Sand Minning Suitability and Impact Modeling with Raster Analysis

Goals and Objective:

In our final lab of GIS 2 I was put to work to determine an area within Trempealeau County, which is located in western Wisconsin, for a new frac sand mine.  To do this I had to create a suitability model of the land, and I had to create an impact on the environment model as well. I used the DEM from Lab 5 as well as using the land cover and geology of the region.  In this lab we utilized the Reclassify, Euclidean Distance, Feature to Raster, Slope, Block Statistics, Map Algebra, and the Project tool.  I then combined using Map Algebra the suitability and the impact analysis to determine the best location overall for a potential sand mine.  

Methods:

Sustainability Model:

Site Criteria for Sand Mining Suitability:

• Geology

• Land Use Land Cover: agricultural (herbaceous planted/cultivated) land use

• Distance to railroads

• Slope

• Water table criteria

These variables came in all different types, and some need more work than others. The end goal was to transform these into raster to be combined together to determine the sustainability model.  For most we needed to determine a rank on a scale of 1-3 with 3 being the most suitable and 1 being the least suitable (Figure 1).

Figure 1: Shows the ranks I used in my model along with my reasoning. 

In Figure 2 below there are many different sets of tools each variable had to go through to get to its final stage.  In the Geology Class I selected the Jordan and the Wonewoc sandstone formations which are frac sand mines target layers while giving these a 1 while the others get a 0 as they are not targeted by the sand mines.  

Figure 2: This is my model builder for my Suitability Model. It included many reclassify tools, euclidean distance, and for the first time the slope tool.  

Figure 3:  This figure shows the outputs of the 6 variables that were taken into consideration. The darker colors are the most suitable, whereas the lighter yellow is the least suitable areas.  The red is the most suitable area whereas the yellow will be the least suitable area. 

Impact Model:

Next I made an impact model that areas that would be impacted the most if a mine was placed within the area, and we had 4 variables plus one of my choice which I used wilderness areas. 

• Proximity to streams

• Prime farmland

• Proximity to residential areas (noise shed and dust shed)

• Proximity to schools (noise shed and dust shed)

• Wilderness areas

Figure 4: This is my model that I created for the Impact Model.  It includes may reclassify, euclidean distance, and finally ends up with a Raster Calculator tool.

Figure 5:  This Table Shows the five variables I used in my model and the way I reclassified each.  I also gave my reasoning on why these variables are important when picking a location for a frac sand mine.  

Figure 6: This is the results from my model builder (Figure 3).  It shows Residential Areas. Schools, Wildlife areas, Farmland, and Streams.  These are all variables that have the potential to have an impact from a frac sand mine. The yellow areas have the highest impacts and the red has the least impacts.

We were then able to take the models we used and combine them together to create an Optimal Sand Mine location.  

Figure 7: Showing the final raster calculator giving the Optimal Sand Mine Location.

Figure 8: This is the Optimal Frac Sand mine location in Trempealea County, Wisconsin.  The Green areas are the least suitable, whereas the areas in the red-white range are the most suitable.  

Conclusion:

In Figure 8, we are finally able to tie everything together on one view of Trempealeau County.  The red-white areas are the most suitable and optimal place for a sand mine. Being able to use all of the different tools really puts in perspective how many objects really go into making a model and deciding on a location to put something new.  I know that if this was a real model there would be even more variables to consider than just the few that we used.  I also see now how easy it is for someone to mess around with the reclassify tool and get a completely different map than what I currently have.  

Sources:

Land Records. Trempealeau County Land Records. Geodatabase  http://www.tremplocounty.com/tchome/landrecords/

Network Analysis

Goal and Objectives: 

The goal of this assignment was to lean how to use and perform network analysis. I used model builder to automate the process for us.  Using model builder helped my understanding to show the flow of work that goes into network analysis and what steps need to happen at what time in the process to get to the end goal.  I applied network analysis to the determine the cost of frac sand mining across counties by the increased truck traffic from moving the sand from mine to rail terminal.  This method helped to understand the impact of frac sand mines that is not just the impact of the mine itself, but the entire county.

Methods:

I had to prepare the feature classes to use the network analysis tool. I am using the mine data that I revived from the Wisconsin DNR.  We have been using this data for a few weeks now.  Some of the mines are not currently active therefore not all of the mines are used.  The mines that I am looking at ship their sand from the trucks to the rail terminals. Using a python script helped too speed up the process which selected all of the active mines without rail loading stations within 1.5km of railway.  I was left with 44 mines.   

To start the second part of this exercise I was given a geodatabase that had rail terminals in Wisconsin and had to use these for my analysis. I also used a street network data set from ESRI street map which was used during the network analysis to learn how to map routes and find the closest facility using incidences and facilities. 

Figure 1: This is a model showing how I came to the conclusion of the graphs below.

Once I was finished with that steep I was to use model builder and create a flow chart (Figure 1). This flow chart was the tool to figure out the distance and cost associated with the roads due to the distance of the trucks going from the sand mines to the rail terminals and back.  First i had to use the Make Closest Facility Layer tool and the add location tool.  I used the mines as the incidents and the rail terminals as the facilities which allowed me to set up my network analysis.  I then had to solve by routes to determine the closest station to each mine. Then, I had to use the select data  and Copy Features tools to create a feature class from the routes that I solved for from the network analysis. I then had to use the Project tool to change it to NAD 1983 HARN Transverse Mercator feet to get the correct numbers to use in my calculations. I then had to use the Summary Statistics tool which allowed me to create a table with the route distances broken down by county.  I then had to create two different fields and use two different calculations.  The first field I had to create was miles from the total shape length.  I then had to create a field called cost for the amount it cost to repair the roads.  The two equations I used were multiplying the foot distance by 0.00018939 to get meters.  I then took the meters number and multiplied it by 2*50*[total_Miles]*0.22.  The numbers for cost and trips are hypothetical.

Figure 2: Final Map showing the closest routes from mine to rail terminal along with the cost of maintenance per county.

Results:

The results I got from the data from the first part can be shown below.  This shows the different routes and gives a better understanding of the model we previously used.  

Figure 3: This graph is showing the distance  trucks would need to drive within each county to get from the mine to the terminal.

Figure 4: This graph is showing the annual cost each county will have to spend on its roads due to the heavier traffic from the trucks delivering sand to the terminals.

Discussion:

I was actually very surprised by the price of maintenance.  I thought it was going to be much higher than I arrived at, but this may be due to things not being calculated.  I also feel that there area some other variables that may happen while the trucks are on the road that were not accounted for, for example, break downs, accidents, and extra trips.  To change any of the numbers we could also start to add the facilities outside of Wisconsin. 

One problem I thought of is that are there any counties that do not allow the trucks to drive on certain roads. I feel like this is something that should be looked into to get a better  detailed and more accurate representation, because I know that the trucks may not be allowed to always go the fastest way if its through a neighborhood or something of the like.  

Conclusion:

I have found that the network analysis tool is one of my favorite tools to use.  I could see myself using this tool in a future job, and I feel like it has some of the largest uses.  Its not just to find the fastest route, but to determine where things can be put or which way to go.  Being able to figure out the price for maintenance on the roads is beneficial, and the figure could be brought to a meeting about opening up a sand mine in a different county determining its affects on the surrounding roads.  I feel like this would be great for the people to see who live in the surrounding area.  

Geocoding Frac Sand Mines

Goals and Objectives:

The goal of this lab was to geocode a set of data that was given to us by the DNR.  This data was not formatted correctly, making it tedious to format each one. We used this data to gain the understanding of how normalization is key to providing data. It also shows the importance of accuracy of data.  

Methods:

Before anything could be done in ArcMap I had to normalize the data that was given to me (Figure 1).   I had to add new columns to the data to separate out different parts from different columns to make it work with the geocoder. By doing this, I was able to successfully able to use the geocoder for a few of the addresses. Sadly, most of mine were only in Public Land Survey System (PLSS) notation making it difficult to use the geocoder, therefore I had to manually locate the mines myself by using the description given to me in the address on the excel file. 

Once all of the addresses were normalized (Figure 2) I was able to upload the table into ArcMap and use the geocoding tool. I had to set up the geocoding tool by selecting which columns went to what on the geocoder. For example, on the geocoder addresses went with addresses. Esri's  The geocoder then matched the addresses using the "World Geocoding Service." The addresses did not come out exactly accurate.  This meant that I had to go into each point and assure they were in the correct spot with the sand mine.  I noticed in the once that I was specifically assigned to geocode that a few did not have the sand mine in the ESRI base map that I was using.  I then had to find it on google and locate it back on the base map using the roads around it.  This made it slightly more difficult, but using the PLSS helped to narrow it down even farther. I used the address inspector to unmatch the points and choose the correct point by using the "Pick Address From Map" tool.

Finally, I was then able to compare the addition of my colleagues results, my results and the actual locations of the mines. To do this I was able to use the "merge tool," which we recently learned in a demo, to combine the data together. Next, I used the "Near" tool to calculate the closest distance of my points compared to my colleagues points. 

Results:

When I first went to use the geocoder I had some of the addresses not showing up, put in the middle of the town, and not located anywhere near a street.  This was not the result I was hoping for, but expected it because I had very few with actually addresses and needed find the location of the PLSS to find the sand mines. 

Figure 3: A map showing the Merged Geocoding and the actual mine locations.

I then used the Distance to Point tool, which allowed myself to figure out the distance between my mines locations and my classmates (figure 4).  This was interesting because I thought the numbers were going to be much higher than they actually were, but ended up being much closer.  

Figure 4: The distance results between my mine locations and my classmates.

Discussion: 

There are so many reasons why there is such a different in the distance between the geocoded points.  In a class reading by Lo they are listed out in a table. I do not believe there to be any gross errors in this data because they are all assumed as sand mine locations.  However, there may be systematic errors found within the data because not all of the mines were shown on the base map making it harder to be as accurate as possible.  There even may be some random errors which could occur from just making a mistake or possibly just moving a point that was not supposed to be moved. This lab however was a great lesson in learning how the different errors can really change the outcome of the data; and how important it is to have accurate data when making real life decisions that are based on data that I have made/used.  

The Inherent errors in the data are actually a large source of the errors in the geocodings.  These are errors in digitizing the data. The fact that each dataset was created differently by each of my classmates really throws the data for a loop.  If it was only one person then only that person has to worry about the format, but with a whole class the format is constantly changing. Operation errors were also a large part of this lab because many students may have used the data incorrectly or made a mistake when using the data. 

To figure out which points are correct compared to the ones that are not correct I would want a full list of addresses of each mine, but without this information we could go over the data as a group to figure out which of the points are correct, and which ones are not.  having the latitude and longitudinal data is also very helpful, making it easier to find the location on the map.  

Conclusion:

Being able to normalize data, understand the data, and accurately use the data is extremely beneficial when geocoding. Have a set of rules would beneficial to the data; this would make it easier to use and having the guidelines would allow a smaller chance of error. 

References Cited:

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

Data Gathering

Introduction:

To understand the impacts of frac sand mining in western Wisconsin I needed to be able to download data from a few different sources.  The goals of this lab were to promote the skill of file management, use python code to create a geodatabase from the downloaded data, and be able to extract data from multiple sources. 

Methods:

I downloaded data from several different locations which include:

• The first was downloading the Railway systems from the US Department of Transportation statistics website.
• The next is the USGS National Map Viewer which allowed me to download data that included the National Land Cover and elevation from their website. 
• Next, I downloaded data from the USDA Geospatial Data Gateway which allowed myself to download cropland which comes from the U.S. Department of Agriculture. Website
• I then downloaded a Geodatabase for Trempealeau County, Wisconsin from the Land Records Office. Website
• Finally, I downloaded data from the Department of Agriculture once again, but from their Soil Survey website for soil data.

Data Accuracy:

• Scale - relationship between depiction on map and its actual size in the real world
• Effective Resolution - 
• Minimum mapping unit - The smallest plotable object at a certain scale. 
• Planimetric Coordinate Accuracy - How close the objects are to the real location on the Earth. 
• Lineage - a documentation that shows how the data was collected, used, and by who.
• Temporal accuracy - How up to date the data is, and when it was published. 
• Attribute accuracy - How accurate the data is represented when compared to real world.  The data is then recorded as a specific number for metric attributes and as an accuracy percentage for categorical data.

Conclusion:

This lab was one of the most difficult and frustrating yet.  It was challenging me in every way possible to figure how how to manipulate the data to correctly display it.  I was having trouble organizing, maintaining, and keeping it all together. I however did learn a lot from this lab and found it very important lab to go through. I learned many skills from this assignment that I will take with to future assignments. 

Python Scripts

Introduction:

Python is a computer programming program that is very user friendly is and is incredibly easy to pick up with a small amount of help along the way.  I found it easy to understand, and easy to find help using ESRI's online help function to guide myself through the process.

Script 1:

In Lab 5 I was able to create a script which allowed me to project, clip, and load rasters into a geodatabase, which used a loop code to go through the rasters and determine its datum.  The code will then decide if it needs to be changed and will apply the proper projection.

Script 2:

In lab 7 we had to select mines from the all_mines feature class that was given to us in the ex7.gdb.  These mines contained mines that are in Wisconsin that we also already worked on for geocoding. We had to select mines that are active and farther away than 1.5km from a rail road system.  Using this code allows us to find the mines that have a high impact on the road systems from trucks transporting their sand.

Script 3:

In lab 8 we utilized this script above to create a weighted model.  I decided to put weight onto the Schools because I feel they are a very important factor in deciding where to place a mine.  The point of this script is to show the difference of putting on different weights on the different variables that we used.  I noticed when I ran this one it really did put a larger impact on the schools area making it seem more important than the rest of the variables.  

Sand Mining in Western Wisconsin Overview

Introduction

Frack sand is silica sand or quartz (SiO2) which is mined and use for the petroleum business to obtain petroleum.  Fracking actually stands for hydraulic fracturing which is relatively new technique used in the oil and gas industry. Hydraulic fracturing is used to extract natural gas and crude oil from rock formations around the world, and here in Wisconsin is lucky enough to have some of the best sand to use for this process.  The location of the sand mines found in Wisconsin can be seen in figure 1 below along with a general location of the sand formations found in the state (Wisconsin Geological and Natural History Survey, 2012).

Figure 1. Frac sand mines and location of sandstone formations. 

The sand found in Wisconsin is desirable because of its size, shape, and sorting.  These are the characteristics used to look and determine the type of sand a unit contains.  In Wisconsin the formations used for frac sand are the Wonewoc, Jordan, Mt. Simon, and the St. Peter sandstone formations.  The largest concentration of these mines are found in the middle west part of the state.  The sand is formed from shallow marine systems which is where it gets its characteristic rounding and homogeneous grain size. This sand "belt" can be seen in red in figure 2.

Figure 2 Geologic map Showing Sandstone location (red), USGS

Problems

There are a few different issues that are associated with frac sand mining in Western Wisconsin which includes  two types of air emissions. First off, the pollutants that are emitted during the mining and handling of the sand.  The next air emissions pollutant includes removal/excavation, blasting, crushing, processing, and transportation of the sand. Therefore, there are permits and regulations that need to be obtained from the state, and city government that allow the mine to continue. Since there have been concerns regarding environmental problems as sand mines.  Mine siting is regulated at the local zoning level.  Mine reclamation plans that are required by NR 135 have to be created before the mine starts its business. The DNR provides assistance to the local authorities for these plans which help to create a sustainable area.

Roles of GIS 

This semester I will be using my skills in GIS and transferring them to figuring out problems and ways to solve them in the frac sand industry. I will be using my skills to analyze the data that involve environmental hazards. Western Wisconsin is going to be my area of interest for analyzing the environmental risks associated with frac sand mining.

Sources:

King, P. (2015, June 3). Wisconsin towns worry frac sand boom will dry up. EnergyWire. Retrieved February 22, 2016.

http://midwestenergynews.com/2015/06/03/wisconsin-towns-worry-frac-sand-boom-will-dry-up/

USGS. (2012). Frac sand in WI. Retrieved February 22, 2016.

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

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WDNR. 2012. Silica sand mining in Wisconsin. Retrieved February 22, 2016.

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