Post 2: Data Gathering

Goals and Objectives
The goal of this exercise was to learn how to download data from a variety of publicly accessible websites and manipulate the data into a form that was functional for our purposes. To do this, we needed to import the downloaded data into ArcGIS and join where appropriate. Finally, all data had to be compiled into a single geodatabase with the same projection.  The processed data will serve as the base data for our ongoing Trempealeau County frac sand project.

General Methods
Four different online sources were used to collect railroad, land cover, elevation, cropland, and soil data. These included the US National Atlas, USGS National Map Viewer, USDA Geospatial Data Gateway, and USDA NRSC Web Soil Survey (SSURGO) (Fig.1).

Figure 1. The data was downloaded from a variety of online sources.

To receive the data, various requests and form submissions were required at each site, which generally resulted in the download of a zip file.  The files had to be unzipped and desired files located.  This was not always straightforward. For example, the elevation data provided by the USGS National Map Viewer was delivered in multiple files due to the size of the data.  As we wanted a single elevation raster, we had to join the two DEMs together using the mosaic tool.  This step required we use the appropriate bit depth, so as not to lose any data in the process. Also, the SSURGO soil shapefile we wanted had to be imported into a feature class in our geodatabase. We then joined the soil information from the "component" table to the feature class based on a relationship class we created. Additionally, we joined drainage index and productivity index tables to the SSURGO data, using DI index as the symbology for the feature.

The data quality varied for each source and data set.  To ensure proper use of the data later in our project, data quality information was compiled from the metadata of each data set (Fig. 2)

Figure 2.  The data sets vary in quality, which will be important to be aware of in later aspects of the project.

Results
With all desired data collected, the final step was to project all the features in the same coordinate system. As our study focuses on Trempealeau County, I used "NAD 1983 UTM Zone 15N" as my projected coordinate system. To do this, I used the "project raster" tool for the land cover, elevation, and cropland features and the "project" tool for the railroad and soils data (Fig. 3). All the data sets appear to have been processed correctly.  They should work well as the base data for later project analysis.

Figure 3.  Layers are shown individually, as well as together (upper left).  

Sources
Concepts and Techniques in Geographic Information Systems
CP Lo, AKW Yeung - 2003 - Pearson Prentice Hall

Post 3: Geocoding

Goals and Objectives
For this portion of the project, our goal was to download frac sand facility location data from a county website (www.tremplocounty.com), as well as a statewide website (WisconsinWatch.org), which was in the form of an Excel file.  We then had to normalize the data and geocode the assigned locations. After all the results were geocoded, matching locations that were geocoded by different users were compared to demonstrate error that can occur in the geocoding process.

Methods
Geocoding involves assigning coordinates or an address for a specific data point.  We attempted to geocode the locations of all known frac sand facilities (proposed and functioning) in Wisconsin based off data provided by WisconsinWatch.org.  The data were in the form of an Excel table with various types of addresses given.  In order to allow the geocoder to run the given addresses, we first had to normalize the table.  We then ran the program to see which locations could be matched and which locations would require further investigation and manual placement or address format changes to be located properly.

For addresses that had a full street address and could be matched, we simply selected the appropriate location and an address was automatically assigned to the point. However, this only worked on 4 of the 14 points, so alternative methods were required. For addresses that were given in the form of a generic or PLSS description, I used Google Earth and a PLSS coordinate converter to attempt to determine a street address, which could be matched in the Geocoder.  This worked for three of my data points. For the remaining seven locations, I used the "pick the address from the map" feature in the Geocoder to manually place the point.  I then manually updated the address information in the table as well.

Results
The initial Excel file downloaded from WisconsinWatch.org was very cluttered with confusing and unnecessary information (Fig. 1).

Figure 1.  The original table had many inconsistencies and additional information that required normalization.

In order to make the data usable, we normalized the table.  This entailed making sure all attributes were functionally dependent on the unique primary key (UNIQUE ID) and minimizing the storage of data to ensure data integrity.  To do this, we separated the address into separate fields of facility address, city, zip code, and state categories (Fig.2 ). We then eliminated all fields except for "UNIQUE ID," "Facility Type," "Facility Address," "City," "Community (City)," "Zip Code," and "State." This removed the excess fields and and made all categories directly dependent on the "UNIQUE ID" field. Finally, we made sure that all facility addresses were in the same format.

Figure 2.  After normalizing the data, information is much more clear and usable.

After normalizing the data and filling in any informational gaps using Google Earth and the mining company websites, the frac sand facility locations were all geocoded properly using both geocoder matching and manual placement (Fig. 3).

Figure 3.  After normalization of the data and filling of informational gaps, all 14 frac sand facilities were properly geocoded.

Geocoding results varied significantly depending on the user.  Errors occurred at all stages of the geocoding process and were both inherent and operational in nature.  For example, our original source data were inherently flawed as the setup for providing the information was insufficient for collecting all the necessary address information.  In addition, there was operational error during our data compilation stage.  There were clearly errors made during attribute data input, as some of the addresses placed the facilities in communities that were incorrect or only near the correct city.  Also, each user digitized the locations differently.  This likely resulted from different user interpretation of Google Earth images regarding frac sand facility locations. The differences varied from a few a hundred to over twenty thousand meters (Fig. 4).

Figure 4.  Insufficient source data and differences in digitization resulted in significant variation in users' geocoding                         results. These two points, which are supposed to be the same location, were generated by two different users and are more than 2700 meters apart.

To assess how significantly these sources of error affected the final data, we used the point distance tool. This provided the distance to the point nearest my designated location for a given facility (Fig. 5).  Of the five facility locations I compared, only one matched exactly.  However, this point was matched directly by the Geocoder. The other four points that I compared, which were placed manually, varied from a separation of ~400 meters to ~25,000 meters.  Unfortunately, this data processing tool has inherent error, as it does not find points based on matching primary keys, but uses proximity alone.

Figure 5.  The point distance tool was used to assess the impact of inherent and operational error on the final data by                       measuring the distance from a specified facility location (INPUT_FID) to the nearest point generated by another user (NEAR_FID).

Conclusion

Though the original data table was quite unclear and lacking in information, we were able to generate adequate geocoding results by using normalization and some help from Google Earth and additional research. However, the final product was imperfect, containing significant inherent and operational error. This problem is not uncommon.  In order to verify complete accuracy of location placement, each point should be compared to reference data with a higher degree of accuracy.  If no such data is available for the frac sand facility locations, coordinates should be manually collected at each site and then compared to reference data points in the area, ensuring the accuracy of each location.

Post 1: Overview

Frac Sand and Hydraulic Fracturing

Discussion of the material known as “frac sand” has become increasingly common in recent years, especially in Wisconsin. This leads to the question, "What exactly is frac sand, and why is it so significant?" Frac sand is used for oil and natural gas extraction in a process known as hydraulic fracturing. Hydraulic fracturing involves pumping a slurry of water, chemicals, and sand under high pressure, deep into oil and natural gas wells in order to expand naturally-occurring or explosive-induced fractures within shale formations. After the pressure is released, the frac sand grains remain behind, acting as pillars that keep the fractures propped open and permit oil and gas to flow more freely from the formation. This technology has been known for the last 75 years.  However, its potential has only recently been realized with advancements in horizontal drilling.  Combining the two techniques (Fig. 1) has drastically increased oil and gas extraction abilities and allowed formations that were previously too expensive to drill to become profitable (WDNR, 2012).



Figure 1.  An illustration of combining hydraulic fracturing and horizontal drilling to enlarge fractures in shale formations to more easily remove oil and natural gas (WDNR, 2012).

The hydraulic fracturing process requires a very specific type of sand.  Effective frac sand must be almost pure quartz, so it is strong enough to hold the weight of the overlying material. Additionally, the sand grains must be well rounded, uniform, and sufficiently coarse to create openings in the fractures that are large enough for the oil and natural gas to flow through (WDNR, 2013). Sand meeting these specifications is only formed in specific geologic environments. Wisconsin has multiple sandstone formations that meet the requirements for frac sand and are close enough to the surface to be mined (WDNR, 2012).

Frac Sand Mining in Wisconsin

Mining of frac sand in Wisconsin has primarily been focused in the west-central part of the state (Fig. 2).  In this region, the Jordan, Wonewoc, Mt. Simon, and St. Peter formations all contain sand with the desired traits and are accessible from hilltops and hillsides (WDNR, 2012). Because of the recent surge in demand for frac sand, both nationally and internationally, and Wisconsin’s abundant supply, frac sand mining has become a major contributor to the state’s economy.  There are over 100 frac sand operations active in Wisconsin. These mines and processing facilities have resulted in the creation of over 2,000 jobs, which generate millions of dollars in income and tax revenue. Communities across the state have benefitted from the money brought in by the frac sand industry. Future prospects show no signs of slowing down, but not all Wisconsin residents are excited about this reality (Nichols, 2013).



Figure 2.  Locations of sandstone formations, frac sand mines, and frac sand processing plants across Wisconsin (WDNR, 2013).

Concerns with Frac Sand Mining

Frac sand mining in Wisconsin comes with certain disadvantages and risks.  Many citizens are concerned about mining away the hills that serve as aesthetic icons of the region and how that could affect erosional patterns. Additionally, explosives are used to remove sand for processing, where it is crushed and sorted.  These processes generate significant dust, which can lead to respiratory issues, particularly if the dust is silica-rich.  The most common concern is silicosis, an irreparable respiratory disease caused by the inhalation of fine silica particles. In an effort to control dust, most frac sand operations use a notable amount of water to spray down the plant.  Add this to water already being used to wash the mined material, and the water toll on a region can be considerable, even if the plant is using a closed-loop system. Beyond this, chemicals are used throughout the mining process as flocculants.  If mishandled, these chemicals have the potential to contaminate local groundwater resources. Furthermore, operating such large facilities can generate substantial air emissions. If not properly filtered, this can reduce local air quality, as well as release greenhouse gases. In addition to health and safety issues, frac sand facilities can be inconvenient for facility neighbors in other ways (WDNR, 2012).

Removal, processing, and transportation of frac sand creates ongoing inconveniences typically not experienced in many rural communities.  Facility, equipment, and train operations often generate ongoing noise and light that can last throughout the night.  Also, frac sand is commonly transported in large dump trucks on roads that previously saw very little traffic (WDNR, 2012).  This is a change locals have to adjust to and has the potential to generate a higher number of serious accidents in the area.  Because of these issues, along with the other health concerns discussed, many locals fear the value of their homes will decrease drastically if a frac sand plant is opened nearby (Nichols, 2013).

In-Class Risk Assessment

Clearly, frac sand mining is an important issue in Wisconsin.  It has high-risk potential, but high-reward potential as well.  Because of this, it is important to build a thorough suitability/risk model for frac sand mining in Western Wisconsin.  This will be the focus of our class project, with a particular emphasis on Trempealeau County. To complete this task, we will download data from a variety of sources, such as the National Atlas, the USGS, and the USDA. Using these resources and a variety of data analysis methods, we will hopefully be able to generate a useful, accurate model.

Sources

Nichols, M. (2013, April). Mining success. Wisconsin Interest, 22(1), Retrieved from
            http://www.wpri.org/WIInterest/Vol22No1/Nichols22.1.html

WDNR. (2012, JANUARY). Silica sand mining in wisconsin. Retrieved from
            http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf 

WDNR. (2013). Frac sand in wisconsin: Fact sheet 05. Retrieved from  

            http://wisconsingeologicalsurvey.org/pdfs/frac-sand-factsheet.pdf