Data Gathering

Introduction:
The goal of this lab was to gather a variety of data on Trempealeau County from different sources to develop file management and data acquisition skills.  This data was downloaded from five different sources then compiled into a database that could be used for further analysis.  Pyscripter was used to project, clip, and load rasters into the geodatabase.

Methods:
-Railway data was collected from the US Department of Transportations website.
https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

-Landcover/Landuse and elevation data was collected from the USGS National Map Viewer.
http://nationalmap.gov/about.html

-Landcover Crop Land data was collected from the USDA Geospatial Data Gateway.
http://datagateway.nrcs.usda.gov/

-A Trempealeau County Geodatabase was collected from the Trempealeau County Land Records.
http://www.tremplocounty.com/landrecords/

-Soil data was collected from the USDA NRCS Web Soil Survey.
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Data Accuracy:
Each data set comes with meta data that provides information about the data.  This information includes:
Scale-

Effective Resolution- Resolution or scale that the data was taken in.  This has to do with camera height and lens focal length.

Minimum Mapping Unit- Smallest depictable or plotable object on a map.

Planimetric Coordinate Accuracy- How close the objects in the data set are to the real world position.

Lineage- Documentation of source materials, whats been done to the data by who and who collected it.

Temporal Accuracy- How relevant the data is today.

Attribute Accuracy- Closeness of descriptive data to real world.  Magnitude of gross error.

The table below shows how each of the data sets fall into the categories above.  This data was taken from the meta data for each of the data sets.

The following maps were created to display the data collected:

Conclusions:
Based on just the metadata provided with the data sets lots of important information was not included.  Resolution and scale should be two things that every data set clearly provide.  It is important for using the data with any confidence.  If I was using this data for a project I would have to keep in mind that some of the data may not be temporally accurate.  The earth is constantly changing and up to date data is important for doing accurate analysis.

Python Scripts

Python is a programming language that is user friendly and easy to learn.  The use of python in this class is aimed at gaining an understanding of what is happening behind the scenes in programs like ArcMap when running tools and other things that require using scripted programs.

Script 1:

In Lab 5 a script was created to project, clip, and load rasters into a geodatabase.  This code uses a for loop to go through the rasters and establish their datums.  The code then decides if it needs to be changed and will project it into the  projection of the database selected.

Script 2:
This python script is query out the mines that are active, are actually the facility type of mine, and do not have a railroad on site.  It then takes these results and creates a new layer called mines_norail_final.

Script 3:
This script was designed to make one class have more weight than the others for the raster modeling lab.

Sand Mining in Western Wisconsin

Introduction:
Fracking has been used for extracting oil and gas in the United States for past 75 years.  Fracking is short for the term hydraulic fracturing.  Fracking is the process of creating fractures in the ground by injecting a combination of liquid and sand mixtures into the earth at a very high pressure.  These cracks allow for easier extraction of oil and gas.  The sand used for fracking is a quartz sand with a very specific grain size and shape.  Western and  central Wisconsin is home to some of the very best sand for this process.  The geologic structure of western and central Wisconsin allows for the sand to be almost perfect size and structure for fracking.  Frac sand is then taken to a refinery that washes, sorts for uniformity, and dries it out.  The send is then ready to use for fracking all over the county.

The new development of horizontal drilling for natural gas in the U.S. has created a mad rush to get as much frac sand as possible. 

Problem:

The mad rush for frac sand has created issues in the local area. With low amount of regulation people have been selling their land and mines have been put up all over the state.  This created economic booms in the surrounding areas.  The problem is that frac sand mining has slowed down over the recent years and employment and economies have fallen because of the lack of money going into these areas. There is also the issue of pollutants that are emitted during the process of mining and refining the sand.  There are permits and regulations that need to be obtained from the state to continue operations.  Since there are environmental concerns around frac sand mining, the state and DNR are hesitant to give these permissions.  

Roles of GIS:

During this course I will be utilizing my skills in GIS to figure out ways to solve some problems in the frac sand business.  I will be using my skills to determine ways that frac sand mining is effecting the environment and ways that can be stopped.  Importing data into GIS platforms will allow for data manipulation, database creation, and a variety of other data implementation to allow for successful analysis. 

Sources:

WDNR. 2012. Silica sand mining in Wisconsin. Retrieved February 22, 2016.

http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

USGS. (2012). Frac sand in WI. Retrieved February 22, 2016.

http://wcwrpc.org/frac-sand-factsheet.pdf

King, P. (2015, June 3). Wisconsin towns worry frac sand boom will dry up. EnergyWire. Retrieved February 22, 2016.

http://midwestenergynews.com/2015/06/03/wisconsin-towns-worry-frac-sand-boom-will-dry-up/