Goal
The main purpose of the lab was to become familiar with python and downloading data in an organized fashion. Below in figure 1 is an example of the general flow of this lab including both parts 1 and 2.
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| Figure 1: General workflow of lab 2. |
Part 1: Data Downloading, Interoperability, and Working with Projections
In order to begin this lab, a large amount of data needed to be downloaded. This data came from US Department of Transportation, USGS National Map Viewer (National Land Cover Data), USDA Geospatial Data Gateway, Trempealeau County land records, and the USDA NRCS web soil survey. This was such a great amount of data that it needed to be done in a very organized fashion in order not to lose it or the integrity of the data. In order to do this I created a file folder for each website that I downloaded data from. From here the data needed to be unzipped and imported into the correct folders. After this, I checked the data to make sure it was accurate. I joined the necessary tables, merged data, and once again made sure it was accurate. I collected information about all of the data that was downloaded and it is shown in figure 2 below.
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| Figure 2: Information about data download in part 1. |
Part 2: Working in Python
The goal for the second part of the lab was to become familiar with python script and apply those skills in order to create maps relevant to this project. There were three rasters we had to clip to the outline of Trempealeau county. The python script for that was used for this is in the previous blog post (GIS II: Python). Writing the script took a of effort before it all worked out, but after it all worked I imported the three rasters now in the shape of Trempealeau county into ArcMap. The final product of this lab is in figure 3 below.
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| Figure 3: Trempealeau county maps final product. |
Conclusions
The data that was downloaded in part 1 of this lab was very different from each other. Some of the metadata was complete and informative and others were lacking in those fields. I learned the importance of organizing my data from the start and continued that process throughout the lab. In part 2, I really dove into the python process. I eventually found myself understanding the ins and outs of the whole program, though many frustrating hours had to go into that before the understanding came. I found that writing the script was actually easier and less confusing at times rather than manually running the tools in ArcMap. I hope to use python more often after now learning the basics of the program.
Sources
NLCD 2011 Land Cover Metadata:
NLCD 2011 Land Cover data info:
NLCD product legend:
Elevation metadata (northern):
Elevation metadata (southern):
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