Ethan Nauman

Lab 4 Mini Project

 

Lab 4 Mini Project

 

    For our end of the year mini project we were assigned the task of creating our own question that pertained to a real life scenario. Since my cabin is located in the National Forest in Taylor County, WI, I wanted my question to be centered around that. Taylor County is pretty isolated with a few small towns scattered throughout. With these small towns being located all over, I wanted to know how they would respond to forest fires in the area and in the National Forest. My question was, "I want to create a map to see all the forest fires in Taylor County since the year 2000. These fires had to take place within the national forest, and within 250 meters of a road. I also wanted to see the fires located within a 1 mile radius of the towns in Taylor County". My intended audience at first was just for me. I was curious to see if any fires had occurred recently that were close to my cabin. There wasn't any. Then I thought more, since I was also showing a 1 mile radius of fires by towns, I figured that these fires would be the easiest for these towns and firefighters to respond to. Also, since I was showing the fires in the National Forest that were only 250 meters off of a road, these two would also be easy to put out and not cause a major hazard.

    To answer this question, I had to use two different data sources. The first source I used was ESRI. I was able to use the state outline of Wisconsin and also the county outlines. This allowed me to bring my area of interest down to just Taylor County. The next data source I used was from the Wisconsin DNR geodatabase. I was able to upload the forest fires, cities, roads, and national forest. Without having access to these feature classes from the DNR, this project wouldn't have been able to happen. After completing my map and analyzing it, I have some concerns about the data. One of my major concerns is about the roads feature class from the DNR. What I mean by this is, some of the roads could be logging roads or have very hard access to in the National Forrest. This could mean that although they say there is a road there, is it washed out, does the road not have access to in the winter time, or is this road over grown by forest now. Another data concern that I have is about the cities. I did a 1 mile buffer around the city to show fires inside that 1 mile. Now where is the center of that buffer located, is it at city hall, or what would make more sense in this case would to have the center be located at the fire station.

    I now want to talk about the methods and tools I used to complete this project. Upon getting my area of interest, Taylor County, I next had to bring in all the feature classes that would allow me to show the fires of interest. The first too I used was a buffer. I buffered the roads throughout Taylor county to 250 meters and I also buffered the city to a one mile radius. This would allow me to show fires inside these. The next tool I used was the intersect tool. I intersected the roads buffer and the national forest. This tool combines the two feature classes, while showing the roads and the buffer throughout the national forest. After using the select by attributes tool which I could then show the fires from 2000 to 2009, I used the select by location tool. For the select by location my two inputs were the fires from 2000 and on, and the National forest. This would show the fires in my National Forest roads buffer, and within the 1 mile radius of my towns. The last tool I used was the merge tool. Since I got two different feature classes, one for the fires in the National Forest and one for the fires within the 1 mile radius of the towns, I wanted to combine them and create a single feature class. The merge tool allowed me to do this, and I renamed these fires of interest. Below you can see my data flow model showing all the tools I used throughout the completion of this project. The data flow model will read from right to left. It will read buffer, buffer, intersect, select by location, and merge.

 

    The results of my project show the fires of interest. These fires are located within 250 meters of a road in the National forest, and within a 1 mile radius of the towns in Taylor County. The fires of interest are located by a red dot, cities a bright green dot, and the roads are black. The National Forest is a dark green color, the cities 1 mile radius is in light blue, the national forest roads buffer is in a light green, and the Taylor County roads buffer is in a rose color. I named my project "Fires of Interest in Taylor County, WI" because that was exactly what I was trying to show. I feel that my map makes sense and is easy to read after a quick glance at the legend. I also added a picture of a forest fire just to show the destruction that these can have on a forest if they are not contained and put out. We always here about the gigantic forest fires out on the west coast, I am just glad that we have enough fire departments to be willing to not let these cause major destruction in Taylor County, better yet not to have my cabin catch fire!!

 

    If asked to repeat the project one thing I would change is the cities buffer. A one mile radius isn't very far, I probably would change the radius to at least 5 or 10 miles. This would show more fires of interest on my map. I also would like to find data on how long it would take for the fire departments to arrive on the scene of the fires and how long it would take to put them out. I understand that it would be hard to find this data because I highly doubt that someone has a stopwatch going to time exactly how long these things would take.

 

Lab 3    

    Goal: The goal for lab three was to introduce us to geoprocessing tools for vector analysis. The tools that we would use would help us to determine suitable habitat for bears in the study area of Marquette County, Michigan. We used a variety of spatial analysis tools along with a few non-spatial tools. The tools used in this lab will lay the ground work for the final two upcoming labs as we will continue to use these tools throughout the remainder of this course.
    Background: The objective of this lab was to workout a scenario for the Michigan DNR. They asked us to find and determine the forest types where black bears are found in central Marquette County based off of the GPS locations from the bears. They want us to determine if they bears are mostly found near the streams, to find bear habitat based on land cover and distance from streams, to find the areas that are managed by the DNR in these bear habitats, while eliminating areas near urban or built up lands, and to be able to generate a data flow model showcasing our workflow and cartographic output.
    Methods: The first tool I used was in objective two. I completed a simple inside spatial join between the bear locations and land cover to give me a table. Upon opening the output attribute table I summarized the minor type field which in turn gave me how many bears live in each land cover. The top three land covers were: mixed forest land with 31, forest wetlands with 17, and evergreen forests with 14 bears respectively.
    The next two tools I used came in objective three, which asked me to find the number of bears that were located within 500 meters of a stream. The first tool I used was a buffer. I buffered the streams to 500 meters and did a dissolve all option which gave me all the streams buffered rather then each individual stream. The next tool I used was the clip tool. I clipped the bear locations and the streams buffer, this gave me the number of bears inside the 500 meter buffer. There were 49 bears inside the buffer out of 68. This equaled 72%, anything over 30% is considered an important characteristic. So, the distance for a stream is relative to bear habitat and is an important characteristic.
    The next two tools again came in objective four. This task asked me to find suitable bear habitat based on two criteria: suitable land cover types, and within 500 meters of a stream. The first tool I used in this task was the intersect tool. I intersected land cover and the streams buffer, this gave me an out of land_streams_intersect. It shows suitable landcover types within 500 meters from a stream. The next tool I used was the dissolve tool. Since polygons from the land cover and streams buffer overlapped, I wanted to get rid of all internal boundaries. I dissolved land_streans_intersect, which gave me landstreams_intersect_dissolve. This rid the internal boundaries in the buffer zone.
    Objective five asked for recommendations to the DNR for a bear management plan. The DNR were looking for suitable bear habitat that were located on their management lands. I had to perform overlay analysis from the political boundaries to find all areas in the suitable habitat that were within the DNR management lands. I clipped DNR_mgmt and the study area. This gave me all the DNR management lands inside the study area of Marquette County. Next I just wanted to find the DNR management lands that were inside my 500 meter buffer. I again clipped the previous clip of DNR_mgmt_clip and the landstreams_intersect_disolve, this gave me the DNR management lands just inside the 500 meter buffer.
    Objective six ask for me to make another map. The DNR liked the work done on objective four that they decided they wanted to change the bear management areas to outside 5 kilometers of urban or built up lands. I began with selecting the urban lands by the select by attributes. I created a feature class out of this selection. The next tool I used was the buffer tool. I buffered 5 kilometers around the urban lands. I then had to clip the buffer to the study area which gave me the area that isn't suitable and the area that is. A majority of the suitable area lies in the northern half of the county while the urban non suitable lands fell in the southern half.
 Results:

 Pictured above is the final maps that I created for this lab. The top right map is a small locator map of Marquette County. The bottom left map if from objective four of the lab asking to show suitable bear habitat based off of two criteria, 500 meters from a stream and land cover. The top left map is from objective six asking to show habitat that is outside of the 5 kilometers from urban lands. The stripped area is not suitable for bears.
Pictured above is my data flow model throughout the lab. It reads from right to left then down. So, it goes streams buffer, then clip, and then down to intersect.

    Python: Above is a small introduction to python script from this lab. The lab wanted to teach us just a few things of how python works and how to understand the script that we were writing. To me, after using python, I think that it is much easier to use then using the tools in Arcmap. It is easier to understand, and easier to come to the conclusion you are looking for. I am excited to use more of python in future labs.
    Sources: There were four different sources that we used to create this map of bear habitat in Marquette County, Michigan. The first source is : Michigan Geographic Data Library.
Source two, land cover is from the USGS NLCD,
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

Source three, DNR management units,

http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

Source four, Streams,

http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

　
　

　

Ethan Nauman

March 18th, 2015

Lab 2

     The objectives of lab two was to teach us how to download information from the U.S Census Bureau, save it and unzip it again, and then to properly join metadata and an image to make a map pleasing to the eye. There were essential steps I had to follow that would allow me to be able to carry out the task I wanted to perform. The most precise steps had to do with saving the metadata and unzipping it properly in a way that would allow me to perform my task. Another key task that I performed in Arcmap was joining the image and the metadata. This allowed me to be able to show either the total population or the total population of african americans in Wisconsin. Joining the information caused me the most trouble as I had to change the metadata into an excel file that would allow me to properly join the information.

     I now want to go into greater detail about the steps that I performed for lab two. Like I stated above, I started with downloading a map of Wisconsin from the census website. This was a easy step seeing I didn't have to change the data at all. On the census website, you are able to select different fields such as county, state, and what parameters you want to search from. I followed the simple steps which allowed me to download the image of Wisconsin. The next step was locating the metadata for the population of Wisconsin. Again, I was able to follow parameters on the website that allowed me to search by state and county. I was also able to search for population in these parameters. Once I located the population metadata, I downloaded and saved it into my lab2 folder. From that folder, I had to unzip the data. This allowed me save the information, four files, again into my lab2 folder. I opened the metadata in excel. The next step was to save the data as excel workbook. The next steps was building my map in Arcmap. This was relatively the easiest part. I brought in my picture of Wisconsin, and also brought in the metadata through the catalog. You are allowed to bring excel saved tables into Arcmap which makes the job way easier. After bringing them both in, the next step was to join the data and picture. I used the join tool which Allowed me to combine the pictures attribute table and the metadata attribute table. I based this join off of the ID from both of the tables as they matched each other. Now that I had one attribute table, the next step was to change the symbology of the map. I used graduated colors which allowed me to properly display my total population information by county of Wisconsin. I then was asked to complete the same steps of downloading metadata from the census website on information that I wanted to find out more about. I chose to download information on race, geared specifically towards the african american population in Wisconsin. I wanted to find out were the majority of the african american population subsided in the cheese state. I conducted the same steps that I performed in the prior process of downloading the data and bringing it into Arcmap. When I was complete I had two different maps displaying the total population of Wisconsin and the total african american population of Wisconsin, all by county. 

     While I was downloading the information from the census website and was trying to save it properly and build a map, I thought that it was very difficult to remember the order of the steps I needed to carry out. Before settling on building my last map on the total african american population, I tried the number of vacant houses in Wisconsin and sex by age. When it came to downloading my final info on african americans, I was able to carry out the steps without rereading the directions. Looking back on this lab I realized that the tasks that I carried out were relatively easy and was overall happy with how my maps turned out. (Pictured below) I brought in a baseman of the United States and also brought in the outline of the individual states. This allowed for a nice backdrop for the information I was trying to show. I made separate legends and also put in a north arrow. I cited my source as the U.S Census Bureau and included a scale bar. I messed around with changing the number of classes on both maps but they didn't come out very pleasing so, I left each of them with five classes. 

Lab 1 Blog

 

Ethan Nauman

 

2/20/15

 

 

In the spring of 2012, Clear Vision Eau Claire announced a partnership between local developers and UW- Eau Claire. The partnership was intended to setup to construct a new development at the confluence of the Chippewa and Eau Claire Rivers in downtown Eau Claire. The development, the "Confluence Project", planned on breaking ground on a new community arts center/ university student housing and commercial retail complex in downtown Eau Claire beginning in 2014. The arts center would be home to three performance spaces, galleries, offices, classrooms, studios, and more.

 

The goal of this lab was for students to become familiar with various spatial data sets divessed for the Confluence Project, public land management, administration, and land use. The students were tasked to prepare base maps for the Confluence Project. We were also asked to create a brief legal description about the parcels that the Confluence Project is going to use.

 

To start with making the base maps, I first had to create a data base that would allow me to digitize the proposed site. After looking through legal documents of the parcels that were given to us, I was able to properly digitize the area of the proposed site. This would be used in every base map that I created so it was essential to digitize the correct parcels of land. The next step was creating the six base maps of the Confluence Project, the civil divisions map, census boundaries, PLSS features, EC city parcel data, zoning, and a voting district map. All of the maps were made in Arcmap, and all of the information brought into the maps were provided to us from the Eau Claire City geodatabase.

 

My civil divisions map allowed me to show the surrounding area of the proposed site. I was also able to show the city boundaries, town boundaries, and village boundaries around the surrounding area. This was the first map I created and it was also the easiest.

 

My census boundaries map shows the proposed site, tracts, and block groups that are determined by population per square mile. As you can see from the map at the bottom of the page, the surrounding area of the proposed site has a high population per square mile. This could be due to the fact that a lot of people go to work in this area, and also there are lots of houses located in the downtown area along the rivers. With the project putting up more housing for students, I would expect that population to grow and potentially bring more life to our downtown area.

 

The PLSS Features map shows the proposed site and the PLSS districts. I used the quarter- quarter district because I wanted to be able to show the district lines still when I was zoomed in. This is probably the most plain base map I made but it still is essential by showing what district exactly the proposed site would fall into.

 

My Eau Claire city parcel data map shows the proposed site, the parcel areas, water, and centerlines of the roads. Looking at this map you can see exactly the parcel areas of the surrounding area and the proposed site. This is essential because if I didn't have the proposed site in the right area, they could possibly tear down a wrong building and cause destruction were it wasn't meant to be. Also, for this base map you can see the centerlines for the roads in the area. This allows for easy access to the proposed site by have it facing two, possibly three roads.

 

The zoning map I created has the proposed site, and zoning areas of the surrounding area. As you can see there are 6 different types of zoning areas that are possible for the proposed site to fall into. The site would fall into a public properties zoning area, while it is closely located next to the central business district as well.

 

My final base map I created was a map of the voting districts. This as well is a pretty plain map. I used a imagery map of the area and brought in the proposed site as well as the voting wards. I was able to label the voting wards so you could see exactly were the site would fall into in the voting wards. The site would fall into the 31st voting ward.

 

By completing all six of my base maps, I was able to properly show all of the features involved with the proposed site. With my base maps created, I would feel confident in showing the partners of UW- Eau Claire my maps and that they could properly disifer where the proposed site needs to be and any other questions involving the area around the proposed site.

 

 

Legal Descriptions:

Parcel No.:020365

PIN: 1822122709200042068

Street No.: 128

Street name: Graham Ave.

Owners name: Haymarket Concepts LLC

Owners address: 3506 Oakwood Mall Dr.

Owners city, state, zip: Eau Claire, WI, 54701

Legal descriptions: Lots 1,2,3,4,5,6,7,8,9 and 10, all in block 62 of the Plat of the Village of Eau Claire, also known as Gleason and Wilson's Subdivision of block 62, Village of Eau Claire, now City of Eau Claire, Eau Claire County, WI

 

Parcel No.: 020357

PIN:1822122709200042063

Street No.: 202

Street name: Eau Claire St.

Owners name: Haymarket Concepts LLC

Owners address:  3506 Oakwood Mall Dr.

Owners city, state, zip: Eau Claire, WI, 54701

Legal description: Lot 1 and W 1/2 of lot 2 block 58 and A PC of land beginning at SW corner of said block 58 THC W on N LN of EC ST 45 ft THC N PRLL with W LN of said block 145 ft THC E PRLL with N LN of said block 106 1/2 ft THC SLY PRLL with W LN of said block to N LN of lot 2 of said block THC WLY on the N LN of said block to NW corner of same or lot 1 block 58 THC SLY on W LN of said lot 1 to POB and also the land between the above DEX land and EC river village of EC TID 8