Exercise 5: Data Downloading, Interoperability, and Working with Projections in Python

 

 

Overview

     Downloading data from different sources is a necessary skill to have when it comes to geospatial analysis. In this exercise we will be downloading data from several different sources, reprojecting the data to reduce distortion, cropping the data the Trempealeau County, WI, and importing it into a geodatabase using the Python scripting program.

 

Data Collection

     Data was collected from a variety of sources. We downloaded railroad data from the United States Department of Transportation using their National Transportation Atlas Database. Next, the National Elevation Data and Landcover data was downloaded using the National Map Viewer. Then, Cropland data was downloaded from the United States Department of Agriculture National Resources Conservation Service geospatial data gateway. Next, we downloaded the Trempealeau County geodatabase from the Trempealeau County Land Records Department. Finally, we used the USDA NRCS to download the Web Soil Survey for Trempealeau County, WI.

 

Geoprocessing

     Now that all the data is downloaded and unzipped into the appropriate folder we use Python to project each of the data sets into a projection that will reduce distortion for Trempealeau County, extract the data that is only found within the boundary of Trempealeau County, and import the data into the Trempealeau County geodatabase. Below are maps showing a reference map of the location of Trempealeau County within Wisconsin (Figure 1), the landcover of Trempealeau County (Figure 2), the digital elevation of Trempealeau County (Figure 3), the cropland of Trempealeau County (Figure 4), and the soil drainage index of Trempealeau County (Figure 5).

 

Figure 1. Reference map showing the location of Trempealeau County in Wisconsin.

Figure 2. Trempealeau County crop land

 

 

Figure 3. Trempealeau County Digital Elevation

Figure 4. Trempealeau County Land Cover.



Figure 5. Trempealeau County soil drainage index.

 

 

 

Data Quality

    

 

 

     As you can see from the table above, the metadata for the data we downloaded earlier is very incomplete. The majority of data does not include any minimum mapping units, planometric accuracy, or attribute accuracy. This makes it difficult to know for sure whether data is good enough to be used for a project. Therefore, there is always going to be some sources of uncertainty and error in any data that is downloaded from the internet, even when it comes from reliable sources like the USGS or USDA. 

 

 

 Sources

 

Trempealeau County Land Records

http://www.tremplocounty.com/landrecords/

US Department of Transportation

http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html 

USDA Geospatial Data Gateway

http://datagateway.nrcs.usda.gov/GDGOrder.aspx 

USDA Web Soil Survey

http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

USGS National Map Viewer

http://viewer.nationalmap.gov/viewer/ 

 

 

 

 

Python Script

 Python is a coding program that allows us to do geoprocessing outside of the ArcMap program. Below is the Python script that was used to reproject land cover, elevation, and cropland rasters to a projection that minimizes distortion for Trempealeau County. Next, the script extracts the 3 rasters by the Trempealeau County boundary. Finally, the script imports the 3 rasters into the Trempealeau County geodatabase.

 

 

The goal of the script below is to select mines that will be using roads to transport sand. To do this we select all of the entries that are active. Out of all the active entries we select all of those that contain the word "mine" in the facility type field. Then we remove selections that contain the word "Rail" in the facility type field. Finally, we remove selections that are within 1.5km of any railroad assuming that these mines will have rail spurs built to the railroad, and therefore will not use roads. This final product is all the active mines that will transport sand on roads.

 

 

 

 

 

     The final python script is designed to create a risk model with weighted variables. In this scenerio I decided that distance from populated areas was the most important risk factor. Therefore, I gave the risk associated with populated areas a a weight of 1.5. This weight gives distance from populated areas a large influence on the overall suitability model. After the weighted raster was created I added up all the risk variables to create a risk model (Figure 1).

 

 

 

 

 

 

 

Figure 1 Weighted risk model based created using Python.

 

 

Overview of Sand Mining in Western WI

 

 

Overview of Sand Mining

 

Introduction

          Frac sand mining is the process of excavating large quantities of sand which will be used in the extraction of natural gas and oil from rock formations. As the technology associated with hydrofracking has increased the need for frac sands has also increased significantly. Therefore, we will look at the requirements sand needs to fall in to be considered frac sands, where frac sand mining is occurring in Wisconsin, the processes associated with a typical frac sand mining operation, common issues that are associated with frac sand mining, and how GIS can be used to explore some of these issues.

 

 

Sand Mine Locations

          Sand must meet certain requirements for use in hydrofracking operations. First off, the sand must be pure silicon dioxide (SiO2), very well rounded, have a compressive strength between 6,000 psi and 14,000 psi, and must fall within a certain size range (Wisconsin Geological and Natural History Survey, 2012). Sand with these qualities is commonly found in Cambrian and Ordovician sandstones that were poorly cemented, such as the Mt Simon Formation and the St. Peter Formation located in Western Wisconsin (Wisconsin Department of Natural Resources, 2012). Estimates from the Wisconsin DNR show that Wisconsin has approximately 63 active mining operations, 45 processing plants (Wisconsin Department of Natural Resources, 2014), and an average of 12 million tons of sand are being excavated yearly (Wisconsin Department of Natural Resources, 2012).  Figure 1 shows the extend of sandstone formations in Wisconsin and the location of existing frac sand mines and processing plants as of December 2011.

 



Figure 1. Sandstone formations in Wisconsin and the location of frac sand mines as of December 2011 (Wisconsin Geological and Natural History Survey, 2012).



         

 

Sand Mine Operations

          Next, we will discuss the process that occurs at a typical sand mine location. First, any unnecessary topsoil is removed from the sand formations and piled along the outskirts of the operation. Next, the actual sand itself is excavated from the site, followed by blasting any tightly cemented sandstone. After any tightly cemented sandstone is blasted into large chunks it needs to be crushed into smaller particles that fit the size requirement. The sand is then sent through a processing plant which washes any fine grained particles off the sand, sorts the grains based on size and shape, and dries the sand. The sand is then transported via large trucks to rail spurs where it will be shipped to its final destination or other facilities for further processing. The final step in the mining process is reclamation of the land (Wisconsin Department of Natural Resources, 2012).





Impacts of Sand Mining

          As with any mining operation there are going to be environmental impacts. Internal combustion engines in heavy machinery and fine grained sand particulates will cause an increase in air pollution, although not significant (Syverson, 2012). A large amount of water is needed to wash the material, which will lower the groundwater table and could have potential impacts on wetlands or other surface water. Water being used to wash the sand will also dissolve certain minerals that will penetrate into the groundwater supply causing contamination. It is also important to look at the socio-economic impacts of sand mining. Heavy machinery will degrade roads and property value surrounding sand mines will decrease dramatically. There are also a lot of economic benefits associated with sand mining. Sand mining creates a lot jobs for electricians, engineers, truck drivers,

accountants, and welders. These jobs will have a large economic benefit associated with them (Syverson, 2012).

Benefits of Using GIS

          GIS is a very important tool in frac sand mining. We are able to analyze areas that would be good locations for sand mines based on several factors including slope, water table height, total amount of sand available, amount of topsoil that will need to be removed, etc. We can also analyze transportation routes that will provide the most effective way to transport sand to areas that need it.

Sources

Syverson, K. (2012, January 15). Kent M. Syverson: Benefits of sand mining outweigh any negatives. LaCrosse Tribune. Retrieved October 1, 2014, from http://lacrossetribune.com/news/opinion/kent-m-syverson-benefits-of-sand-mining-outweigh-any-negatives/article_a7607fc6-3e36-11e1-9401-001871e3ce6c.html

Wisconsin Department of Natural Resources. (2012, January). Silica sand mining in Wisconsin.         Retrieved from http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

 

Wisconsin Department of Natural Resources. (2014, July). Locations of industrial sand mines and processing plants in Wisconsin. Retrieved from http://dnr.wi.gov/topic/Mines/ISMMap.html

 

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