Lab 5 Potential Cabin Locations in Bayfield County, WI

Introduction

For this lab my goal was to find out the best locations in Bayfield County, Wisconsin that I could build a cabin. I created a list of four specific criteria for where I wanted my cabin to be located in Bayfield County. The first criterion was that the location must be in a census tract with a population greater than 2,500. I wanted my cabin to be in an area that was somewhat isolated but I also wanted there to be people in the vicinity. The second criterion was that the location had to be within 25 miles of a lake. If I were to build a cabin in Bayfield I want it to be near a lake so that I can go boating and fishing. The third criterion was that the location must be within 20 miles of a county forest. I wanted my cabin to be close to a county forest so that I could go on nature walks and experience nature. The fourth criterion that I wanted was that the location be at least 5 miles away from a major highway. The map that I created for this lab shows the best locations for my cabin. I think the map may be helpful to anyone who is thinking about or planning to build a cabin in Bayfield County. 

Data Sources

To answer my spatial question I needed to use data provided by ESRI and the Wisconsin DNR. I retrieved my data from the ESRI and Wisconsin DNR databases in ArcMap. The data files that I retrieved from the ESRI database include the Bayfield County boundary, the lakes of America, census tracts, and major highways. From the Wisconsin DNR database I used data for the county forests in America.

Wisconsin DNR- http://dnr.wi.gov/maps/gis/       
ESRI- http://www.esri.com/data/find-data

Methods

To answer my spatial question I first added the specific data layers I needed to my map from the ESRI and Wisconsin DNR databases. Next, I located my area of interest, Bayfield County, Wisconsin in ArcMap and made it a feature class. Next, I clipped the census tracts, lakes, county forests, and major highways data layers to the Bayfield County feature class. My next step was to begin using the analysis tools in ArcMap to answer my spatial question. I began by looking for census tracts that had a population of greater than 2,500. I did a select by attributes query to find the census tracts that had a population greater than 2,500. After I found the tracts I wanted I proceeded to find areas in Bayfield County that were within 25 miles of a lake. To find these areas I created a 25 mile buffer on the lakes data layer and dissolved to make sure that any internal boundaries were cleared. To find the areas that were within 20 miles of a county forest I created a 20 mile buffer on the county forest data layer. Once I had found all of the criteria I wanted for my cabin location I was ready to use the intersect tool. I intersected the new census tract, lake, and county forest data layers that contained the areas I wanted my cabin to be located. Then I moved on to find the areas that were 5 miles away from a major highway. To find these areas I created a 5 mile buffer on the major highways data layer. I did not want my cabin to be near a major highway so next I used the erase tool to remove the areas in Bayfield County that were 5 miles from a major highway. After doing the previous steps I finally discovered the best locations to build my cabin based on my criteria.

Results

My final map has four separate maps that show each of my criteria along with the best locations to put my cabin. Based on my maps the areas that met my criteria were located mainly in the northern region of Bayfield County.

Evaluation

I thought this project was fun because I was able to choose what my spatial question was going to be. I also liked that I was able to choose the criteria I wanted for the question. If I were to do this project over I would try to make my criteria more detailed and less basic. In order to make my criteria more advanced I would look up more information about what attractions are in Bayfield County that I may want my cabin to be near or do some research on locations that I may not want my cabin to be next to. For example, next time I would try to find data on the locations of restaurants in Bayfield County because when I go to the cabin I would like to be able to not have to drive very far if I want to go out for a nice meal with some friends.

I ran into some challenges as I worked through the project. One problem that came up was that one of the criteria I chose did not work when I ran the analysis tools. Before I ran the analysis tools I wanted my cabin to be in a census tract with a population under 1,500 and be 25 miles away from a major highway. The area that resulted from this criteria was non-existent because there were no areas that matched this criteria in Bayfield County. So in order to fix this problem I had to change the buffer distances. After changing the buffer distances I was able to find areas to put my cabin.

Figure 1. Potential Cabin Locations in Bayfield County, WI

Data Flow Model

Lab 4 Vector Analysis with ArcGIS

Goal

The ultimate goal for Lab 4 was to use different geoprocessing tools available in ArcGIS to help find suitable habitat for bears located in the study area of Marquette County, Michigan.

Methods

The first step I took to find suitable bear habitat was to find the top three forest types that bears prefer in Marquette County. I did a table join between the landcover and bear locations feature classes to determine the top three forest types that the bears liked the most. Next, I wanted to figure out if streams were essential in a bear habitat. To find out if streams were important to bears I did a query of the bear locations and streams feature classes to see if the bears were located close to streams. I found out that streams are important to a bear habitat because 72 percent of the bears were located within 500 meters of a stream. Now I had the two important criteria for a bear habitat, close proximity to streams and the best forest types for the bears. Next, I found the best areas for bears to live in Marquette County based on the criteria I created. I clipped the streams and the preferred landcover feature classes in order to locate the streams that were located within the preferred landcover area. Then I did a 500 meter buffer on the streams feature class and I made sure to dissolve after the buffer so that any internal boundaries were omitted. Next, I figured out the best areas for bear habitat in the management lands of the Michigan DNR. I found the best areas for bears by clipping the study area and the DNR management feature classes. Then I created a union between the DNR management and preferred land cover feature classes and dissolved to clear away any internal boundaries. The DNR did not want the bear habitat to be within 5 kilometers of urban or built up land. To make sure the bear habitat was away from urban or built up land I created a union between the landcover feature class and the all habitat in the study area feature class. Then I did a query to determine where the urban or built up land was located and created a 5 kilometer buffer for those areas of land.

Results

The Bear Habitat in Marquette County map indicates that the optimal place for bears to live is near the streams and near the evergreen forest, the forested wetlands, and the mixed forest land. The Michigan DNR Land map indicates that there are areas within the DNR Management lands that will be ideal for bears to live.

Figure 1. Bear Habitat in Marquette County, Michigan

 

 

 

 

 

 

Sources: Michigan Center for Geographic Information http://michigan.gov/cgi/0,1607,7-158-14652-30811--,00.html

-Landcover is from USGS NLCD http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

-DNR management units http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

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

 

Figure 2. Data Flow Model

 

 

 

 

 

 

Lab 2 Downloading GIS Data

Introduction

My goals for lab 2 were to learn how to download census information and make a map using data from the U.S. Census Bureau website.

Methods

For this lab I created two different maps. For the first map I wanted to map only the total population census data for the state of Wisconsin. To make the total population map I downloaded the 2010 total population census data and the shape file of the 2010 Census boundaries for Wisconsin from the U.S. Census Bureau website. Once I downloaded the data I extracted all the files and opened the CSV files to view the metadata and the tabular data. Then I saved the tabular data as a Microsoft Excel file to use in my map. Next, I added the shape file and the population data to a new ArcMap document. Then I joined the attribute tables for the shape file and the population data by using the GEO#id field which was common in both of the tables. Once I joined the attribute tables I was ready to map the total population data for Wisconsin. I made a graduated color map of the population data by selecting a color scheme and the number of classes to classify my data. The second map I created represents the percent of the total population of Wisconsin who are 65 years of age and over by county. To make the second map I created a new data frame and added the shape file for Wisconsin and the 2010 census data that I downloaded from the U.S. Census Bureau which contained the information for the number of people who are 65 years of age and over in Wisconsin. I made sure to normalize the number of people who were 65 years of age and over by the total population for Wisconsin. After I normalized my data I joined the attribute tables for the shape file and the data for the individuals 65 years and over. I joined the tables using the GEO#id field because it was common in both tables. Then I mapped the age data using graduated colors and chose a color scheme. In order to complete the two individual maps I changed the projection for each to NAD 1983 Wisconsin TM so the state of Wisconsin was represented well. For each map I included essential map elements such as a title, legend, north arrow, scale bar, and source and date information.

Results

The total population map of Wisconsin indicates that the mid-section and south eastern area of the state is where the majority of people live. The northern portion of the state is where fewer people live. The map of the percent of Wisconsin’s population that is 65 years and over indicates that people who are 65 years and over live predominantly in the northern counties of Wisconsin. Those 65 and older also tend to live in the center region of the state and in Door County. The reason why I think the majority of people that are 65 and older live up north is because many of these individuals may choose to retire in this area. The retirees may enjoy the fishing and boating opportunities as well as living in the peaceful natural environment of the northwoods away from busy city life.  

Figure 1. Two Maps of Wisconsin Population Data

Sources

Census data- U.S. Census Bureau
Basemap- ESRI

 

Lab 3- Introduction to GPS

Goal and Background

My goals for this lab were to learn how to create a geodatabase in ArcCatalog, use a Trimble Juno GPS to collect spatial data of the new UW-Eau Claire campus mall, and to create a map that includes the features I collected with the GPS.

Methods

The first step I took for Lab 3 was to make a new geodatabase in ArcCatalog and add point, line, and polygon feature classes to the database. Next, I imported a shape file of the buildings on campus and a raster of the UW-Eau Claire campus to the geodatabase. Then I used the extension called the ArcPad Data Manager in ArcGIS to set up my data to be used on the Trimble Juno GPS by choosing the option to check out my geodatabase layers. Next, I deployed my geodatabase folder to the Trimble Juno so I could go to the campus mall and collect my data. The next step I took was to go to the campus mall and collect point, line, and polygon features. I made sure that my GPS was activated so that I could get satellite signals. I collected six grassy areas using the point averaging and streaming data collection techniques. In addition, I collected the location of one line feature (the footbridge) and six point features which included trees and light poles located on the campus mall. I made sure to enter attribute information for each feature I collected. For example, I entered whether a specific point was a light pole or a tree. After I collected all of my features on the GPS I checked my data back into ArcMap by using the ArcPad Data Manager tool. As I checked in my data I made sure to select the point, line, and polygon features I collected to add to my map. Now I was ready to start creating my map. I chose appropriate symbols to represent each feature on the map. I made the symbol for the campus buildings a light color to de-emphasize them because the buildings are not the focus of the map. To finish my map I included the main components of a map such as a title, legend, north arrow, scale bar, source information, the date the data was collected, and the date the map was created.

Results

The Old Davies Student Center was recently demolished and a new campus mall with walkways and green spaces was constructed that took its place. The campus imagery in the map has not been updated and still includes the location of the Old Davies Center. In the map the new green spaces overlap the Old Davies Center building. In the map I located new trees and light poles that were placed in the new campus mall area. I also included the footbridge on campus as a line feature.

Figure 1. Map of the Physical Features on the UW-Eau Claire campus

Sources: GPS data- collected by Brianna Joslin-Zirngible on 10-16-13  

              Aerial photo- National Air Photography Program

Lab 1 Technical Report

Goal and Background

The construction of a new complex called the Confluence Project is being planned by UW-Eau Claire, the Eau Claire Regional Arts Center, and Haymarket developers that will include a community arts center, housing for university students, and shopping space. My goal for Lab 1 was to learn about spatial data sets that are used for public land management, administration, and land use for the city and county of Eau Claire. In addition, I wanted to gain the ability to create a map to represent the Confluence Project.

Methods

For this project I created a map of the Confluence Project that includes a civil divisions, census boundaries, PLSS, city of Eau Claire parcel, zoning, and voting district data frames. For each data frame I first added the necessary feature classes that I wanted to include in the frame as well as a basemap. For each data frame when it was appropriate I changed the transparency of the layers so that the basemap was visible. I added a scale bar to each map and when applicable I included a legend. I first made the civil divisions data frame by adding the county boundary of Eau Claire. The next data frame I created was for the census boundaries. In the census boundaries data frame I included the block groups and tracts feature classes. In the census boundaries data frame I chose to display the population per square mile. Next, I created a data frame with PLSS data for the city of Eau Claire by adding the PLSS data from the city and county of Eau Claire’s geodatabases. Next, I made a data frame for the city of Eau Claire’s parcel data by including parcel area, centerlines, and water features. Then I created a data frame for zoning information by adding the zoning areas feature class and labeled the features according to the zoning code. Lastly, I made a data frame for voting districts and labeled the features according to the ward number.

Results (See Figure 1)

The civil divisions data frame indicates that the proposed site for the Confluence Project is located in the city. Based on the frame the surrounding area of the city of Eau Claire consists of towns and villages. The census boundaries data frame shows that the Confluence Project is situated in an area of the city where the population is between 4000 and 5000 people per square mile. The census boundaries data frame indicates that as you move farther away from the city the population decreases. The city of Eau Claire Parcel data frame indicates the location of the proposed site in relation to other parcels in Eau Claire. The zoning data frame shows that the Confluence Project is situated near the public properties district and central business district. A pattern that is relevant in the zoning data frame is that in the residential areas there tends not to be land designated for the central business district and industrial. Another pattern is that much of the land designated as the public properties district is located next to water and the Chippewa and Eau Claire rivers. The voting districts data frame indicates that the Confluence Project is located in voting district 31.

 

 

 

Figure 1

City of Eau Claire's Property and Assessment Search Website= http://www.bis-net.net/cityofeauclaire/search.cfm