Introduction
For the final lab of this class the task was to use PyScripter to create a Swiss Hillshade tool that could be used in ArcMap to create a swiss hill shade on any DEM.

Methods
Figures 1.1 and 1.2 show the script that was used to create the Swiss Hillshade tool.

(Figure 1.1: Part 1 of Hillshade Script)

(Figure 1.2: Part 2 of Hillshade Script)

Exercise 8: Raster Processing with Booleans and Loops

Introduction
For the second to last lab the objectives were to create a script that can search through a file find all the TIFs. These TIFs were topo maps that needed to be projected and clipped so they could be properly mosiaced together. Also this script uses loops to modify the names of the topos in order to make more meaningful and manageable names. The 4 different topos depict four different areas of the National Park Kettle Moraine and when they are mosaiced together there will be a complete image of the park.

Methods
Figures 1.1, 1.2, 1.3 and 1.4 show the script that was used to make the mosaiced map below.

(Figure 1.1: part 1 of the script)

(Figure 1.2: part 2 of the script)

(Figure 1.3: part 3 of the script)

\

(Figure 1.4: The final part of the script)

Results

Figure 2.1 shows an image of the final mosiac process. 

 

Exercise 7: Risk Model for Landslides in Northwestern Oregon

Introduction
This lab is the final section in the risk model for land slides in Oregon. This lab used PyScripter and Python to create a fishnet that is used as a way to analysis the potential risk of landslides in regards to the roadways, buffers the major roads in the area, intersects the fishnet and road buffer, creates variables for the reclassifying of the rasters, reclassifies the rasters, multiplies the rasters to create the risk raster, uses zonal statistics to tally the risk values for each fishnet area and joins them back to the fishnet.

Method
Figures 1.1, 1.2 and 1.3 show the script that was used to create the risk model.

(Figure 1.1: Part 1 of the script)

(Figure 1.2: Part 2 of the script)

(Figure 1.3: Part 3 of the script)

Results

There are two maps in this section. The first is a locator map of Oregon that shows the where the study took place. The second map is the Risk Model of the study area, where the darker brown areas represent high risk and the lighter brown represents lower risk of a landslide. Figures 2.1 and 2.2 show the maps.

(Figure 2.1: Locator Map of the Study area)

(Figure 2.2: Risk Model of Landslides for Northwestern Oregon)

Sources

-The “Oregon Spatial Data Library” Accessed in 2016 from  http://spatialdata.oregonexplorer.info/geoportal/catalog/main/home.page

-USGS National Map Accessed in 2016 

 http://nationalmap.gov/

Exercise 6: Analyzing Raster Data for Landslide Susceptibility in Oregon

Introduction
In this lab PyScripter was used to create a script that would allow for the selection of landslides that meet the required criteria, add the raster values for precipitation, slope and land use to a point feature class, add and calculate fields to find a radius a of a buffer for the landslides and buffer the landslides using this radius, use zonal statistics to find the mean slope of the buffered landslides and add it to the table, create an update cursor to update the null values, calculate the summary statistics for the raster values based on movement class and land cover type then, as well as the combination of both and finally use the tabulate area tool to calculate how much of each buffer falls within different analysis class. The results were then exported to a MS Excel table.

Methods
Figures 1.1, 1.2 and 1.3 show the script that was used to create the final table.

(Figure 1.1: Part 1 of the script)

(Figure 1.2: Part 2 of the script)

(Figure 1.3: Part 3 of the script)

Results

Figure 2.1 shows a portion of the table that was created using the script. On the table are all the different land types that are within the study and have experienced a landslide. 

(Figure 2.1: Final Table)

Sources

-The “Oregon Spatial Data Library” Accessed in 2016 from  http://spatialdata.oregonexplorer.info/geoportal/catalog/main/home.page

-USGS National Map Accessed in 2016 

 http://nationalmap.gov/

Exercise 5 Using Raster Tools with Python

Introduction
This lab uses Python and PyScripter to create a script that is able to find a hand full of raster in a file, project the rasters to an appropriate coordinate system so that it is possible to clip the rasters to the study area. This script also runs the Hillshade tool and calculates the slope of the rasters. Lastly the script ends by mosaicing the rasters together so that there is 3 DEMs, Elevation, Hillshade and Slope.

Methods
In order to run all of the process for the rasters lists were created. Lists allow for the storage of many files in one line of code. So in this case there were three lists clipList, hsList, and slopeList. After each raster goes through the process that each list describes it gets stored in that list. In order to get this script to run the various different rasters continuously through the three different functions a FOR IN loop was used. This loops allows for functions to be ran over and over so long as there is a new feature for the function to be used on. Once all the rasters went through the loop and were stored in the 3 different lists they were then mosaiced together to create the final product. Figures 1.1, 1.2 and 1.3 show the script that was used.

(Figure 1.1: Part 1 of the Script)

(Figure 1.2: Part 2 of the Script)

(Figure 1.3: Part 3 of the Script)

Conclusion

Figure 2.1, 2.2 and 2.3 show the final mosaiced rasters that were created by the script.

(Figure 2.1: Elevation)

(Figure 2.2: Hillshade)

(Figure 2.3: Slope)

 

Exercise 4: Adding Fields and Running Calculations

Introduction
In exercise 4 use PyScripter and Python to add fields and run field calculations for the dissolved feature class from exercise 3. This script adds two fields to the feature class and runs field calculations for both as well. The first calculation creates a field that has the area in kilometers and the second calculation creates a field that contains the snow compactness for all areas larger than 2 kilometers.

Methods
Figure 1.1 and 1.2 show the Python script that was used to create the fields, run the calculations and selections.

(Figure 1.1: Part 1 of the script)

(Figure 1.2: Part 2 of the script)

Conclusion

Figure 2.1 shows a map of the results of the above script.

(Figure 2.1: Map of the compactness of the Ski Resort Areas)

Exercise 3: Python Script of Ski Resorts

Introduction
This exercise was very similar to exercise 1 where a model was used to find locations that are suitable for a new Ski Resort in the greater Rocky Mountain Range area. In this exercise instead of using a model a python script was created to find the same out comes. The python script was created using a program called PyScripter.

Methods
Figures 1.1, 1.2 and 1.3 show the script that was used to find the locations for the new ski resort. A combination of variables, functions and strings were used to create the script. The script is broken up into 3 parts because it made it easier to capture the entire script for uploading.

(Figure 1.1: Part 1 of the script)

(Figure 1.2: Part 2 of the script)

    (Figure 1.3: Part 3 of the script)

Conclusion

Figure 2.1 show a map of the final product of the script.