Lab 4: Final Project

Introduction

For my final project, I wanted to find the best locations to put new campgrounds in Forest County, Wisconsin. My objectives were to use at least four different tools I learned throughout my GIS course and several different limitations on my data sets to narrow down land areas that would make suitable campgrounds. Forest County, the Chequamegon-Nicolet National Forest, and anyone looking to open a campground would benefit from the data I have produced.

Research Question: Where are the best locations to put new campgrounds in Forest County, WI?

Data Sources

To answer this question, I needed to acquire a number of different data sets. I started by connecting to the Wisconsin DNR geodatabase and adding the National Forest, county boundaries, major roads, and water bodies feature classes. I created a new shapefile for the campground locations and used the data from the Chequamegon-Nicolet National Forest webpage regarding camping - each individual campground had GPS coordinates that I plugged into ArcMap and placed a point in that location.

Data concerns: I searched for general campgrounds, so I may have missed some in my search. The webpage provides only public campgrounds in the National Forest (of which I logged the ones inside Forest County), not private grounds. With that in mind, there may be insufficient data regarding locations of all campgrounds throughout the county.

Methods

I needed to query, clip, and buffer my feature classes in order to locate appropriate locations for new campgrounds. I began by clipping the National Forest, major roads, and the water bodies feature classes so I had only the data for Forest County (within the county boundaries shapefile). I then queried the water bodies' type and created a new shapefile containing only lakes. After, I buffered the lakes, campgrounds, and major roads feature classes to one, three, and five kilometers respectively (Figure 1). I intersected the National Forest, major roads buffer, and lakes buffer feature classes to produce a new shapefile with locations that were inside the Chequamegon-Nicolet National Forest, within one kilometer of a lake, and five kilometers of a major road. I then erased the three kilometer buffer of existing campgrounds from my new shapefile and was left with the potential areas for new campgrounds.

Figure 1. Map showing each of the different buffers, the existing campgrounds, lakes, major roads, and the National Forest within Forest County, Wisconsin.

Below shows my data flow model in detail (Figure 2).

Figure 2. Data flow model for this project.

Results

Figure 3 below provides the map I created that shows the locations for potential campgrounds, which are represented by the green areas. I would recommend opening a new location in the south central area of the state; it looks like there's a lake just south of the major road that runs horizontally along the southwestern portion of the state that's almost entirely surrounded by National Forest and is far enough away from existing campgrounds that it would be an adequate competitor in the region.

Figure 3. Final map portraying areas for potential new campgrounds in Forest County, Wisconsin.

Evaluation

I enjoyed this project - having the freedom to choose my own spatial question and needing to complete every step on my own without detailed instruction was more intriguing than essentially being told exactly what to do. If I were to repeat the project, I would be sure to include data for any existing private campgrounds. I would also search for data pertaining to outdoor activity rental services that may be useful to campers, or I'd consider locations that are on a lake big enough and with enough traffic to support a rental service, then recommend opening one along with (or inside of) the campground. That being said, using population or tourist data could also be beneficial.

The biggest challenge for this project was finding and ultimately entering in data for existing campgrounds. The feature class that I used for water bodies was also slightly confusing - the classification system wasn't entirely clear at first, so it took some analyzing and critical thinking to understand what the fields in the attribute table were referring to.

Lab 3: Vector Analysis with ArcGIS

Goals

The purpose of this lab was to apply the knowledge and skills learned in previous weeks about spatial relationships and manipulating data using spatial and non-spatial tools to locate suitable habitats for bears in central Marquette County, Michigan. Learning how to create data flow models and use python code were also important parts of this lab.

Methods

Objective 1: Map a GPS MS Excel file of black bear locations in the study area. To complete this objective, I imported the bear locations data using the "Add XY Data" feature in ArcMap (Figure 1) and selected the same coordinate system as the other spatial data. I then exported the data into my geodatabase so I could bring it into the map as a feature class.

Figure 1. Add XY Data in ArcMap.

Objective 2: Determine the forest types where black bears are found in the study area based on GPS locations of black bears. I spatially joined the bearlocations and landcover feature classes to produce the bear_cover feature class containing information on the type of land the bears were located on. I then used this new feature class to summarize the minor_type field and created the bear_coversum table containing the number of bears located on each type of land cover (Figure 2).

Figure 2. Table showing the top three types of land cover bears were located on.

Objective 3: Determine if bears are found near streams. First, I created a 500 meter buffer around the streams feature class to produce streambuff. I then used select by location to determine which bears were found within the buffer (Figure 3).

Figure 3. Map of the study area showing a 500 m buffer around each stream and the bears within the buffer (highlighted points).

Objective 4: Find suitable bear habitat based on suitable land cover and distance from a stream. First, I queried landcover for suitable habitats (from objective 2), which were mixed forest land, forested wetlands, and evergreen forest land and made a new layer called suitable_cover from that selection. I intersected streambuff and suitable_cover and dissolved the interior boundaries to form bearhabitat_diss, which displayed the suitable habitat locations for the bears.

Objective 5: Find all areas of suitable bear habitat within areas managed by the Michigan DNR. First, I needed to clip the dnr_mgmt feature class so I only had the data within the study area. I then intersected the new feature class dnrstudyarea with bearhabitat_diss and dissolved the interior boundaries to produce the dnrbearhab_diss showing the bear habitats within the DNR's management areas.

Objective 6: Eliminate areas near urban or built up lands. I queried the landcover's major_type field for urban or built up lands and created a new layer from that selection and created a 5 kilometer buffer, producing urbanbuff (Figure 4). I erased the dnrbearhab_diss areas located within the buffer and produced bear_mgmtareas, which showcased bear habitats that were within the DNR's management areas and at least 5 km away from urban or built up lands.

Figure 4. Map of the study area showing a 5 km buffer around the built up and urban land covers.

Results

Objective 7: Generate a digital data flow model of the workflow and cartographic output. Figure 5 below presents the final map results showcasing the bear locations and suitable bear habitats both within and outside of the DNR's areas of management. Included is a map of the state of Michigan to indicate where Marquette County and the study area are located. My data flow model is also included within this figure.

Figure 5. Bear Habitat in Marquette County, Michigan.

Objective 8: Use some python. See Figure 6 below.

Figure 6. Python code used to complete objective 8.

Sources

ESRI

State of Michigan Open GIS Data.

Landcover: USGS NLCD.

DNR Management Units: Michigan State DNR.

Streams: Marquette County, Michigan GIS Data.

Lab 2: Downloading GIS Data

Goals

The purpose of this lab was to learn how to download data from an online source, specifically the U.S. Census Bureau, create two different maps for the data, and learn how to publish a web map to my ArcGIS Online account. To reach this goal, I utilized many tools I had previously learned and simultaneously learn new ones. These tools and skills included downloading data from the U.S. Census Bureau website, joining data, creating aesthetically pleasing layouts, and uploading the web map.

Methods

I began by obtaining the data from the U.S. Census Bureau's webpage. This allowed me the opportunity to learn how to search for appropriate data on a government website, which will be beneficial in the future. We were given specific instructions with what to look for and download: the table data of 2010 census total population for each county in the state of Wisconsin, the shapefile consisting of the counties, and then another set of table data for a variable of our choice (Figure 1). I chose to map the counties by their percentage of rural land, so I downloaded the “Urban and Rural” data set. I opened Microsoft Excel and saved the CSV file that came from the website to an excel workbook so I would be able to successfully open the data in ArcMap.

Figure 1. U.S. Census Bureau webpage containing the search categories (upper left) and the display of the highlighted areas of the shapefile I downloaded.

I performed a table join with the data from the counties shapefile and the total population data set in order to map the data sets I downloaded (Figure 2 below). Next, I added a new field in the attribute table and used query to copy the data into the newly added field column (Figure 3 below). Then, I mapped the data in the new column in a graduated color display with a Natural Breaks classification containing six classes. I went through the same process in a separate data frame to map the "Urban and Rural data set. Once the mapping processes were complete, I set the data frames up in the layout screen and added pertinent information to each, such as a north arrow, scale bar, legend, title, and source (Figure 7 below).

Figure 2. Join Data screenshot joining counties shapefile and data set table downloaded from the U.S. Census Bureau by their shared fields GEO ID.

Figure 3. Field Calculator copying data from one column to the newly added field column so it's available to map.

Finally, I went through the process of publishing my web map to my ArcGIS Online account. I only wanted to publish the information on the total population by county, so I removed the “Urban and Rural” data frame. To publish, I began by signing into ArcGIS online through ArcMap and sharing my document as a service with the UW-Eau Claire Geography and Anthropology hosted service. Next, I changed the capabilities from "Tiled Mapping" to "Feature Access" and checked the UW-Eau Claire box in the sharing tab. Then, I added some additional information to my web map’s description, such as a summary, description of the map, and tags, then clicked "Analyze" to make sure there wouldn't be any issues when attempting to publish the map as a service. Finally, I clicked "publish" and opened ArcGIS Online in my web browser, added my data as a layer to a map (Figure 4 below), edited the information to match what I had put in ArcMap, and configured the attributes to display the county names and populations (Figure 5 and Figure 6 below). My web map, after finalizing the publish, was then available in My Content of my ArcGIS Online account.

Figure 4. In ArcGIS Online webpage, adding the newly uploaded layer to the web map.

Figure 5. In ArcGIS Online webpage, finding where to configure attributes once added to the map.

Figure 6. In ArcGIS Online webpage, specific configuration of the attributes for the layer.

Results

Figure 7 below showcases the final map results. The total population data frame displays Milwaukee, Waukesha, Dane and other south/central eastern counties contain the highest populations in the state, which makes sense due to the fact some of the largest cities in the state lie within these counties. Counties in central and north central Wisconsin seem to have the lowest populations, likely due to large forests, poor soils for agriculture other than tobacco, and no large cities. The rural percentage map shows central and north central Wisconsin counties having the highest percent, which correlates with their low total population. South eastern and central eastern counties seem to have the lowest percentage of rural land, which also correlates with their higher total populations.

Figure 7. Left: Map displaying total population of Wisconsin counties. Right: Map displaying rural percentage of Wisconsin counties.

Sources

U.S. Census Bureau 2010.

Lab 1: Base Data

Goals and Background

The purpose of this lab exercise was to showcase the skills learned in previous week of the GIS I class, specifically with various spatial data sets and manipulating them to display the information appropriate to the exercise. The lab instructed me, a hypothetical intern at Clear Vision Eau Claire, to use such spatial data sets used in public land management, administration, and land use to prepare a basic report and develop base maps for the Confluence Project. The Confluence Project is a plan for the development of a community arts center, a mixed-use space for retail, commercial, parking, and university housing apartment complex. The space for development is located at the confluence of the Eau Claire and Chippewa Rivers.

Methods

Constructing the report and base maps took several different steps. First and foremost, I familiarized myself with the feature data sets given and reviewed basic skills I thought I would need for this project. Then, I needed to digitize the Confluence Project's proposed site. Finally, I built several data frames to showcase the information I gathered from the geodatabases and created this report.

Familiarizing myself with the spatial data sets and information consisted of going through both geodatabases within ArcCatalog and previewing all feature data sets and classes. The geodatabases contained spatial data pertaining to the census features, development, parcel features, PLSS, and the roads within the city and county of Eau Claire. I also reviewed certain skills for this lab such as how to digitize, manipulate the layout frames, and add pertinent information like legends and scale bars.

Next, I digitized the site for the Confluence Project. This process began with creating a new, blank feature class named Proposed Site in the geodatabase I created specifically for this exercise. In ArcMap, I added the Imagery basemap, the parcel area feature class, and the Proposed Site to a blank map, zoomed to the location of the Confluence project, and turned on the editor toolbar. I used the editor toolbar to create a new feature in the Proposed Site feature class outlining the location of the Confluence Project and made it a bright blue color so it would easily be visible next to any other feature classes added to a given map. This gave me my digitized site.

Finally, I built six data frames with information pertinent to the exercise. Each data frame displays the location of the Confluence Project within different maps of the local area and what is important legally and spatially. I used different skills developed in previous weeks, such as manipulating data frames in the layout view of ArcMap, to make the maps look presentable and aesthetically pleasing.

Results

Figure 1 below presents the data frames created to present the information on the Confluence Project. The first data frame displays the civil divisions, separating the city of Eau Claire from the towns surrounding it and Chippewa county to the north. The second frame shows the census boundaries with each section represented by their populations and normalized by square mile, ultimately showing the population density of the surrounding area. The third frame presents the location of the proposed site in terms of the PLSS quarter quarter divisions; it's legally described as residing in Section 20, Township 27 North, Range 9 West. The fourth frame shows the city of Eau Claire's parcel data and the proposed site location is highlighted. The fifth frame displays the zoning data in Eau Claire; each zoning class is highlighted in a different color with the proposed site located in a red zone, which represents a central business district. The final data frame includes the voting districts for the city of Eau Claire and the location of the proposed site within district 31.

Fig 1. Base maps displaying varying information. 

Sources

City of Eau Claire and Eau Claire County 2013.