Gathering Data

Goals and Objectives:

The main purpose of this exercise was to gain practice in gathering online data. This data is needed for the semester long project regarding frac sand mining in Trempealeau County. This exercise also places importance on importing, joining, and projecting the data. Finally, the exercise allowed for experience in creating a geodatabase for all of the data to be stored.

General Methods:
To begin downloading data, I first created a new folder in my Q-drive labeled Ex_5. This allowed for better organization of all of the data. The data gathered in this exercise came from:
                            
                             US Department of Transportation
                             US Geological Survey- National Map Viewer
                             US Department of Agriculture
                             USDA NRCS Soil Survey
                             Trempealeau County Land Records 

 Each of the datasets were downloaded to a temporary folder in the department's Q-drive because of the large size of the unzipped datasets. From the temporary folder, the data was extracted to a working sub-folder within the Ex_5 folder. The data downloaded was then projected, joined, and added to a geodatabase through Python Script. This process can be seen in the blog post titled "Python Coding". The following maps of relevant data were then created in ArcMap (Figure 1). 

Figure 1: these four maps consist of relevant raster data for Trempealeau County, WI. Data was collected from various online sources. These sources can be seen below in "Sources".

Data Accuracy:
It is important when gathering data off of the internet to be confident in the data accuracy. This can be determined by looking at the metadata. Metadata should be found in the properties of every dataset and shows how accurate and reliable the data is. Important information in the metadata include its scale, effective resolution, minimum mapping unit, planimetric coordinate accuracy, lineage, temporal accuracy, attribute accuracy. This information can be seen in Table 1.


Conclusions:
The above data shows a wide array of accuracy and extents. It is important to know the type and accuracy of the information for this project because of the reliance on accurate features. In order to create accurate maps and analysis of frac sand mines and their impact on the Trempealeau County the data collected and used must be correctly displayed.



Sources:
US Department of Transportation-
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

USGS National Map Viewer-
http://nationalmap.gov/about.html

USDA Geospatial Data Gateway-
http://datagateway.nrcs.usda.gov/

Python Coding

Python

Python is an extensive computer coding language used in several programming software. In ArcGIS, python is used to organize and conduct geoprocessing. Using python allows for faster and more efficient geoprocessing procedures. 

Purpose

The purpose of this blog is to demonstrate the various python coding skills learned throughout the semester in GIS II. These python scripts will be used to work toward the semester long project regarding frac sand mining and the impacts it has on Trempealeau County, Wisconsin.

Python Script #1:
PyScipter, a python software, was used for this script. PyScripter allows for better organization compared to the python window found in ArcGIS. This python script shows the process of gathering 3 raster files (downloaded in part 1 of exercise 3 from various sources) and putting them into a geodatabase. The geodatabase is for data within Trempealeau County and is title "TMP.gdb". Once the files were ready to be put into the geodatabase, I began using pyscripter. To begin, I had to set the workspace environment and several variables to be used throughout the script. Within the script, I had to project the rasters by adding a file from a dataset in the geodatabase already. By doing this, the raster in question will adopt this dataset's projection. Next, I had to clip (extract by mask) the files with the Trempealeau county boundary. This allows for faster and more efficient geoprocessing. Finally, I moved the raster files into the geodatabase to be used in the remainder of the project.

Python Script #2:
This python script was created to prepare the data for network analysis in Exercise 7. Our goal was to determine the impact that transporting frac sand has on roads within the local area of the specific mine. The following perameters were set to calculate this impact.

• The mine must be active.
• The mine must not also have a rail loading station on-site. 
• Mine must not be within 1.5 kilometers of a railroad

To begin, I set up the script and set the work space environment. I then set up the variables that would be used throughout the script. Using these variables, I wrote  several Structured Query Language statements to ensure the parameters were met. I then ran these statements to narrow down the amount of mines to be used. From here, I selected all of the mines within 1.5 km of the railroads and removed them from the selection. This left me with all of the mines that can be used later on in the exercise to conduct network analysis.