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. 

GIS I lab 2 Wisconsin Census Data

Wisconsin Census Base Data

GIS I lab 2

Introduction:

The purpose of this lab was to get us familiar with the United States Census data website. We where tasked with the project of downloading and utilizing the copious amount of data at hand and portray said data onto a map of Wisconsin. The overall objective of the lab was to make two maps one including the total population of Wisconsin and the second was to show any other data of our choosing that interested our personal views.

 

 

Methods:

The objective of this lab was to utilize and outside source other than ArcGIS to portray data. We where tasked with the objective to familiarize ourselves to joining data with fields and attribute tables in ArcGIS for future use. To accomplish this task there was a few steps that needed to be taking in order to accomplish our goal successfully.

 

In starting the lab we first needed to download data from the US Census Bureau allowing us to have information about the population within Wisconsin. To do this we needed to open up the US Census data page and locate the information containing Wisconsin. First, we had to narrow our search by selecting specific topics within the US data base, like population, what type of geographical information such as county, then county within a specific state ( in this case Wisconsin). After we then needed to locate the proper attribute table to download, for there is still many types of tables one could choose from. In the case of this lab we wanted to focus on the SF1 data which is the summary data of the census and is the most basic data taken every ten years in the United States. Some of the other options one would see is the ACS which is a more detailed data, that has been put in place since 2005 and has now started to collect every year in high populated cities. However, for the purpose of this lab we needed to have total data and therefor looked at the summary file. Once we had located the appropriate file we then downloaded the file and saved it to our precise folder to extract all of the data.

 

To extract the data we opened our saved census file and extracted all of the attribute tables, these included metadata tables, the individual data sheets within the excel file, and some other tables that included information about the data. Once extracted the tables then could be connected within ArcGIS. Although at this point the data is not spatially referenced and needs to be linked to a existing file in the mapping program to allow us to manipulate and map the information.

 

To map the downloaded information of the first map (figure one left map), we downloaded the total population of each individual county we needed to join the census table with the existing ArcGIS map. To accomplish this we opened both the attribute table of the Arcmap and the attribute table of the census data and found what field names where similar to join. In the case of this table it was the Geographical identification number that was similar. Once joined ArcGIS now has the data information in the system and can reference the data.

 

To display this data however there is one last step we needed to take for the Arc program to understand the data. In the case of this specific census data download Arc could not display the data for it was a string (contained data with both numbers and letters). We first had to make a new field within the attribute table to get narrow down the records in the table. To accomplish this we made a new field named the field, and then used the field calculator to only process the number giving us the values we can use to show in a unique values map. After this step was finished we then could portray the data on Arcmap.

 

The final objective in this lab was to repeat the previous process but to locate our own interesting data. For my case I wanted to look at the correlation between population numbers and average age within counties. Coming from a small town in the north woods I noticed growing up that the majority of the people seemed to be older in age and retired. I wanted to see if my assumptions held true.

 

The results where as followed. Once the data was displayed we can see that for many of the counties in the northern region of the state there is significantly low population numbers but the average age is mostly between 47-51 years of age or older. This was a very interesting find for you can also see the areas in which a younger generation if found like in areas of more college based towns. These maps although simplistic could be very useful for business owners, politicians, or other interested parties.

 

Results:

 



 

Sources:

United States Census Bureau (2015, March 16).

 

Retrieved from: http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

 

GIS I Confluence Project

Clear Vision of Eau Claire (confluence project)

Goal and Background:

The goal of this lab is to become familiar with the Eau Claire district confluence project. The project was voted on by the members of the Eau Claire county and city. The Project is to develop two buildings along with public parking ramp to amplify and revitalize the old downtown area. The buildings will mainly be built to benefit the local community with bettering the education and presentation of musical and theater. It will also provided housing for a few students at the University of Eau Claire who is one of the major contributors to the project. The goal of assignment is to show all the different aspects that where looked during the planning of the confluence project, such as voting regions, zoning, and others.

Methods

The overall purpose was to build six maps that portrayed different aspects needed to show the public the location and the surrounding attributes of the area.

First we needed to located and make the proposed site known. Using the PLSS and the parcel data from the county data base we where able to single out and highlight the two prospected area including 128 Graham Ave, and 202 Eau Claire St parcels. Once we had created this data set we then could incorporate the data in each of the six maps to give a general overview to someone where the buildings would be placed along with a brief legal description. All of the following base maps have a base layer of the world imagery map overlaying the natural area of Eau Claire to show the natural features near the project.

Objective one: Civil Districts

 

This map included and incorporated the civil distinctions in the city of Eau Claire. Once placing the districts and the parcels over one another, you can get a general idea that the buildings will be built in the heart of what is known as the old downtown area. To fully get an idea of where we placed the premade prospected site layer into the map with clear labeling to show where exactly within the township the site can be found.

Objective two: Census Boundaries

The second map shows a gradient of population based on square miles. This was accomplished by overlaying the block and tracts group from the county data base. The overall map shows wonderful perspective of how dense the area is where the project is going to be carried out as well as the density of the surrounding areas to show how beneficial the project maybe.

 

 Objective Three and Four:

Both of these maps where not as in-depth as the previous two but are just as important none the less. The both incorporate the use of the Public Land Survey System to show both where the prospected site is and the parcel that they lie on. In order to fulfill this task in the third map or the parcel map we first needed to overlay the parcel and the centerlines of the roads over the base map. After we highlighted the near by water to show relative location on the parcel. The fourth map is very similar but in stead on parcels and centerlines it shows where the parcel is located in relevance to the PLSS(Public Land Survey System).

 



Objective Five:

Map five shows the zoning classes of the Eau Claire city, It focusses mostly on the area closest to the proposed site. We succeded in showing this by placing the projected site overlaying on the zoning class shown as unique value color graph. representing the different classes with separate colors.

 

Objective six:

The final map in this base map layout shows the voting districts in the emidiate surrounding area of the proposed site. It was accomplished by overlaying both the voting areas and the proposed site over the base map, with numerically labeled voting districts.

 



Results:

Figure 1.1
 

 

 

After completing and interpreting the maps we can not conclude any overlaying details within the maps. However, we can see a few notes that stand out. A few being that the main location of the proposed site will be located in the 31 district of the voting. We can also see that the main location of the sites will be in a commercial zoning area and not bother many residential homes with the building or post production activities. Lastly we can see that the general build of the site will be in a very centralized area and can be of great importance to the town and the people who thrive on the towns production.

 

 

Sources:

W:\geog\CHupy\geog335_s14\lab\lab1: 2009-07-13_EauClaire.gdb and City of Eau Claire.gdb

 

University of Wisconsin-Eau Claire (2015,Feb 18). Frequently asked questions: The Confluence Project. Retrieved from http://www.uwec.edu/News/more/confluenceprojectFAQs.htm