Data Interoperability, Data Sources and a continuation of Sand Mining Suitable in Western Wisconsin

 

Goals and Objectives: For this lab the objective was to become familiar with process of downloading data from different sources online. Then, once downloaded, understanding the organization, structure, and methods that must be undertaken to use the data in ArcGIS. These processes include importing, joining and projecting various data sets such that they all can be aggregated into a data set which can be displayed and analyzed. In the continued project of understanding sand mining impacts on Western Wisconsin we are going to particularly look at Trempealeau County, which again, has multiple frac sand mining sites, as well as a transportation infrastructure for transporting the sand.

 

General Methods: In order to begin to analyze impacts of sand mining, various data will be needed to realizes mining and transportations impact on the county. For the first set of data, we had to download the National Rail Network Data Set from the U.S. Department of Transportation. Next we had to download data for land cover from the U.S. Geological Survey's National Land Cover database.  From the USGS we took three sets of files;  a Land Cover Dataset, a Percent Developed Imperviousness data set and a 1/3 arc-second DEM data set for Trempealeau County. Next we downloaded data from the U.S. Department of Agriculture Geospatial Data Gateway, for cropland data in Trempealeau County. Next we downloaded data from the U.S. Department of Agriculture National Resource Conservation Service for SSURGO data, which is a digital soil survey developed by the USDA. The SSURGO data was in the form of an access database which had to be altered and aggregated so it could be useable. And Finally we downloaded data from the Trempealeau County Land Records Department for a geodatabse of the county.

 

Getting all of this data in a useable form was actually quite a challenging process. which involved merging tifs, clipping data, mosaicing rasters, and a in depth knowledge of the values and abilities of both the datasets and ArcGIS in order to keep the data intact and usable throughout the process of readying the data.

Sources of data used in this analysis:

 

• U.S. Department of Transportation http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

• U.S. Geological Survey
• http://nationalmap.gov/about.html

• U.S. Department of Agriculture Geospatial Data Gateway
• https://gdg.sc.egov.usda.gov/

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

• U.S. Department of Agriculture National Resource Conservation Service
• http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

 

Data Accuracy: For each of the different data sets that I have downloaded the following data measures were reported:

Data Source	Dataset	Scale	Effective Resolution	Minimum Mapping Unit	Planimetric Coordinate Accuracy	Lineage	Temporal Accuracy	Attribute Accuracy
U.S. Department of Transportation	National Transportation Atlas Database (2015)	1:24,000 to 1:100,000 scale			None	None	"Continually Updated"
U.S Geological Survey	National Land Cover Database (2011)	None	None	None	None	None
U.S Geological Survey	1/3 arc-second DEM	None	One arc second (~30 meters)	None			20
U.S. Department of Agriculture	Cropland Data layer by state	~1:100000	30 meters by 30 meters			None	Data collected between 03/01/1997 and 09/14/2006
Trempealeau County Land Records	Trempealeau County file geodatabase	None	None	None			"updated on regular basis"
U.S. Department of Agriculture Natural Resource Conservation Service	NRCS Web Soil Survey	0.736111111	None	None			9/17/2015

Fig 1. Table of Data Accuracy. Squares left blank indicate no information was found.



Fig 2. Map of Wisconsin with Specific not of Trempealeau County



Fig 3. Trempealeau County Land Cover

Fig 4. Trempealeau County Cropland. Note* Cropland with minimal values (10 counts or less) were not included



Fig 5. Trempealeau County Drainage risk index and Rail Lines.

Fig 6. The Elevation of Trempealeau County.





Fig 7. Trempealeau County Soil Drainage Risk Index. Very high soil run off risk is indicated by the darker



Conclusions: As you can see from the data table, most of the GIS data that was downloaded was missing key metadata that should be a standard in the GIS industry. I find the lack of accuracy or precession reported for any attribute data particularly to be odd, if the data is to be published in the purpose for analysis, that information is key for analysis by the scientific method. With out that information we don't know how much error or variability is in the models we are building, and that is a very troubling thing for finding answers to questions.

Looking at all the maps that were created, we see that the Frac sand mines are near developed areas, on crop or near cropland, on higher elevations that have a higher risk of soil runoff. All of these factors individual may or may not have drastic impacts on the surrounding area in the grand scheme of things, but it may not have actually occurred to anyone that these factors combined may have magnify potential negative effects of frac sand mining.

Python Scripts

Post # 2 (4/12/2016)


For the second Python Code we had to write we were attempting to automate a series of tools that would not only select fields based on SQL Query Statements, but create feature classes of those narrowed layers to select Frac Sand mines in Wisconsin that were not close to a rail network. This will be important because Frac Sand mines that are not close to rail networks will rely on roads to transport the sand to rail network locations, impacting the roadways and local infrastructure.

Python Script for selecting mine locations based on the mines proximity to rail networks in the state of Wisconsin.