Data Gathering and Preparation for Frac Sand Mining Project

Introduction:

The goals and objectives of this lab were to learn how to properly query and download data from various online sources.  This data that was downloaded will all be pertinent to the future suitability model to be run regarding frac sand mines in Trempealeau County.  After the data was downloaded, it was required to run a python script in order to properly convert all of the data to the NAD83 HARD WISCRS Trempealeau County Feet projection and clip all of the data to the Trempealeau County border.  All of this data is now stored in a file geodatabase and is ready to be used in any future analysis.

Methods:

The first step in this lab was to download the various data required.  All of the data needed to be queried, downloaded to a temporary folder, and unzipped into a working folder.  The various data downloaded is shown in Figure 1.  The base sets obtained included a railroads dataset from the US Department of Transportation, a Digital Elevation Model from the US Geological Survey, soils data from the US Department of Agriculture Soil Survey, and land use/land cover data from the USDA.

These various datasets were queried and downloaded to help aid in the future project.  They were all downloaded to a temporary folder and then unzipped the data into a working folder.  (Figure 1)

After all the data was put into the working folder, it was examined and determined that the rasters for the DEM, the soils info, the LULC info, and the railroad vector feature class were all to be imported into the Trempealeau County geodatabase and converted to NAD83 HARN WISCRS Trempealeau County Feet.  This was done using a Python script (Figure 2) in order to help teach basic programming in Python.

This is the Python script that was generated and run in order to project the necessary rasters, clip them by the Trempealeau County boundary, and import them into the Trempealeau geodatabase.  (Figure 2)

From here it was possible to generate a map to show all the different projected and clipped layers that had been downloaded and are now ready for further analysis (Figure 3)

This map was generated using the data downloaded from various online sources.  The data is now in a file geodatabase and will be used in future exercises to run analysis on frac sand mining operations in Trempealeau County.  (Figure 3)

Data Accuracy:

An important aspect of the future of this project is knowing the limitations of the data gathered.  In order to know this, it's important to look at the metadata and find the different data quality components such as scale.  As much information on the data as possible was found and reported in Figure 4.

This is a data quality table that was generated by looking at the metadata of the various sources of data downloaded.  Some information was unavailable and some was estimated and is an approximate value.  (Figure 4)

Conclusion:

Downloading the appropriate data and prepping it for further analysis is a critical step in suitability modelling and will be extremely pertinent for the future of this project.  It is also important to know the quality of the data downloaded.  Some problems that may arise are the land cover and cropland classes being at a larger scale than the rest of the data.  The soils data and DEM data are precise and up to date and seem like they will be very useful for the future of this project.