Exercise 8: Raster Modeling

Goal and Objective
The goal of the lab is to practice using a variety of raster geoprocessing tools to create models for sand mine suitably, and environmental and cultural risk within Trempealeau County in Wisconsin. During the lab I built a sand mine suitability model, and sand mine risk model, which I then followed by overlaying the two results to find the most suitable areas to sand mine characterized by the least amount of environmental and community impact.

Methods

Sand Mine Suitability Model
The first model was created to design an output for sand mine suitability based on the following criteria:

1) Geology
2) NLCD Land cover
3) Railroad terminals
4) Slope
5) Water table data

Each off these data sets were clipped to fit my study area of Trempealeau County, to determine a mining suitability rank for the land that falls inside this boundary. All of these feature layers were clipped to my study area, converted to a raster based on the desired field type, then reclassified. While reclassify, the feature class data values were divided into three ranks, based on their suitably for mining. Data ranges were given a 3 for more suitable, 2 for moderately suitable, and 1 for least suitable. The data ranges I chose for each data layer are descripted below for each data source, and listed in Figure 1. This allowed me to use raster calculator at the end of the lab to determine areas that had the highest and lowest suitability (Fig. 6).

Geology
Beginning with the geology of Trempealeau County, areas labeled as Jordan were most desired, and reclassed as a three, leaving the other geology types as a one.

NLCD:
Next, data from the NLCD website was gathered for Trempealeau County. Two rasters were created during this process. One raster included land with suitable mining area, and one included areas that should excluded from mining.

Rail Terminals
Next, rail terminal distance was taken into account. Here, I created a feature class of rail terminals that were within my study area of Trempealeau County. After this feature class was created, I ran the Euclidean Distance tool to display the distance after from this rail terminal. I then reclassified this feature class, and designated the areas closer to the terminal as most suitable, and farthest away as least suitable.

Slope
Following this, I used a DEM of Trempealeau County run the slope tool. The output generated a percent slope of areas within my study area. Next, I reclassify this based on the percent slope grade, and labeled the lowest slope as most suitable for mining, and the highest slope as least suitable.

Water Table
Using the watertable arc data, I used the convert the topo to raster tool. This gave me an output with the water table elevation in each cell. I then reclassified this data base on the elevation, where higher elevation was considered more suitable for mining by making it easier to collect water for the mining process.

Final Suitability Map
This output was created by using raster calculate to add all the above 5 layers together. Because I based my reclassify ranks as 3 to be areas of most suitability, adding the rasters allowed me to indicate areas with the highest final output raster cell value would be areas of highest suitability. This final map is displayed in Figure 6.

Figure 1: The five data layers used in the suitability model along with the data ranges chosen for reclassification of suitability. Reclass ranks are 0-3, where 3 represents most suitable, 2 represents moderately suitable, and 1 represents least suitable, and 0 represents excluded area.

 

Figure 2: Sand Mine Suitability model created in Model Builder.

Sand Mine Risk Model:
The first model was created to design an output for sand mine suitability based on the following criteria:

1) Streams
2) Farmland
3) Zoning
4) Schools
5) Wetlands

Each off these data sets were clipped to fit my study area of Trempealeau County, to determine a mining risk of impact to the land that falls inside this boundary. All of these feature layers were clipped to my study area, converted to a raster based on the desired field type, then reclassified. While reclassify, the feature class data values were divided into three ranks, based on risk of impact from sand mining. Data ranges were given a 3 for least risk for impact, 2 for moderately risk for impact, and 1 for least risk for impact. The data ranges I chose for each layer are descripted below for each data source, and listed in Figure 3. This allowed me to use raster calculator at the end of the lab to determine areas that had the highest and lowest risk for impact (Fig. 7).

Streams
Using the DNR hydro flowline data, I collected stream based on their ecological significance using the steam count field. Higher numbers were associated with primary, perennial running streams, and therefore were selected to be of high importance for analysis. These were selected out of the feature class and created into a new feature class. After, I projected it to WGS 1984 UTM 15N and clipped it to my study area. From here I ran the feature to raster tool, and next ran the Euclidean distance tool on the feature class. This gave me a final output of distance of each pixel to the nearest stream. I then reclassified this output, based on locations closest to the stream to have higher risk.

Farmland
Using the prime farm land feature class, I ran the feature to raster tool. Next, I reclassified the data based what time of farmland was more and least at risk. I specified areas such as agriculture fields and prime farmland should be of highest concern for impact, and areas not used as farmland should be considered of least concern.

Zoning
The zoning feature class was used to designate areas of highest potential for community impact. Within this class areas such as industrial, commercial, and residential zones were selected has areas to be avoided. Once these were created into a new feature class, I used feature to raster to convert it to a raster, followed by running the Euclidean Distance tool. This output gave me distance away from the areas, which I then reclassified, as areas closer to residential areas are of higher risk.

Schools
Here, I used the selected out the different schools within Trempealeau County to make a new feature class. After clipping and projecting the feature class to WGS 1984 UTM 15N, I was able to run the feature to raster to convert it to a raster, followed by running the Euclidean Distance tool. This output gave me distance away from the schools, which I then reclassified. Areas under 640 meters away from the school were considered excluded areas, and areas closer to the schools to be of higher risk

Wetlands
Here, I used the DNR wetlands feature class. After clipping and projecting the feature class to WGS 1984 UTM 15N, ran the feature to raster to convert it to a raster. Following this I ran the Euclidean Distance tool. This output gave me distance away from the wetland, which I then reclassified. I choose to specify areas under 800 meters (0.5 miles) away from the wetland were considered excluded areas, and areas as areas closer to the wetlands to be of higher risk.

Final Risk of Impact Map
This output was created by using raster calculate to add all the above 5 layers together. Because I based my reclassify ranks as 1 to be areas of highest risk, adding the rasters allowed me to indicate areas with the lowest final output raster cell value would be areas of highest risk of impact. This final map is displayed in Figure 7.

Figure 3: The five data layers used in the sand mining risk model along with the data ranges chosen for reclassification of suitability. Reclass ranks are 0-3, where 3 represents least at risk, 2 represents moderately at risk, and 1 represents most at risk, and 0 represents excluded area.

Figure 4: First part of risk model created in Model Builder.



Figure 5: Second part of risk model created in Model Builder.

Map of Final Sand Mine Suitability Map
This was created by added the final outputs of the Suitability Model and Risk Model using raster calculator. Therefore, based on the ranking systems previously specified, areas of lowest risk and highest suitability would now have the highest values on the Final Sand Mine Suitability Map. This output was reclassified and displays areas on a scale of 1-3, where higher numbers are better suited for mining locations. This final map is displayed in Figure 8.

Results

Figure 6: Map of Final Sand Mine Suitability Map created by reclassifying the 6 data layers displayed on the left portion of the map, followed by added these raster layers together using raster calculator to get the final Suitability output. Reclass ranks are 0-3, where 3 represents most suitable, 2 represents moderately suitable, and 1 represents least suitable, and 0 represents excluded areas. Therefore, highest numbers on final output represent higher suitability for sand mining.

 

 

Figure 7: Map of Final Sand Risk Map created by reclassifying the 5 data layers displayed on the left portion of the map, followed by added these raster layers together to using raster calculator get the final Suitability output. Reclass ranks are 0-3, where 3 represents least at risk, 2 represents moderately at risk, and 1 represents most at risk, and 0 represents excluded areas. Therefore, lowest numbers on final output represent higher risk of impact.

 

 

Figure 8: Python Script ran to generate output picture in figure 9. Stream were used as a weighted factor, however residential, wetland, school, and farmland data were still included areas of assess risk. More details about script are listed in my PyScript blog tab.

 

Figure 9: Python Output of Weighted Values. Python Script in figure 8, used to create this output. Stream were used as a weighted factor, however residential, wetland, school, and farmland data were still included areas of assess risk.

 

Discussion of Results and Methods
My results show, that based on my criteria section and rank scheme that there are minimal suitable areas for sand mining in Wisconsin. This is not surprising knowing during reclassification, I typically gave increased protection to environmentally significant features, such as excluding all wetland areas as seen in Figure 7. Through this lab it was clear data organization is key to a creating credible model. Model builder was extremely help to organize the processes I had completed, and facilitated describing the process I took to find my results. Creating the ranking tables was also critical to keep track of what final outputs layers would be combined together at the final stage in raster calculator. Without these methods of organization, it would be easy to produce an erroneous final output. This is important to realize because these model can used to make important decisions that can have negative impacts on the surrounding areas. Additionally, this includes making sure to use reliable and updated data, and to update the model created frequently due to changes such as in infrastructure and community growth.

Conclusion
The process of generating a Raster Model requires attention to detail and the ability use spatial data and apply it to a real world phenomena. This activity require me to practice data and methods organization through using model builder and excel. This also required me to use a variety of tool independently but also in series to generate a final output. Lastly, in at the end of the project, I was able to combine my data output into a final model that provide information on the topic of sand mine suitability which allowed me to practice a real world GIS application.

Exercise 7: Network Analysis

Goals and Objectives
During this lab I practiced preforming a Network Analysis, using frac sand transportation as the network. Within this exercise, I will determine routes from a mine to its nearest railroad terminal, as well as calculated the total cost each county will incur based on the amount of sand truck traffic on the local roads. This lab was broken down into two parts. In Part 1, I was required to run Pyscript to create my features for my geodatabase. In Part 2, I used these previous outputs to preform my network analysis, which I documented in model build.

*It is important to note the data values, such as the amount of trips or the costs per mile traveled, analysis within this lab are hypothetic values created by our professors. The dataset used during this is created by Esri (street map USA).

Methods
After run Pyscripter, I had a collection of output. I began by bringing theses into my map document in ArcMap. One of these was a feature class of mines, within Wisconsin, that are more than 1.5 km away from a rail terminal. Another is a feature class of rail terminals within Wisconsin that are capable of loading sand. After these classes were added, I turn on the Network Anlysis toolbar, which allowed me to begin creating a new closest facility layer. This required me to designate my terminals feature class as the facility (destination) and my mines feature class as the incident (source). From here I solved to find the closest facilities to each mine.

Next, I began using Model builder. The first item I added was the Closest Facility layer, and the Esri streets layer became the input. From here I added Add Location tool. This allowed me to specify my mines feature class as incidents. Next, I added an Add Location tool again, however this time I specified my rail terminal feature class as the facilities. From there, I added the Solve tool, which generated the closest facility routes. Next, I added the Select Data tool, which allows me to select the new closest facility routes that I just created. Next, by adding the Copy Features tool, I was able to make these selected features into a new feature class, which I named Routes.

From here I began to calculate the statistics of the model. The first goal was to calculate the total road length for the routes by county. The second goal was to calculate the costs each county should expect to have based on the hypothetic costs that every mile driven on a country road by a sand truck driver costs the county 2.2 cents. To approach these, I began with the first task. I ran the Project tool to project the output into WGS UTM Zone 15N. From there I located the WDNR county boundaries of Eau Claire, and projected  this feature class into the same coordinate system. Next, I added the Intersect tool, which overlaps the Route and Countybundaries. From here, I ran the Summary Statistic to get a sum of the Routes shape length per county, as well as how many trips were taken on each route. Now I had a table with a "Sum_Shape_Length" which represented the total road length per county, however it was in meter. To convert this to miles, I added the Add Field tool, and named this field totaldistance_meters. To calculate what would go into this new field, I added a Calculate Field tool, and added the equation [Sum_Shape_Length] * 0.00062137. This number is the conversion factor of meters to miles.

To approach the second goal, I added a Add Field tool, to create a field called cnty_cost. This field represents the total cost incurred by each county. I then added a Calculate Field tool to specify the equation used to calculate the values for this field. The equation I used here was 100 * [totaldistance_meters] * 0.022. I used this equation because each mine is visited 50 time per year, however the trucks make a return trip as well, meaning each route will be driven 100 times per year. Therefore this number would then be times by 2.2 cents to determine each counties total cost.

Figure 1: Process ran in Modelbuilder

 

Results




Figure 2: This map displays location of mines, rail terminals, and sand truck routes from mines to the close rail terminal.  Additionally, counties that contain a sand truck route are display with an estimated annual cost incurred at a rate of $0.022 per miles traveled by sand truck.



Figure 3: This graph represents the amount of money each county will incur within a year. These counties all have sand truck routes running through them.

Discussion
Referring to Figure 3, the top three counties incurring the highest costs are Chippewa County, Dunn County, and Wood County. These top three counties are represented at the darkest green counties within Figure 2. These counties, along with the other countries that experience sand mine traffic should be aware of the potential impacts associated with the increased traffic. It is possible drive time, road maintenance, traffic incidences, and noise pollution may increase within these areas. Prevention for these may need to be discussed amongst the county. Additionally, funding for these yearly costs should be discussed as well.

Conclusion
In this lab I practiced running a transportation Network Analysis. I used this tool to narrow down the Closest Facility route from mines to terminals. This output allowed me to extend to analysis to a cost analysis. To run a cost analysis, I was required to build a model in Modelbuilder. This allowed me document my process. With the outputs of this models, I created a map to display the trends in the date. Furthermore, I displayed the cost analysis in a graph. Both of these from of displaying data are helpful to determine which counties are experiencing highest maintenance costs.

Exercise 6: Geocoding Addresses

Goals and objectives
During this assignment I practiced geocoding addresses gathered by my professor from the WDNR. I was personally given 21 sand mine addresses. During this lab, I practiced the steps that are typically taking when geocoding addresses. For example, I was required to normalize my data table of addresses, geocode them using ArcMap, and assessed the error between my geocoded addresses, my classmates geocoded address, and the addresses actual location. The precise methods, and results of my lab are documented below.

Methods
I began this lab by locating a document of sand mine addresses produced my the DNR. Of these, I was designated 21 of the address. In order to geocode the addresses, I was required created a normalized excel data table (Figure 1). I created new columns for the following information: Unique Mine ID, PLSS code, Street Address, City, State, Zip Code, and any additional addresses. This process creates a standard for how the data is entered and organized.

After my table was normalized, I imported it into ArcMap. From here I turned on the geocoding tool. With the geocoding tool, I used two different methods to locate my addresses.

If an address was provided in the table, the geocoding tool located the correct location based on the address, city, state, and zip code provided. I verified these locations using the ERSI base imagery and Google maps imagery. Majority of this points were initial accurate, however a few required minor adjustments to place the address point at the entrance along the main road.

If an address was not provided in the table, I was required to locate the address point myself. When this occurred, I used the PLSS code to locate the address. The geocoding tool automatically defaulted these points to be located at the city's center, which was a highly inaccurate location. The PLSS is a four number code that signifies the Quarter Plot, Section, Township, and Region of the address, respectively. To find the location, I brought in each of these data layers from the WDNR database. The code is read from right to left, therefore I first located the Region of the code, followed by the Township, Section, and Quarter Plot. After I narrowed the address to the Quarter plot section, similar to the other method, I placed the address point at the entrance along the main road.

Once I plotted all addresses, I located my classmates' geocoded addresses and brought the shapefiles into my ArcMap document. From here I merged all of my classmates' shapefiles together. The output resulted in a feature class that contained all of their geocoded mines. From here, I ran the Near Proximity ArcTool to find the distance between my geocoded addresses and the my classmates geocoded addresses that shared the same Unique_ Mine_ID, therefore being the same mine. This output table result can be seen in Figure 2. This Near Proximity ArcTool method was repeated for the actual location of the address points, provided by the WDNR. Once again I compared my address locations with WDNR's geocoded addresses that shared the same Unique_ Mine_ID. This output table result can be seen in Figure 3.

Results

Below are my outputs and tables created throughout the lab.

Figure 1: My final normalized table including 21 sand mine addresses provided by the DNR



Figure 2: This is the Near Proximity ArcTool output, displaying the distance between my geocoded mines and my classmates' geocoded mines of the same Mine_Unique ID. This was ran to assess error between to two points, based on their distance apart.

Figure 3: This is the Near Proximity ArcTool output, displaying the distance between my geocoded mines and WDNR actual location of mines with the same Mine_Unique ID. This was ran to assess error between to two points, based on their distance apart.

Figure 4: Below is my map displaying my geocoded addresses, their actual locations, and my classmates geocoded mines. These points represent the location of 21 mines within Wisconsin.

Discussion
There are several types of errors that can occur during geocoding. According to Lo Chapter 4, there are three sources where errors may originate. These are from the original map, during data automation, or during the data processing and analysis stage. Within these, errors my begin at the earliest stage of setting your map projection and scale, or during image selection. However, the possibly of accruing errors continues during processes such as attribute input. Additionally, errors can arise while interpolation the data, during data classification, or but incorrectly rounding number values. The complete list of errors discussed by Lo can be seen below in figure 5.

Figure 5: Lo chapter 4 table that lists errors that may arise while analyzing geographic data.

Knowing there are an abundance of errors that may arise throughout the geocoding process makes it difficult to determine which of your geocoded points are actuate or not. In this lab, we practiced ways of eliminating error and assessing error while geocoding. For example, data that is normalized before it is geocoded should theoretically be more accurate than if than data that is not initially normalized because it would lacked an initial process of standardization that causes data format errors. Another way to increase a data point's accuracy is to compare the geocoded locations in ArcMap with their location through other sources such as Google maps. For example in our lab, mines that were visible in Google maps were most likely geocoded more accurately than mines that weren't visible because their address was able to be reaffirmed.  However, even using Google Maps to located the mine, it is possible the geocoded location are still inaccurate. In order to compare which points are most accurate, an error assessment can be ran. In this lab, it was used to compare distances between my personal geocoded addresses and the same address geocoded by a creditable source, such as the DNR. For example, as seen in figure 3, the Near Point Tool will show which points had the smallest distances to the DNR's location. In this case, the smaller distances to the DNR's mine suggests a smaller error margin exists, and therefore we assume these points are more accurate.

Conclusion 
This lab shows there is a great chance that error and variation will occur between the Ersi Geocoding tool users. This is due to the fact that errors mar arise through out the geocoding process, such as during data entry, placement of address point, or during error analysis. Also, it is likely the users are basing the address locations on entities that are subject to change, such as roads and entrances since within images. Setting data entry and overall geocoding standizations will help to minimize this error rate.

Exercise 5, Part 1: Data Gathering

Goals and Objectives:

The goal of this lab was to practice gathering data from different online sources. This required me to organize the data in ArcCatelog, and import the data into ArcMap to join various data together. Using PyScripter, I practiced coding to create output raster maps that all shared the same coordinate system, and practiced navigating them to the appropriate folder destination. Throughout the downloading process I collected all metadata that was provided from the sources; this is located in a table within the methods section. 

General Methods:

To complete the lab, I was required to download data from five different websites. This required me to navigate through the sources to collect both the data and the metadata if available.

1) U.S. Department of Transportation
To download U.S Raillines data, I navigated to the U.S. DOT website:
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/polyline
This source allowed me to selected the most recent National Transportation Atlas Database data (2015), where I was then able to select the Polyline feature class type. In this tab I was able download the Railway Network ZIP which stored the Roadlines feature class.

2) USGS National Map Viewer
To download the 2011 National Land Cover Database data and the National Digital Elevation data, I navigated to the USGS National Map Viewer website: http://viewer.nationalmap.gov/basic/

At this page, I was able to navigate to my study area and designate it as my area of interest. My AOI for this activity was Trempealeau County. After I selected this, I was able to download both the 2011 National Land Cover Database (NLCD) data and the Nation Elevation Data for this specific area, as well as view it's metadata.

3) USDA NASS Geospatial Data Gateway
To download the Land Cover Cropland data, I navigated to the USDA NASS Geospatial Data Gateway website: https://gdg.sc.egov.usda.gov/GDGOrder.aspx

After selecting my area of interest of Trempealeau County, WI, I navigated to the Land Use Land Cover tab, to locate the Cropland Data Layer. Selecting this provided me a link through email to download the cropland data specifically for Trempealeau County.

4) Trempealeau County Land Records
To download the Trempealeau County Geodatabase, I navigated to the Trempealeau County Data Dictionary webpage: http://www.tremplocounty.com/tchome/landrecords/data.aspx

A link of this page allowed me to directly download the entire Trempealeau County geodatabase that stored all of the features listed under the data report on the same webpage.

5) USDA NRCS Web Soil Survey
To download SSURGO soil data , I navigated to the USDA NRCS Web Soil Survey webpage: http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

This brought me to an interactive map that allowed me to select my soil survey area of interest of Trempealeau County. Once I navigated to the download soils data tab, I was able to download the data directly to my computer.

6) Python Script
Once all of the data was collected, I ran a Python Script to generate three output rasters for Trempealeau County. These rasters represent the Nation Land Cover Database data, the Digital Elevation Model, and the Cropland Data.for Trempealeau County.

Figure 1: The National Land Cover Database map, DEM map, and Cropland map of Trempealeau County, WI, created using Python Script.

Data Accuracy

The data accuracy should be evaluated based on the metadata provided by the data's distributor. As I collected the data, I collected as much metadata information for the data as well, based on the following data quality components seen in figure 2:


Figure 2: The metadata collected for the downloaded data

Conclusions:

The five different data sources represent the variability in ways data can be downloaded offline. While downloading the data for the lab, I encountered webpages that required me to select my AIO though an intereactive map, or through a dropdown tab. After selecting my study area by either of these methods, most of the websites would generate a link, allowing me to directly download the data from their website. One of these websites however sent a download link through email. The Department of Transportation did not require me to select an AOI, and therefore I was required to download an national railline feature dataset. When downloading the Trempealeau County countyboundary feature class, I was required to download the entire geodatabase. The most interesting part of this data downloading process is how it relates to metadata collection.

In addition to the methods of downloading the data, the methods to collect metadata from these sites varied drastically. This made it difficult to develop a consistent procedure of collecting and documenting this information. Unfortunately, none of the downloaded datasets had the metadata table under item description filled out with sufficient metadata and therefore had unique ways of distributing their metadata. For example, DoT include their the data's scale within the properties description, however no other information. The USGS metadata was located it two separate locations; one was an entirely different webpage and the other was a link directly beneath the download link. Although one of the pages appeared to have significant metadata documentation, this information did not assist with filling out the table of our desired information. The USDA NASS metadata was also located beside the link to download data, however the USDA SSURGO was not. They included a PDF link that provides information on "structural metadata" but I could not locate the majority of the metadata precisely for the soil survey area data. Lastly, the Trempealeau County geodatabase metadata would have to be gathered separately for each feature class. Specific feature class metadata was provided on their website however it did not include the majority of information I was hoping to collect.

Working through these issues, it is clear that metadata is difficult to collect, which can lead to accurate problems when working with the data. It is important to know the accuracy of the data you are working with, along with the resolution, minimal mapping unit, and the temporal accuracy. If you are unable to find one of these data characteristics and wish to use the data for analytical purposes, it is critical to locate it either by continuing to navigate the webpage or contacting the distributor directly.

Citations:

Data was collected at the following websites:

US Department of Transportation (2015). National Transportation Atlas Database data [Polyline Feature Class]. Retrieved on 10/19/2015. Retrieved from
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/polyline

USGS National Map Viewer (2011). National Land Cover data and National Digital Elevation data [Trempealeau County Area]. Retrieved on 10/18/2015. Retrieved from http://viewer.nationalmap.gov/basic/

USDA NASS. Land Use Land Cover database [Cropland Data Layer]. Retrieved on 10/19/2015. Retrieved from https://gdg.sc.egov.usda.gov/GDGOrder.aspx

Trempealeau County. Trempealeau County Geodatabase. Retrieved on 10/19/2015. Retrieved from
http://www.tremplocounty.com/tchome/landrecords/data.aspx

USDA Web Soil Service. SSURGO Soil Data [Trempealeau County]. Retrieved on 10/19/2015. Retrieved from http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

Exercise 4: Overview of Sand Mining in Western Wisconsin Overview

What is Sand Frac Mining?
Within the US, sand has been mined for many of years to support industries such as glass and petroleum. According to the Wisconsin Geological and Natural History Survey, sand mining in the form of frac sand mining has become a popular mining method. The process of fracking, or hydraulic fracturing, has lead to advances in oil and gas extraction techniques. The abundant sand resources within Wisconsin have lead to a high demand for frac sand mining within various areas of the state.

Location in Wisconsin
According to the Wisconsin Department of Natural Resources, sand mining in Wisconsin occurs within the west-central portion of the state. The frac sand and sand mine distribution within Wisconsin can be seen in a Figure 1 below, created by the Wisconsin Geological and Natural History Survey.



Figure 1: Map of frac sand distribution and frac sand mines within WI. (2007). Wisconsin Geological and Natural History Survey.

Issues With Sand Frac Mining in Western Wisconsin
Furthermore, according to the DNR, sand frac mining can impact environmental quality in several ways. For example, it can have negative impacts on air quality, land quality, and water resources. Issues associated with air quality are due to the wind dispersal of crystalline silica dust generated while sand fracing. Dust inhalation is of concern as well as asesthetic impacts on property. The mines can also affect water resources if the stockpile comes in contact with ground water, streams, rivers, wetlands, or stormwater systems. There are also concerns with the impacts on managed lands, in areas where nature based outdoor activities typically occur. Aside from silica dust pollution, noise and light pollution can be a problem as well, affecting near by residents and wildlife. Along with environmental impacts, the frequent transportation of the heavy sand loads to their ultimate dump site is expected to have impacts on road conditions.

Future GIS Work in this course related with Sand Frac Mining
Later this semester we will use GIS analysis tools in relation to sand mining in Wisconsin. For example, we will use ArcGIS software to calculate the best possible sand mine routes by determining statistics such as which facilities are the best for the sand mine drivers to bring their loads. We will also be geocoding mines within Wisconsin based on their addresses.

Citations
Figure 1: Map of frac sand distribution and frac sand mines within WI . (2007). Wisconsin Geological and Natural History Survey. Retrieved from http://wcwrpc.org/frac-sand-factsheet.pdf

Wisconsin Geological and Natural History Survey (2012). Frac sand in Wisconsin. Retrieved on 10/20/2015. Retrieved from http://wcwrpc.org/frac-sand-factsheet.pdf

Wisconsin Department of Natural Resources (2012, January). Silica Sand Mining in Wisconsin. Retrieved on 10/20/2015. Retrieved from http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Star Tribune (2014, October). Wisconsin Country Shuts Down Frac Sand Operation Running Wild. Retrieved on 10/20/2015. Retrieved from http://www.startribune.com/wisconsin-county-shuts-down-frac-sand-operation-running-wild/278463561/