Ethan Nauman

GIS 335 Lab 5

5/12/14

 

      For this lab we were able to come up with any question we wanted and then had to project the question onto a map and build a model to show how we created our map. The purpose of this lab was to show the techniques and skills that we had learned over the semester long course. We had to use at least four tools to answer the question and three of them had to be different. For my question I wanted to see all the fires that had taken place in Iron County, WI since the year 2000, that had taken place in the county forests, and were within a 5 mile radius of the major roads in the county. Someone who might use this map to their advantage would be the surrounding area fire departments. By using this map they could come up with alternate routes to get to the fires quickly, or find easier ways to get water to put out the fires.

      This map required data from our WI geo-database along with data from the Wisconsin DNR, all of the metadata I used had already been downloaded onto our arcmap database. However here is the website that would allow you to download DNR metadata if it had not already been available https://www.dnrftp01.wi.gov/geodata/. Upon viewing the data from the DNR on fires I have come up with a concern. I want to know when they plot a point for a fire in the county forest, are they talking about a campfire that went out of control and burned a small area or are they talking about acres that had been burned. I would like to know what is the minimum acreage that has to be burned for the DNR to plot a fire point. This information could potentially change my map or even add fire points to it.

      To portray this map properly I had to use a few different tools. The tools I used to create this map were an erase, a buffer, and 3 different spatial joins. The erase allowed me to show just Iron county while erasing the rest of the counties and the outline of Wisconsin. After the erase I added all the major roads in the county, I eventually wanted to buffer the major roads which would allow me to show a 5 mile radius from the center of the roads. This could help by allowing different routes for the fire  department. For the first of my three spatial joins I joined county forests with Iron county. This would show just the county forests in Iron county. I also joined the major roads with Iron county. This too would also just show the major roads in Iron county. For my final join, I joined the fires with the county forests. This was the most important join since it would show all the fires that occurred in county forests in Iron county. Below is my data model for the map.

      The results of my map allowed me to show exactly what my question had asked. All the fires that had taken place in Iron county since 2000, that were within the county forests, and the fires that were within a 5 mile radius of the major roads in the county. On my map I also left the fires that occurred in county forests but that were not within the 5 mile radius just to show some of the remote areas where these fires had occurred in Iron county.

       My overall impression of this project was that it takes a lot of work to create maps from scratch, and it is sometimes difficult to come up with a sensible question allowing you to project a map of importance for someone or something. If I was asked to repeat the project and change something, I would change the roads section of my map. I would want to find all the roads in Iron county not just the major ones. This could shed more light for a fire department if they were to use this map to get to fires in remote areas. Some of the challenges I faced in creating this map was coming up with a question of relative importance. It was difficult to determine for me exactly what I wanted to project, but once I figured out my question I was able to create the map relatively quick.





      The goal for lab 4 was to enhance our geoprocessing tool set in ArcMap for bear habitat management in the study area of Marquette County, Michigan. We have been asked by the DNR department of Michigan to come up with a map that would be able to portray different scenarios that the DNR have asked for. For example, they want to know how many bears live in the county, what are their locations (x,y, coordinates), if the bears are in DNR management land, how far from streams were they located, along with much more information that will allow the DNR of Michigan to conduct their study and determine the best ways to manage the bear population and habitat.
      When making this map I used various different data tools to help get the information the DNR asked for the map. A few of those tools were spatial joins, buffers, multiple ring buffers, and export. Without knowing how to use these tools and what they do, it would have been impossible and taken forever to make a simple map that the DNR had asked for. I used a spatial join multiple times for this map, a spatial join joins attributes from one feature to another based on the spatial relationship. One example of a spatial join for this map is: I joined bear locations and land cover with a spatial join allowing me to determine the best areas in the county for bear cover. I also used the tool buffer, which would allow me to show the area of my liking around a point, line, or polygon. I was asked to show 500 meters around all the streams to determine if a stream played a role in the habitat for bears. Without these tools making this map would have been very hard to complete to the liking of the DNR.
      With my map I am confident that the DNR would be able to make good decisions in determining how to control and take care of the bear population along with the bear habitat in Marquette County. On my map I put all the bear locations that are symbolized by a yellow triangle. I put all the types of habitat that covers the entire county, and by using a spatial join I am also able to show suitable bear habitat throughout the county on my map. You can also see streams and DNR management lands in the county, however the DNR lands had to be 5 kilometers from urban and built up lands. Another big factor for this type of map is that it is more of a wide map then a height map, so I had to change the layout view to 11x17 allowing for better viewing of the map by the DNR.

Sources: all of the data were downloaded from the Michigan Center for Geographic Information
      Landcover: www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
      DNR units: www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.html
      Streams: www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html




  

      Lab three I found to be the most interesting lab that we have done so far. We actually were able to  get hands on experience with working in the field with GPS units, along with more work on our digitizing skills that we are beginning to master. The goal of the lab was to teach us how to collect data in the field on a GPS unit, then be able to transfer the data into ArcMap which would allow us to  overlay the data onto a map of the campus. With getting hands on experience in the field, I feel that it is very important to know and understand how to collect data on a GPS unit and transfer it over into ArcMap.
      When working with the GPS units there are some skills that I learned. First off I learned the difference between collecting data continuously and collecting data points, which then would connect to form the polygon or area that you're trying to map out. Another important skill I learned was how to read your GPS and track how many satellites it is reading off. It is important to have between 5 and 8 satellites which allow you to get the most accurate reading for plotting your points. After collecting the data, I learned the important skill of transferring your data from your GPS onto the computer, which in turn allows you to digitize and make small corrections for the areas that your GPS unit might have been off slightly. To get your data onto the computer you first have to connect the GPS with the HDMI cord to the back of the computer. This then allows you to check in your data that you want to get transferred over to the computer into your personal geodatabase. This step is key in making sure that your only transfer the data that you want and not some random data points or points that have zero significance with the map that you're trying to display.
      After I checked in my data and had the data added to my base map of the Eau Claire campus, I realized that some of the points from the GPS didn't line up exactly with how they were portrayed on my base map. This allowed me to make edits to allow both my base map and GPS points to align. I started with the Grass areas on my map. I brought up the editors toolbar and proceed to edit vertices. Once I was this far I could line up my GPS points exactly with how they were on the base map. This allowed my map to look neat and in order. I also had to plot out points for trees and light poles. This was very simple and the GPS points that I recorded were exactly were they needed to be on the map, the only minor changes I made were I changed the color scheme and symbols for the trees and light poles.
      Below is the map I created of the data I collected, while overlaying the base map of the Eau Claire campus. The grass areas I collected are in green, trees are bright green triangles, light poles are yellow dots, and the campus bridge is a red line at the top of the map. The campus buildings were already on the base map and are in a purple hue color. I was also able to put the title, north arrow, legend, scale bar, author, source, and date on the map to make it pleasing to the eye and easy to read.

Ethan Nauman

3/7/14

Lab 2 Blog Post

      The goal for Lab 2 was to introduce us about how to download information and metadata from the internet and transform it onto a map. It may sound easy but there were quite a few steps that I had to fulfill allowing me to open up the data in ArcGIS, and also being able to map the data in the correct format. A few of the skills I was able to take away from this lab were: being able to download data and metadata from the internet and unzipping it, allowing me to access it and map it in Arcmap. Another skill that I learned was how to normalize the data for a map, this lets me change what information that I actually want to be displayed on the map. Instead of just mapping the population like I did for the first map, on the second map I was able to change the normalization allowing me to map any of the data I chose from the second set of data I choose, area and size of the counties. Also, I learned about the color scheme of the data. By changing the color scheme to qualitative this allowed me to map more then one set of numbers. It allowed me to show you different populations throughout the state in the first map and it allowed me to show different areas of the counties in the second map. 

      I am now going to walk you through how I was able to download information from the United States Census Bureau and how I was able to add the files to my maps. First I had to go to the website for the census bureau, the I had to figure out what information I wanted to look at based on our lab 2 manual, and for the second map I was allowed to download any information that I wanted to map out. The first map was population in the counties and the second was the size and area of the counties like I said earlier. After finding the data, I had to download it and unzip the files which would allow me to access them in excel. This part was key and took a little figuring out and hands on time to allow me to download and save the information to excel. By using excel, the information then is allowed to be used in Arcmap. Once I viewed the metadata and tabular data in excel I had to change it from a csv file to an excel file. The next step was to open Arcmap and view the attribute table for the data to make sure that it came over properly from excel. I could then exit out of the tables and download the WI counties to the two data frames that I would be working with. After uploading the counties, I then had to join each of the two data sets together. I could verify that it worked by after joining, I had to again open the attribute tables. After verifying that it worked I then had to change the symbology of the shape files allowing me display the variables that I downloaded, population and area and size of the counties. Upon completing and displaying the data that I choose, my map was close to finished. All I had left to do was insert a north arrow, scale bar, title, author, source, year the data was from, and legend. I could then mess around with the display of the data allowing my maps to take up most of the space provided for me, and the color schemes.
      The results fell into the predictions that I thought would happen. For the first map on population of the counties, most of the population in Wisconsin is in the south eastern part of the state. In Wisconsin, most of the bigger cities are located in these regions from Green Bay down to Milwaukee and over to Madison. It is very easy to determine this by the color scheme that I incorporated on the map. My second map I chose to map the area and size of the counties. This also followed my prediction that most of the bigger counties would make up the northern part of the state were not as many people lived.  Almost exactly the northern half was quite a bit bigger, except for two counties down by the border of Illinois. Using the color scheme I did on the second map it's even easier to tell the size and area of the counties compared to the population on the first map. The source of the map came from the United States Census Bureau's website. This information allowed me to make the maps I wanted and is quite up to date considering it was information from 2010. The only map that would change is the first map considering that it deals with population and population can change some over a few years.
      I am overall happy with how the maps turned out and how the information is displayed. I believe it is quite easy to determine what I am trying to map and viewers of the information can determine the information by the different color schemes that I used. Below are my two maps on the information of population and area/ size of the counties.

Lab 1

Ethan Nauman

In 2012 Clear Vision Eau Claire joined partnership with University of Wisconsin Eau Claire and it's GIS students to help construct the development of the Confluence Project located in downtown Eau Claire. This plan is to make ground on a new community arts center/ university student housing and commercial retail complex.  The arts center will also house three performance spaces, galleries, offices, classrooms, studios, and much more. 

    The goal of this lab is for the GIS students to become familiar with spatial data sets used in public land management, administration, and land use methods to prepare base maps for the confluence project. A few objectives are to digitize  the Confluence Project site, learn about the Public Land Survey System, create a brief legal description of the two parcels that are given, and build a map layout of each of the major thematic feature classes. 

    This next section I am going to try to explain some of the major steps that I used to set up the maps for the Confluence Project and how I came about my final maps for this lab. I started by adding the data frame for the city of Eau Claire, this is the base for all six of the maps I created. From there I was able to add data for each of the different maps from parcel data, roads, zoning quarters, voting districts, and even civil boundaries. I needed to outline the area of proposed site for the project, once I completed the boundaries for one of the maps I was able to save the proposed site so I could upload it to all the maps. This saved me quite a bit of time rather than having to outline the site for every map. One unique tool that I learned to use in this lab project was the transparency tool. I knew there was a tool used for this but I never used it before, I used this tool on three of my maps I created: census boundaries, voting districts, and civil boundaries. This tool allowed me to show the underlying layer of the aerial view of the city of Eau Claire while still allowing my top layers to show what I wanted. 

file://localhost/Users/EthanNauman/Desktop/Screen%20Shot%202014-02-22%20at%201.03.11%20PM.png

For the Parcel Data map you can see the proposed site, the waterways that it is located on and the parcel areas for the map. The PLSS features map shows the quarters and quadrangles for the city of Eau Claire and where the proposed site is located. The civil boundaries map shows the three different boundaries along with the proposed site. The census boundaries map is my favorite because you can see the population of the surrounding area around the proposed site. The zoning map shows the different zones located around the proposed site, and the voting district map shows the different voting zones located around the proposed site. 

I am overall happy with how my maps turned out besides the zoning map, I feel that I could've added more or made it more appealing to the eye to make it jump off the spreadsheet better. This is my first lab for this course and I am hoping that I will be able to contribute the information and techniques I used on these maps to other labs and my job, that I hope to have in this field in the future.

 

Ethan Nauman