The objectives of this lab include

1. Using an AOI and land ownership layer to identify random plot sites

I accomplished this by using the land ownership layer to select BLM from the agency field, from there I used a 'select by location' tool to identify randomized points (described below) that fall within BLM land in the AOI.

2. Isolate randomized plot sites as to meet a quality of standard, avoiding the anthropogenic influence of roads but making the sites accessible

I generated 1000 randomized points in the AOI using a 'create random points' tool. After isolating the points that fell within the BLM boundaries of the AOI (described above) I set parameters that the sites would meet. First using 'select by location' I found points that were within 100 m of roads, using a road network data set for the AOI, then deleted these selected points from my set of BLM points. Then, I used the same 'select by location' tool to select points that were within 500 m of a road, and extracted this data to a new set of points. Lastly, to create a finalized set of 10 sites I used a 'subset features' tool which once again selected a randomized set of points within the subset of quality BLM points based off of a determined value (10).

3. Utilize the ArcGIS network analysis tool to create the most efficient route from the closest BLM field office

I combined the closest BLM field office location (ArcGIS dataset) using the merge tool, and then added an additional point to the dataset that would sit adjacent to the closest field office (Fillmore). I opened the attribute table and designated the two field office points as '1' and '3' under 'ID', and listed all field sites as '2'. Next, I ran two network analysis in order to create the most efficient route and additionally preserve the first and last step by designating the field sorting by 'ID'.