Exercise 5: Data Downloading, Interoperabiliy, and Working with Projections in Python

Goals and Objectives
This exercise had a few goals.  Becoming familiar with downloading data from varies sites on the internet, learning how to import the data into ArcGIS, joining certain data sets with tables and then projecting data from different coordinate systems into one. From there the task entailed building a geodatabase and finally building and running a script in python to manipulate the data. In post 2 is the python script that was used during this exercise.

General Methods
The data for this lab was obtained on the internet from several different websites. There was data used from major data suppliers such as the USGS, USDA, USDA NRCS, and the Trempealeau county land records along with a few other sites. Prior to the process of downloading data, a folder was created in the temporary folder on the Q drive. This is done because the data that was downloaded required a lot of space, and the Temp folder is emptied every month. From the Temp folder the zip files were unzipped into the working folder that was previously made in the Q drive in the class specific folder. Once in the working folder, the data is now able to be worked with. The railways were clipped to be the ones just inside Trempealeau county. The second part of this exercise was to build the Python script to that manipulated the data into the maps below. Figure 1 is a table of the sources that were used, there uses for this exercise and there web addresses. Figure 3 and 4 are just a few sample maps of the finished product geodatabase for this exercise.

Figure 1

Figure 2

Figure 3

Figure 4

Data Accuracy
The Metadata provides an insight into what the data can do in a way, it is very effective in explaining the limitations of the data. Below in Figure 5 is the table that displays the metadata, if there was no data then the cell is designated with N/A.

Figure 5

 The amount of cells that are not filled and display the N/A are a pretty good hint that maybe the data we received is not as reliable as initially thought.

Conclusion
The skills that were learned in this lab are crucial to be a productive member of the workforce. Each step that was done has a real world application that will be utilized in the future. Downloading, joining, clipping and organizing data in a geodatabase is an essential skill to have. At times this exercise seemed impossible, the python script is not easy and requires patience, not only did the lab teach us the skills but it also offered a valuable lesson in time management.

Post 2: Python Scripts

Python is a programming system that is designed to help user deliver more concepts in fewer lines of text. Python makes geoproccessing in ArcGIS more simple, this is the system that ESRI chooses to use as its base platform for open source distribution. Python has many operations that it can do including frameworks, advanced content management, web development and many other uses. In comparison to other similar software it is widely considered to be more user friendly. Knowing how to work with a program such as python helps to diversify your skills and is becoming a larger part of the GIS industry each year.


 Below is the Python script that was written for exercise 5, the data was taken from multiple websites online, organized in Arc catalog and then the script was written to do the operations that are described in the script.

Figure 1: First attempt into writing our own script

Below in Figure 2 is a Python script that was designed to preform network analysis to help find a way to accurately tell the impact of trucking sand to rail terminals by the use of roads. The script below was made to include certain details about the mines. The mines had to be active and more than 1.5 KM away from a railway. That is done because some sand companies build there own private railway to hook up with the big rail system. The script below produced a total of 44 mines. 

Figure 2: Sand Mines Script