GIS II: Frac Sand Mining in Western WI: 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.