Data Downloading, Interoperability, and Working with Projections

Goal/Objectives

This lab was the one referenced in my first blog post that was, until recently, unaccessable due to the government shutdown. This was supposed to be the first part of a multi-leveled lab course aimed at the sustainability of Frac Sand mines in Tremealeau County, WI. This  lab is now part three in the installment. The purpose of this lab was to learn how to download data from online government sources, making these data collections interoperable and to fix them with popper projections. Several different government web sources were utilized in the creation of this lab. They were as follows: National Atlas, USGS National Map Viewer, Web Soil Survey, and the USDA.

General Methods

The general methods that were utilized here consisted of many different services. While downloading data from the online resources I had to use the web explorer device to unzip and save the data into my folder in our classes folder. Some of the sources allowed data transfer straight from their sites to my destination, while others offered an email containing the data. After all of the data was downloaded a personal geodatabase was created to store all of the tables and shapefiles. I assigned the NAD_1983(2011)_Wisconsin_TM in order to get the best picture of the whole state. I didn't only have information covering Trempealeau county so I had to use a common coordinate system for the whole state that would accurately portray all of the data. A feature dataset was also created to help in creating a relationship between the soils data and the component table. The soil data went under a process of joining both component data and a layer that contained a legend as well. Importing of a SSURGO geodatabase into access was also done to give the table a reference. Feature classes, tables and rasters were all imported into my ArcMap in order to create my final product. As all of the data was added to my database it was projected on the fly using my above coordinate system.

Results

The result of this project was several maps that were projected into the same coordinate system to best reflect the data in the Wisconsin region. The below data in Figure 2 shows the all of the data that I download: railroads, DEM, soils, NLCD, and NASS cropland. Figure 1 shows all of the data quality/metadata from the data that was downloaded as well.

Figure 1:

 

Figure 2:

Conclusion

So far in our investigation we have created the tools to have a strong database for analyzing the spatial connections of Wisconsin, it's mines, and the effects that they are having on people and the environment. We now have several databases full of data on the physical features of Wisconsin, along with knowledge of what frac sand mines are. It will be interesting to see how our investigation continues to progress as we compile and analyze data more acutely.

References
Lo, Chor Pang and Albert K.W. Yeung. "Concepts and Techniques of Geographic Information Systems." (2006).

Geocoding Frac Sand Mines in Wisconsin

Goal/Objective

This lab is an extension of the previous post and aims to show frac sand mining in Western Wisconsin through another lens. The goal of this lab was to download Trempealeau County data from the county website. The final result of this lab is a geocoded map created in ArcMap and an excel spreadsheet showing the distances of my points compared to other geocoded mines with the same address as mine.

1. Download Trempealeau County Land records data.
2. Download the updated list of mines from Wisconsin Watch.
3. Connect to the geocoding services provided by ESRI.
4. Geocode the downloaded mines to the street addresses that they area associated with.
5. Geocode the mines with the PLSS system manually.
6. Compare our results with our colleagues from class.

Methods

The initial part of this lab was to download Trempealeau County data from their geodatabase. The data was then utilized in our lab to concentrate on our region compared to the state. Trempealeau county has one of the highest concentrations of mines in Western Wisconsin and the data that was collected showed this. The next method used in this lab was to download data from our teacher's file which contained updated mines in Wisconsin. Each students in our course was assigned a unique number that corressponded with several mines. Then the mines were normalized in the excel file to make it plausible for them to be geocoded. The table was then put into ArcMap in a dbf file. Some of the addresses of the mines didn't match up correctly so you could either manually change the addresses or go with the suggested address of Arc. Once the geocoding was finished the class submitted their geocoded data in the form of a shapefile to the course folder. All of the data from the shapefiles were merged using ArcToolbox's merge tool. After geoprocessessing the mines were querried to find the mines with the same UNIQUE_ID as the mines that each individual originally geocoded. The querried mines were then created into their own feature. Finally, the point distance tool was used to find out how far our points were from our colleagues same mine points.

Results

In the figures below, you can see the excel files that were normalized, excel files with the finished product of distances and a map of the mines in Wisconsin. In Figure 1, the addresses were normalized to make an easier geocoding experience. The data given to us gave us some addresses or  hints to addresses but were not in a form that Arc could geocode. Figure 2, shows the distances and how geocoding and manually normalizing can create differences in points that have almost the same data. Finally, figure 3 is a map of the mines that I did and the ones that were the same as mine.

Figure 1:

Figure 2:

The distances above are in kilometers. The distances correspond to the space between my points and my colleagues.

Figure 3:

Discussion

Looking at Figure 3, it is very apparent of how manually normalizing data between individuals can create a distance divide in geocoded data. Because of the manual placing of addresses to mine sites when geocoding the ability to get perfectly correlated points between colleagues is nearly impossible. In the future I now have the ability to know more effective ways of normalizing data, especially when working with others. This lab also gave me the knowledge of how to work with others and finding a best way to normalize data where it will work with everyone's data. Both inherent and operational errors occur commonly according to Lo. Inherent errors are those in which occur during the creation of your data, while operational errors are those in which are created during work on your data. Because of these errors your data may not be fully correct and should be analyzed very closely. One example of an operational error is when I placed my mine inside of a certain county while my colleagues placed them elsewhere. Inherent errors would occur in my data if I didn't project my data in right coordinate system.

Conclusion

In our investigation of frac sand mines in Wisconsin we have so far learned about why frac sand is used, how to download data from government sites, and how to geocode addresses. Other skills that I have acquired from our labs include the ability to normalize data, how to navigate dbf files, how to convert dbf files into exel files, utilizing useful online source, comparing data to images, etc. As we continue discovering more about the sustainability of frac sand mines there will be more extensive data added to this blog.

Thank you for reading.

References:
Lo, Chor Pang and Albert K.W. Yeung. "Concepts and Techniques of Geographic Information Systems." 2 (2006): 108

Overview of Frac Sand Mining in Wisconsin

Overview of Frac Sand Mining in Wisconsin

Introduction

My Geographic Information Systems (GIS) 2 course will be examining frac sand mining in Wisconsin. We will be looking at Trempealeau county extensively throughout the semester. GIS will be used to model various components of the mining in Trempealeau country through mapping and reports. This post will cover an overview of what frac sand mining is, where it is in Wisconsin, and the issues associated with it.

What is frac sand mining?

Frac Sand mining has recently been a very heated topic in Wisconsin due to the state's reservoirs of fine grained quartz. While sand mining in Wisconsin has persisted for hundreds of years the industry has recently seen an explosion of mining proposals. Wisconsin sands are of high quality and fine grain, which in turn is the leading cause of the increase in permit requests. Once the sand is mined it is shipped out of state to be used by oil companies in Texas, North Dakota and Montana. The process that Oil fields use to pull the fuel from the earth's crust is called hydrofracking. Hydrofracking, hydraulic fracturing, is the process of drilling into the earth's surface, using explosives to break apart the rock and then surging water mixed with the frac sand which helps to hold the fractures open. The busting of the rock layers release natural gasses and liquids which are retrieved for commercial value.

Where is it in Wisconsin?

Western Wisconsin has the largest reservoirs of the silica sand that is needed for the hydrofracking process. Eau Claire, Chippewa, Trempealeau, and Monroe see a heavy amount of mining. Sand stone formations span across western and central Wisconsin where the concentration of mine sites are located. As stated at the beginning of this blog Trempealeau county is our studies focus and is also one of the best examples of a western Wisconsin county who is highly affects by sand mining. Trempealeau county, as seen in the figure below, has a very high concentration of both mines and processing plants. Sand from glacial and river beach deposits are generally too angular to be used in the hydrofracking process, so Wisconsin's sandstone mines are the best place to get pure sand. However not all sands are usable for hydrofracking. Natural resource companies look for a certain grain of sand to use.

Issues associated with Frac sand mining

In recent news the frac sand mines have been seen as more of an issue that in its several hundred year history. Regulators, local and state governments and the general public have all generated interest in this topic for their own reasons. The public's greatest concern appears to be the encroachment of mines into residential areas. Regulators want to create laws and guidelines for mining companies so as to limit the amount of sand taken. Sand mines fall under the jurisdiction of keeping water safe for drinking and also to keep hazardous materials out. Even though these laws are in place these issues still persist. The removal of overburden, which is the top of hills that covers the sandstone, has also met the public's eye because it is destroying the hills of western Wisconsin.

Using GIS to help observe some of these issues

Our course is designed for the enhancing of GIS knowledge, but our labs will also give us skills and knowledge of very important environmental issues in our region. We will also obtain the ability to download and use data from outside resources. We will be using ArcGIS to help in our investigation of Trempealeau counties frac sand mines. Lab 1 will be posted shortly, however because of the government shutdown my data download has been greatly hindered. I will post again as soon as possible!

Thank you for your attention!

http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf
http://dnr.wi.gov/topic/mines/silica.html
http://dnr.wi.gov/topic/Mines/Deposit.html
http://wisconsingeologicalsurvey.org/pdfs/frac-sand-factsheet.pdf