Data Downloading, Interoperability, and Working with Projections in Python

Objectives

    There were two major goals dealing with this assignment: the process of downloading data from different sources, and becoming familiar with Python Scripter. I have downloaded data before from different sources so I am familiar with the process and how much "massaging" of the data that needs to occur, but I am fairly new to Python at this stage. By continuing to grow and learn Python will allow me to further my knowledge and understanding of the capabilities of ArcGIS.

Methods

    There were five basic steps when it came to data for this lab exercise: 1. download the zip files from the sources to a temporary directory, 2. extract the zip files to a working folder, 3. project the data, 4. load data into geodatabase through Python, and 5. getting rid of the redundant data. By following these steps with all my data obtained in this exercise I would be able to create the maps on sand mining in western Wisconsin.

    The first set of data I obtained was from the USDOT. This data included rail lines, Amtrak lines, and rail nodes. The website I obtained this data from was United States Department of Transportation website: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

This data came from 2015, and had the three shapefiles already established upon un-zipping the data. The second set of data I obtained was from the USGS National Map Viewer. There was three different sets of dataset included. One was a general large view topographic profile of my AOI or Trempealeau County, with the other two sets being DEM's of the county. With the raster and DEM's I will be able to focus on Trempealeau County. The website I downloaded this data from was The National Map website found here: http://nationalmap.gov/about.html.

The third data set I downloaded came from the USDA Geospatial Data Gateway. This data included land cover and cropland data, my focus being Trempealeau County. This data gave me all the different kinds of land cover and croplands that I would be able to find in the county. The website was: https://gdg.sc.egov.usda.gov/.

My fourth data set that I wanted to download was of the Trempealeau County land records division website. This would give me all kinds of different information, 85 different types of information, to create maps with of the county. The website I downloaded this data from was: http://www.tremplocounty.com/tchome/landrecords/.

The fifth set of data I downloaded came from the Web Soil Survey website. The data I was able to download was of Trempealeau County again, focusing on the different types of soils that have been collected and mapped out for the county. This will become very useful when talking and creating maps on Frac Sand Mining. The website I obtained this data from was: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.

Data Accuracy

    Dealing with downloaded data, it is important to look through all the metadata. Metadata tells us what scale the data is in, how it was obtained, and along with accuracy. I was only able to find portions of metadata for three of the data sets I downloaded.

Figure 1 shows the metadata that I was able to find.

Conclusion

     There are many concerns that have came to me when dealing with these datasets I downloaded. One thing that isn't shown in figure 1 is all the different projections that the datasets are in. This means that I will have to change them to make the data work. Another concern is that I wasn't able to find all the different metadata parameters. This could be operator error, or it could be that this metadata wasn't entered in. Not being able to understand all the different parameters of the metadata can cause my maps to be inaccurate or have false information. This is a major concern of mine.
Data Maps
   

These maps reflect the data I downloaded in this lab activity. I was able to combine different datasets allowing me to show more in-depth detail about Trempealeau County. This data will come in handy in future maps allowing me to visually show the Frac sand mining occurring in this area.