GIS I Lab 4: Final Project

The Ideal Location for a Campground

Introduction:

For my final project in Geography 335, my research question was, “Where would the best place be for a new campground in Oneida County, Wisconsin?” One objective of my project was to identify an area or possible areas that would be suitable to create a new campground in northern Wisconsin that took advantage of the natural resources and scenery. The other objective was to craft a data flow model that I could create and complete on my own; demonstrating that I have mastered the concepts I have learned throughout the course.

My intended audience is for investors who see a potential in environmental tourism. Wisconsin is blessed with a large amount of green space that is still relatively remote in this world of constant urbanization. The remoteness of Oneida County offers something would be enticing to campers who wish to get away from the hustle and bustle of everyday city life. Being located somewhat close to Green Bay give the county a desirable location as a weekend summer getaway.

Data Sources:

For the ideal area for campground to be located, a moderate amount of data sets would be needed. Data taken from the Wisconsin DNR included the county forests in the state of Wisconsin as well as all lakes in the state. The major roads data set was also taken for reference. This data set would not be taken into account for the actual analysis. ESRI provided data for the cities and county borders.

To access the actual data, I had to connect to the UW-Eau Claire data servers. There I was able to pick and choose all that data from a huge database that contained a wealth of information. Taking data straight from the Wisconsin DNR and ESRI insured the most accurate information compared to finding data online from a website whose credentials may not be able to be verified.

When running analysis on GIS, there should always be data concerns. I had multiple concerns over some of the data I had collected. One was the county forest borders. I was unaware if there were any logging operations that have occurred in boundaries or near the boundaries that could have disturbed the natural tranquility I was searching for. Another concern I had was settlements on lakes. As I desired remoteness for a possible campgrounds, I could not tell if lakes had significant human development on them. This is a problem as a lot of lakes in northern Wisconsin have become pseudo-resort areas with large human footprints. A final concern I had was with the population and size of the cities ESRI provided. Knowing Rhinelander was the only significant town in Oneida County with a human population of 7,557, I was unable to estimate the size and scope of the other unincorporated communities that were spread throughout the rest of the county.

Methods:

Once all the necessary data had been acquired, certain tasks had to be performed so analysis could be run. The first step required me to locate and isolate Oneida County from the rest of United States’ counties that ESRI provided. Once selected, a new layer was exported from the selection of Oneida County. To prevent analysis from being run on data that included the rest of United States and Wisconsin, a series of clip tools had to be run on the cities, county forest, major roads, and lakes data files. These new clips contained only the necessary data that fell within the boundaries of Oneida County.

The next step would finally allow the data files to be appropriate for analysis. The data downloaded was projected in a Web Mercator projection among other projections. To make sure all the data would be presented in the least amount of distortion, as well as sharing the same projection, the project tool was used. Therefore, all data presented in the final map is presented in the NAD 1983 HARN Wisconsin Transverse Mercator (US Feet).

To select the best possible campground, I came up with three factors that would decide the ideal location. The first was that the campground had to be within a quarter mile of a lake. The second was that it needed to be at least five miles away from an urban settlement. The final stipulation was that the campground had to be at least one mile away from protected county forest. Four tools were used in this process: buffer, dissolve, erase, and intersect.

Lakes, county forest, and cities were all given their own buffers of .25 miles, 1 mile, and 5 miles, respectively. Lakes then had their buffers dissolved so lakes close to one another appeared as one. Cities, along with their five mile buffers, were erased from the map. This final layer was then intersected with the dissolved lake buffers and forest buffer. This process is depicted in the model below.

The following image shows the date flow model used for the assignment. Tools are shown in red. Inputs and outputs are displayed in blue.

Results:

According to my criteria, there only appears to be one area that is suitable for a campground that wishes to be both remote as well as near water and woods. This area falls on the western end of the Willow Flowage. Being situated nicely between a huge tract of county forest as well as a large water body offers a perfect place for that weekend getaway. Not being near any urbanization provides the tranquility that campers clamor for.

Although there were three other large bodies of water in Oneida County, none were near county forest that my criteria specified. Also, some of the lakes fell within the five mile buffer of a local community so they were eliminated by my criteria as well. 

This map shows the final results for this lab assignment once all tools and analysis had been completed.

Evaluation:

I enjoyed this project due to the lack of hand-holding that was typical of MAG assignments we have done prior in the class. Everything from the topic to the data used was up to me. I was in control of the assignment and I had to create my own road map that would answer the question I selected. This fostered a sense of independence that I will now be able to carry with me into projects I may be tasked with in GIS in future course work or the professional world.

Not knowing much about the geographical layout of Oneida County, I expected there to be more lakes and larger tracts of county forest within its borders. However this was far from the case. If I were to redo the assignment, I would do more research into the features of a county to find one that had more waterways or protected forest. These counties would possibly contain more sites than just the one area I identified in Oneida County.

A few challenges I encountered started from the beginning of the assignment. I had a difficult time in coming up with my own specific spatial question. I wrestled with many ideas, but some could not be answered with data that was provided or recommended to use. As the land cover date file was described as not being reliable, I had to find my own substitute for a data file that would show the most amount of untouched forest.

GIS I Lab 3: Vector Analysis with ArcGIS

Suitable Bear Habitat

Goal:

The goal for this particular lab assignment was to familiarize oneself with various geoprocessing tools for vector analysis in ArcGIS. The tools were used to identify habitat for bears in a study area of Marquette County, Michigan that fulfill a certain set of criteria.

Background:

In this simulation, I was tasked by the Department of Natural Resources to find suitable areas for black bears to live. I was given a set of data with bear population, land cover types, and a host of other information. The purpose of this simulation was to apply teachings from the classroom to a real-world scenario. Tools such as buffers, spatial joins, and intersects would be used to determine where bears would most likely thrive in central Marquette County.

Methods:

Multiple tools had to be used to complete the eight objectives. The bear locations provided by the DNR had to be exported into ArcMap and given a coordinate system that was compatible with the rest of the data.

The main skills acquired in this lab were gained from operating multiple tools for vector analysis. A spatial join was used to match bear locations with the type of land cover the animal resided in; this operation created a new feature layer which allowed additional analysis to be ran. To discover which type of land cover was most utilized by bears, the new feature layer was summarized to identify the top three habitats.

By using a spatial query, I was able to see that streams were an important part to bear habitat. A 500 meter buffer was placed around the stream. This new buffer was then combined to the new bear habitat layer using the intersect tool. A dissolve tool was ran to remove lines within the individual polygon to clean up the image. This new feature layer presented all suitable bear habitat.

To find proper DNR management areas that fit within the study area, a clip tool was run. This tool removed the management zones that fell outside the study area. These zones’ internal boundaries were then removed using another dissolve tool. Another intersect tool was used between the new DNR zones and the suitable bear habitat layer to show only DNR zones in ideal bear habitat.

Since the DNR only wanted zones that were far away from urban areas to prevent human-bear conflict, a buffer tool was required. Selecting “Urban Areas” within the land cover feature class allowed me to create a new layer that only contained these urban areas. A five mile buffer was placed around the areas with a presence.

This new buffer was then used in conjunction with the DNR areas in suitable bear habitat to create a layer which showed rural DNR management zones perfect for black bears. An erase tool was used between the two to create the final product.

The entire process was mapped out using a data flow model that I created in Adobe Illustrator. The model shows which tools and outputs were used so a user experienced with GIS could complete the same process I just completed.

The following image displays the data flow model used to determine suitable bear habitat. Tools are depicted in red. Inputs and outputs are displayed in blue. The final feature layer is colored yellow.

Another skill learned from this lab was the ability to code in Python. This allows a user to perform tools straight from a command window rather than going through a tool’s interface. Python possesses a strict syntax so statements had to be wrote out perfectly for the code to work effectively.

This image shows what coding in Python looks like in command window. A buffer, intersect, and erase tool are being performed.

Results:

Once the final set of analysis was completed, a map of suitable bear habitat was created. Although there was an ample amount of suitable bear habitat present, the majority of it was not on land that the DNR had designated as management areas. To add to the list of restrictions, the DNR needed management areas to be at least five miles away from urban areas. Most of these areas were in the southern portion of the study area. As a result, the entire middle swath of the study area running east to west held most of the suitable management areas. A particularly desirable rural DNR management zone is situated in the northeast corner of the study area.

This map is the final product from Lab 3 after all analysis and tools had been completed.

Sources:

State of Michigan. (2015). Open GIS Data [Database]. Retrieved from http://gis.michigan.opendata.arcgis.com/

Michigan Center for Geographic Information. (1992). Michigan 1992 NLCD Shapefile by County [Data file]. Retrieved from http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

Michigan Department of Natural Resources. (2001). wildlife_mgmt_units [Data file]. Retrieved from http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

Center for Shared Solutions and Technology Partnerships. (2014). Michigan Geographic Framework: Marquette County [Data file]. Retrieved from http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

GIS I Lab 2: Downloading GIS Data

Introduction: This lab was meant to familiarize the user with using the U.S. Census Bureau's vast and complex database. The Bureau's website is a huge source of data, but it is critical to find the correct data. Lab 2 helped with focusing in on the correct data set and making sure it had a proper source. Sometimes too much information can be a problem and this lab helped sift through the unnecessary data.

Methods: First, data had to be taken from the American Factfinder website of the U.S. Census Bureau. The data then then had to be extracted and changed into an Excel Workbook file. I then downloaded the Wisconsin County shape file. I then opened both of these files into ArcMap and joined their respective attribute tables together. Afterwards, I exported the joined shape files to a new window of ArcMap so I could transfer the new map to ArcGIS Online.

This whole process took about three hours to complete. I learned two skills that will help me down the road in using GIS: Finding creative ways to get around a problem in ArcMap and how to use the U.S. Census website.

The first skill was obtained when I reached a roadblock in the directions for the lab. When I tried to use the WebMap_lab2.mxd to upload it to the ArcGIS Online site. It kept saying I had base map layers on it even though there were no base map layers on it. I decided to open a new window of ArcMap with my current WebMap_lab2.mxd still open. I just right clicked on the bottom of the desktop to open a new window.  Then I selected the blank template. I then went back to the WebMap_lab2.mxd and copied the layer "Rural_Population". Finally, I pasted it into the blank template and saved it as TryThisWebMap_lab2.mxd.

This allowed me to complete the lab. If I had just simply stopped because of the errors, I would not have been able to finish. Finding different ways to work around a problem will help me in the future.

The second skill I learned was working with the U.S. Census website. I have worked with it in the past, but I have never downloaded data from the site. Knowing that I need to match data with the correct source is something I would never have thought of. As a future geographer, knowing how to work with the complex American Factfinder will be crucial to furthering myself in the field.

Results: The result of the lab resulted with two maps in ArcMap and one on ArcGIS Online. The two maps from ArcMap depict certain demographic information on the state of Wisconsin. The population of the individual counties in the state of Wisconsin is displayed in the first of two maps. The second map shows the population of the individual counties who reside in rural communities as a percent of the total county population.

The patterns on the maps are quite apparent. Most of the population is in the state of Wisconsin is located in the southeast. The southeast is where Milwaukee and Madison both reside, the state's two biggest cities and main economic drivers. This leads the rest of the state population to be spread across the rest of the counties. These counties have more of a rural lifestyle and the northern part of the state shows this quite clearly.

The third map that was created in ArcGIS Online was an interactive map that was based on the second map. It shows the population of the individual counties who reside in rural communities as a percent of the total county population as well. It can be found at the following link:

http://arcg.is/1M1aHb0

Source: 

United States Census Bureau. (2015). American Factfinder [Data file]. Retrieved          from http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

GIS I Lab 1: Base Data

Goals and Background: My first lab assignment for GIS I presented me with a variety of tasks to complete and data to collect. First, I would learn how to navigate the public land systems that the city of Eau Claire uses to manage the city, as well as the county. Then I would compile the data I found into 6 maps focused on the Confluence Project, a public-private works project in the downtown of Eau Claire. All of these goals would be completed using ArcGIS and the city website.
  For me to successfully complete the lab, I had to finish a list of various objectives.

1. Find data from the City and County of Eau Claire that could answer nine questions about the datasets.
2. Locate the Confluence Project in ArcGIS and digitize its location.
3. Become familiar with the Public Land Survey System (PLSS).
4. Craft two legal descriptions and reports for the two parcels of land that would be used for the project.
5. Create six maps that would present the data from the main feature classes I had used previously.

Methods: The lab was a long process that I would separate into two phases. One, was data collection and management. The second phase entailed the map making process.

  Objective 1 had me familiarize myself with the data I would be using. Using ArcCatalog, I answered numerous questions about specific data, such as how many feature classes are in Topology or which fields contain the zoning information.

  Next for Objective 2, I would begin the digitization process of the Confluence Project. I created a blank geodatabase, EC_confluence. Into this blank geodatabase, I placed "pro_site" (Proposed Site). I used the same coordinate system that Eau Claire County used by selecting their "CENSUS_FEATURES" dataset and importing it.

  After getting my data all set to go, I uploaded a blank map and added  "World Imagery" as a base map. With the "parcel_area" added, I found the two sites of 128 Graham Avenue and 202 Eau Claire Street by using the identify tool. Once located, I turned on the editor toolbar. This allowed me to craft a shape over the two parcels using the Polygon tool.

  Objective 3 was the next step. I inserted a new data frame and added the "PLSS_Townships" layers from both the city and county geodatabases on top of the "World Imagery" basemap. After the adding the quarter-quarter sections from both geodatabases, I answered a question about the location of the Confluence Project using the identify tool.

  Using the Parcel ID from the attributes I acquired using the identify tool previously, I uploaded these ID's into the city's property and assessment search engine. I was able to compile data on these two parcels and created a brief report for each one. To finish Objective 4, I created two maps with each parcel highlighted and labeled. A quick note, ArcGIS must have had an outdated Parcel ID for 202 Eau Claire Street, so I used the address "202 Eau Claire Street" to search instead of the Parcel ID.

  Objective 5 had me re-size the layout of ArcGIS, changing the size to 11 X 17 and placing it in a landscape orientation. I then inserted six new data frames, each one containing separate themes. Data had to be added into each frame. I also placed titles, legends, and scale bars on all of them. By changing the transparency on each layer, I was able to create aesthetically pleasing maps that also revealed the imagery beneath.   

Results: The following image contains my six maps, each showing where the Confluence Project is. The first, going from left to right on the top row, features the types of municipalities that are in Eau Claire County.. My second map lists the census boundaries. The census uses tracts, further subdivided into the smaller blocks, to organize spatial data. I then had the Block class symbolize population per square mile. The third map shows the Quarter Quarter sections of the PLSS.

  Starting the second row, the fourth map shows each parcel and the centerline of each road. Lakes and rivers are also symbolized by the Water feature. The fifth map shows the zoning districts in the city of Eau Claire, chiefly those around the Confluence Project. The centerline of roads are also added to this map. The final map shows the numerous voting districts for the city of Eau Claire.

Sources: City of Eau Claire and Eau Claire County 2013

City of Eau Claire, Wisconsin. (2015).

   Mapping services [Data]. Retrieved from

   http://eauclairecitywi.wgxtreme.com/