Lab 3 Vector Analysis with ArcGIS

Goal:

The goal of the project was to find suitable bear habitat within Marquette county Michigan for the DNR to set up within there purchased study area.

 Background:

The Michigan DNR was in search of suitable bear habitat within there purchased study area located in Marquette county. They needed to find areas that would have the highest success and bets placed for the protection of the bears in the area. They first needed to set a few parameters that needed to be meet when looking for a location.
The location needed to be where most of the bear sittings already where. They wanted to find the specific forest cover that where most used by the bears and they also wanted the area to be at least five kilometers away form an urban or built up area to keep down on the interaction with humans.

Methods:

To fulfill the needed parameters I first needed to set all of the locations in which the bears have been located. To do this after we found the X,Y coordinates from the USGS and the DNR information we needed to create a "event theme" which would allow us to use the given locations. Since the coordinates where given in a global coordinate system I needed to add all the data points and set the coordinate system to the same as the geodatabase. Once this was complete I could then export the locations in a new field and use the locations within the maps.

After exporting all of the bear location it was now time to focus on the parameters. First I wanted to see what area the bears where using the most for forest land cover. To complete this first task I needed to spatially join the bear location with the forest land cover. After I joined them I could summarize the table based on land cover type. The summarized table showed the top three forest land types used by the bears.

The next task the DNR wanted to find was how much of an influence streams had on bear location. Being that bears love fish and the similar habitat around streams the DNR needed to see if at least 30% of the bears where spotted within 500 meters of the streams to consider it critical habitat. To find if this hypothesis was true I needed to buffer a 500 meter zone around all of the streams. Once I found the buffer I could select all of the bear locations within the buffer zoned through a spatial join and found that about 72% of the bears used this area making it key habitat. Although since we already found one of the parameters we wanted to compound the results. Using the intersect tool with the buffer zone and the key land types I was able to find all areas that are suitable for the bear within 500 meters of a stream and within the key forest types.

The new key habitat area now can be proposed to the DNR to show them all of the key areas within there study lands. However, the DNR only had a select amount of land that they where able to put into management zones so in order to find the final project areas we needed to clip the DNR management sites from the key habitat area. Once found I could dissolve the internal boundaries leaving us with all of the final best areas for the bear management.

Lastly the DNR wanted to have all of the final proposed management areas at least five kilometers away from any urban or built up zone. To do this we needed to select by attribute from the different zones and find all of the urban and built up areas. Once we located the areas we then could place a buffer around all of the urban areas. and erase the buffer zones from the proposed key habitat this gave us the final and proposed area habitat.

Last to get a understanding of python we where tasked with finding some of the similar areas using the python coding. Below you can find the code used to find the buffer zone of the streams and intersecting it with the suitable habitat area to produce all areas suitable for bears.

Results:

Figure 1.1 shows the best bear forest land cover types based on the top three results which contained about 90% of the total locations. Figure 1.2 is showing the final proposed area found out of all suitable are within the streams and land cover parameters.
After adjusting for containing the DNR management zones and being away form urban areas in both figures you can find the proposed management zones in orange.

This is the model used to make the proposed area.

Lab 4 Mini Project

Goal:

The Goal of this project is to loacte and project the best area to place a recreational guide service that is placed in Brown, Kewaunee, or Door county Wisconsin.

Background:

Throughout my life I have always been an outdoor activist. Spending time outside, hunting, fishing, camping, and even visting parks. It was this passion and enjoyment from the outdoors that made me want to find the best location in Brown, Kewaunee, and Door county in Wisconsin. After living in this area for a couple of years I found a few parameters in which I would place a recreational guide service. They included being in the three counties, within five miles of Lake Michigan, one mile from a state park, and at least 3 miles away form an urbanized area.

Methods:

The methods used to find these few parameters where as follows.
First in order to better represent the states and the positions of the areas I needed to project the data frame in the UTM zoen for central wisconsin. By doing this all of the data although projected different would all be represented in the UTM projection presenting them in the most realistic way for that specific area. 

Next I needed to clip in the three counties of interest as a starting point for the parameters locations. By clipping them from the WI DNR metadata state county information I now could start the basis for the location

From there I needed to being location the area of interest. To accomplish this I needed to set up a buffer zone aroudn the few locations I was using as parameters. By selecting Lake Michigan usign a select by attribute I was able to buffer this new feature class by five miles. To be able to set the buffer around the parks systems I first needed to spatially join all of the parks that where completly within the three counties of interest. Once located I could buffer and dissolve all internal boundaries of the one mile radius. 

By intersecting the two buffers together as shown in figure 1.2 you could vividly start to see a general outline of where the best location would potentially be mainly in the upper peninsula of door county and around the edge of the bays in the remaining two counties. 

Lastly I needed to narrow my location by getting ride of any areas that where within three miles of an urbanized zone. I wanted to stay away from these areas by a few miles so costumers would not feel like they where in the middle of a city when they were supposed to be guided in the outdoors. I first set the buffer on the urban zones and then erased them from the intersected region thus leaving us with our best area for the guide service as seen in figure 1.3

Results: 

The results where as follows where the best area based on the four parameters would be in all areas highlighted in red. The main focuss for areas concentrated in the Door countie region and left a few small areas in Brown and Kewaunee. Further research would need to be done to find if these locations would be habitable for a building and if the land was purchasable. If not more studies would have to be donw and the parameters might need to be shifted to find a more feasible location. 

The following is a model built to represent the procces in which the maps where created.