx http://nationalmap.gov/viewer.html http://datagateway.nrcs.usda.gov/GDGOrder.aspx

Frac Sand Mining

Introduction:

For many years frac sand has been used for multiple things such as glass manufacturing. With new developments in technology, there are now more advanced ways to extract oil from the earth. That is where the frac sand boom comes into play. This new process used for extracting oil is called hydraulic fracturing. It splits rock and then sand is then used to keep the cracks open underground.   The sand that is needed has to meet very specific criteria. It has to be nearly pure quartz and rounded in shape. Wisconsin has an abundant amount of this particular type of sand. Many of the deposits range from the northwest part of the state all the way to the south central part of the state near Marquette and Adams counties. 

With the recent demand for frac sand, Wisconsin has been a hot spot for sand to be obtained. It has the highest amounts of sand necessary for hydraulic fracturing. With all of this sand available, there has been a need for many frac sand mines. According to the Wisconsin DNR, as of May 1^st, 2014, there were 121 sand mines in the state. Of those 121 mines, 63 were currently actively mining.  The figure below (1.1) shows where the sand is located in the state of Wisconsin. It also details where different frac sand mines were located. This map is from 2011 so it is slightly dated, but it gives a good outline of the locations of where all the sand deposits are positioned.

Figure 1.1 shows the locations of Wisconsin's frac sand mines as of 2011.
Source:http://wcwrpc.org/frac-sand-factsheet.pdf 

The figure below (2.1) shows why frac sand mining is so popular in the state of Wisconsin. Out of the whole United States, Wisconsin has the highest percentage of the sand that is needed for hydraulic

fracturing.

Figure 2.1 This map is showing where the best frac sand is located. Wisconsin has a large amount of this type of sand.
Source: http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Consequences:

These sand mines benefit the local area in many different ways.  They help to provide jobs for many different people. It will also help to boost the local economy. Since technology has allowed for different ways to extract oil from the earth, this sand is beneficial to help extract this resource from many new places. 

Although this sand allows for more oil to be extracted, it comes with a price. Frac sand mining has many negative environmental impacts. Some of the most prominent are the impact on soils and air quality. Since the sand is below the surface of the earth, topsoil has to be displaced. This causes acres of productive farmland to be taken out of production. With the soil being disturbed, it can also cause erosion. The loose soil is not held together by anything anymore, so if a strong rain falls, erosion can easily occur. The transportation of the sand also causes lots of dust to blow around the locations of the mines. This dust contains crystalline silica, which can cause cancer at elevated levels. This is an area of concern with more mines being built and more sand being hauled away.

GIS technologies can be extremely helpful in the future implementation of frac sand mines. Since sand mining involves many different industries, such as railways, roadways and the locations of the different mines, GIS will help tremendously. It will allow for the best transportation routes to be made from the mine to its destination determined by different parameters. 

Sources:

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

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

http://www.cbsnews.com/news/fracking-debate-turns-to-wisconsin-as-sand-mining-for-drilling-booms/