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.