Lab 4: Mini-Final Project

Nathan Sylte
Geog-335
Lab 4

The Invasive Species Threat to Pine Lake

Introduction:

Pine Lake is a pristine drainage lake located in northern Chippewa County that has been subject to many research projects dating back to the early 20th century. The water quality of the lake is not surpassed by many lakes in the entire state of Wisconsin. Public access to the lake is present but limited, and the lake is moderately developed with a forested surrounding it. Currently, Pine Lake does not possess any threatening invasive species which is uncommon compared to many of the lakes in Wisconsin. It is extremely important that Pine Lake be preserved for future generations to admire and enjoy.

This project involved the follow up of the Pine Lake project that took place during the summer of 2015. The overall objectives of the Pine Lake project were to research the various species of aquatic mosses inhabiting the lake, obtain information regarding invasive species in the surrounding area, and to conduct a bluegill nest survey of the entire lake to be used in future research. The Pine Lake project is important in ensuring the preservation of Pine Lake.

So what lakes in the surrounding area containing harmful invasive species pose a threat to contaminating Pine Lake? To answer this question lakes had to meet certain criteria. This criteria included proximity to Pine Lake, proximity to a DNR access boat landing, relative human activity on the lake, and presence of certain invasive species. The identification of these "hazard lakes" is extremely important for the preservation of Pine Lake. Identifying which lakes are hazardous to Pine Lake could be very useful for the Wisconsin DNR's efforts to preserve our natural resources.

Data Sources:

Data used in this project were obtained through the Wisconsin DNR's geodatabase. This includes county, major road, and lake data. Additional DNR boat landing data was accessed on the DNR's website. Invasive species data was compiled during the summer of 2015 Pine Lake project (unpublished data) and contains information about many surrounding lakes. Some of the information obtained during the Pine Lake project about surrounding lakes includes the number of docks on each lake, as well as what invasive species the lake possesses.

Methods:

In order to classify hazard lakes, those lakes had to meet certain criteria. Hazard lakes were identified in the following manner. First, lakes within the target counties (counties around Pine Lake) were clipped. This clip was then joined to the invasive species data collected during the Pine Lake project to attach invasive species data to lakes within the target counties.

Lakes were then selected that contained invasive species and had high amounts of human activity. Several common invasive species were chosen for their abundance in the region as well as their prolific behavior. The invasive species chosen include Rusty crayfish, Eurasian milfoil/milfoil hybrid, and Curly leaf pondweed. Lakes that had high amounts of human activity were determined based off of how many docks they possessed. Lakes possessing 100 or more docks were classified as being high in human activity. The lakes clip/invasive join was then subjected to a query to select for lakes that met the previously described criteria. Lakes that met the criteria were then considered hazardous. However, not all of the lakes selected were within a close enough proximity to Pine Lake.

Lakes that were selected as being hazardous were then subjected to several selections by location. The lakes that were to be identified as hazardous to Pine Lake had to be within ten miles of a DNR access boat landing. They also had to be within 20 miles of Pine Lake. Lakes that met the proximity criteria were then labeled "hazard lakes". A data flow model was also created to display the process of how the hazard lakes were selected (Figure 1). Finally, a graduated colors map was generated based off of how many of the previously listed invasive species the hazard lakes possessed. This map was used to determine which lakes posed the biggest threats to Pine Lake.

Figure 1. Shown above is the data flow model generated from the identification process of lakes hazardous to Pine Lake.

Results:

The graduated colors map generated (Figure 2) yielded which lakes where most hazardous to Pine Lake. The chain of lakes located in Chetek, WI which included Prairie, Pokegama, and Chetek Lakes where all identified as hazard lakes. Certain lakes that lie very close to Pine Lake were also selected. These lakes included Potato, McCann, Chain, and Long Lakes. These lakes are located only several miles from Pine Lake and should be a cause for concern. The Chippewa River system located to the east of Pine Lake was also selected as hazardous. The Chippewa River system possessed all of the invasive species previously listed and creates a hazard for all lakes in the surrounding area.

Figure 2. Shows the graduated colors map generated which identifies lakes that are hazardous to Pine Lake. Lakes containing all invasive species appear as dark red while lakes containing zero of the listed invasive species appear as light tan.  

Evaluation:

Overall the project represented more of a pilot study to potentially be used in the future. A Crayfish research project that is set to take place this summer will potentially use some of the data generated during this project. If asked to repeat the project, several things would have been done differently. First, the amount of invasive species included would be greater. Only four invasive species were used which do not represent the entirety of the invasive species problem. Time was a limiting factor; more time would have allowed for the inclusion of more invasive species. Second, the area of the project would have been increased to a state-wide area, instead of just looking at hazardous lakes within 20 miles of Pine Lake. This wider gaze would be more insightful in identifying areas that contain many invasive species. This project also posed several challenges. The main challenge was organizing the data from the Pine Lake project to be used in ArcMap. The data was not collected for use in ArcMap, though it should have been. Organizing the Pine Lake project data took some time. Another challenge was determining which tools to use. The nature of the project did not involve the use of many tools; however, it did involve other aspects such as several lengthy query statements. Overall, the project proved to be very insightful and practical.

Sources:

Wisconsin Department of Natural Resources. (2016). Retrieved from ftp://ftp.wi.gov/DNR/public/Lands/

Wisconsin Department of Natural Resources. (2016). Wisconsin DNR: Base Data [Data file and documentation].



Lab 3: Vector Analysis with ArcGIS

Nathan Sylte

GIS 335

Mapping Black Bear Habitat in Marquette County, Michigan

Introduction:

The American Black Bear (Ursus americanus) is a common North American big game animal and is a favorite for many hunters as well as nature enthusiasts. The purpose of this project was to establish areas suitable for black bear habitat to create management areas for the Michigan Department of Natural Resources (DNR). This was done by using multiple geoprocessing tools for vector analysis to analyze which areas were favorable for black bears. 

For this project I took on the roll of an employee for the Michigan DNR that was in charge of mapping suitable black bear habitats in Marquette County, Michigan.

To complete the project our objectives were:

Objective 1. Generate a map of bear locations within the pre-determined study area using a GPS MS Excel file of known black bear locations.

Objective 2. Use the GPS locations of black bears to decide which forest types they prefer to inhabit.

Objective 3. Decide whether black bears are found in proximity to streams.

Objective 4. Determine preferable black bear habitat using their preferred forest type and proximity to streams as criteria. 

Objective 5. Locate black bear habitat that falls within land already managed by the DNR. 

Objective 6. Remove proposed habitat areas that are near urban or developed lands.

Objective 7. Create a data flow model of the how the results were obtained. 

Objective 8. Generate some python script based off of the work flow. 

Methods:

All data was downloaded from the State of Michigan Open GIS Data Michigan Data. Land-cover data was from USGS NLCD Land-Cover. 

To complete objective one, the known bear locations with (x,y) coordinates had to be added to ArcMap as an event theme. This was done so the bear locations could be displayed. In objective two, black bear habitat was determined by using a spatial join to join land-cover with the bear locations. The bear/land-cover join was then summarized to determine which three forest types black bears preferred. It was then determined in objective three that bears were commonly located near streams (results). This result was determined by generating a 500 meter buffer around all streams. After the buffer was created the streams buffer was intersected with the bear/land-cover join, and we were able to see how the amount of bears associated with streams. 

Since it was determined that bears preferred mixed forest, forested wetlands, evergreen forests, and were often associated within 500 meters or streams, objective four was completed by using a query of the three preferred forest types. Then preferred forest types were intersected with the streams buffer to generate suitable black bear habitat areas. 

To resolve objective five, DNR lands was dissolved into one, then intersected with suitable black bear habitat to determine management areas.

Management areas located within five miles of urban or developed areas were then removed using the erase tool to complete objective six. 

A data flow model was then generated to display how the map was generated (Figure 1), and python script was created from some of the operations that were performed (Figure 2). 

Results:

Figure 1. Shows the data flow model used to create the bear habitat map, and identify management areas. 

Figure 2. Python script showing some of the operations used in lab 3 including buffer, intersect, and erase. 

Figure 3. Black bear habitat map showing bear locations, preferred black bear habitat, management areas, streams, DNR lands, urban areas, and the study area. 

Results from objective three: Out of the 68 total black bear locations, 49 of them were located within 500 meter of a stream (72%). 

Sources:

http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

    DNR management unites

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

    Streams

http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

    USGS NLCD

http://gis.michigan.opendata.arcgis.com/

    State of Michigan Open GIS Data

    

Lab 2 Investigating Population Characteristics of Wisconsin

Nathan Sylte
GIS 335

Investigating Population Characteristics of Wisconsin 

Introduction:

Political campaigns that are successful possess an understanding of important regional population characteristics. This understanding is critical for efficient and strategic campaigning. Current election cycles show the importance of strategic campaigning. Time and money are often limited, so candidates must isolate key areas to spend time in.  

The background for this project involved taking on the role of someone working for a candidate running for president during the presidential primaries. To maximize voter turnout, this candidate is targeting young males specifically in the 21-year old age group. Males of this age are important constituents of this candidate so maximizing their vote is crucial for success.  

Some of the goals for this project included downloading census data from an online source, downloading a shape file from the census data, joining tables from both sources, and mapping the census data. 

Methods: 

The first step involved downloading 2010 census data from the Wisconsin Census Department. This involved going online to Fact Finder Census Data. An advanced search was required to obtain the desired data. The advanced search involved choosing under Topics,  People, Basic Count/Estimate and then population total. The area was selected by choosing Geography, Counties, County 050, and then Wisconsin. The data was then selected from the 2010 SF1 Dataset.

The next step involved downloading a shape file from the Wisconsin Census Data. This involved selecting Geographies, Options, Map Tables, and then downloading the file as a shapefile.zip. The shape file then had to be joined to the 2010 SF1 Dataset.

Sex and age population data was then downloaded from the same census data website. This was done by keeping the geographic area the same but choosing Topics, People, Sex and Age, and then Age in that order. A new layer to the map was then added including this data and the same shape file previously downloaded. The table from the age data was then joined to same shape file (shown by the figure to the right).

Finally, a map was created with two layers. One of the layers showed the population totals, and the other showed the proportion of the the population of males age 21. The proportion of the population of males age 21 was shown by creating a graduated colors map (as shown below). Some important feature classes that were included were major cities, major roads, and congressional districts. Counties, major cities, and congressional districts were labeled.

 Results:

The results of the project are represented by figure 1 shown below.

Figure 1. Two maps showing population density and percent population of males age 21. Major roads, major cities, and congressional districts are also represented in the maps.

References:

American FactFinder - Search. (n.d.). Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

Lab 1: Generating base maps for the Confluence Project

Nathan Sylte
GIS 335

The Confluence Project



Introduction:


The Confluence Project is a proposed development that will include a new community arts center, university housing, and a commercial retail complex (Confluence Project Area) . Downtown Eau Claire at the confluence of the Chippewa and Eau Claire Rivers is the location where the new arts center is set to be built. The arts center will include classrooms, studios, offices, galleries, and several performance spaces (About Confluence Project ). Overall, the Confluence Project is an exciting proposition for the University of Eau Claire as well as the City of Eau Claire.

The background for this project involved taking on the role of an intern at Clear Vision Eau Claire. Clear Vision Eau Claire is a company that is collaborating with the Confluence Project and the City of Eau Claire to use geographic information systems to construct the Confluence Project.

There were several goals for this project. One was to gain familiarity with multiple spatial data sets that are used in public land management, land use, and administration. Another important objective was to generate base maps for the Confluence Project. Overall this project will lead to a better understanding of geographic information systems.

Methods:

To start the project, I previewed the databases that were to be used for the project. Once I was familiar with the databases, I focused on the location of the Confluence Project site. This was done by using parcel areas and digitizing the areas that are to become the sites of the project. The digitized area was then used as a feature class called proposed site and was used in the maps.

After the sites were digitized, I learned more about the public land survey system. This was done to obtain a better overall understanding and to generate a better map. The PLSS Quarter Quarter feature class was then added to the map for later use.

Next, a legal description was generated for the parcel sites of the Confluence Project. The legal area was viewed on Eau Claire's property and assessment search website (Mapping Services ).

The final objective was to create a map representing important base data crucial for the Confluence Project. A total of six maps were generated. Represented in these maps are civil divisions, census boundaries, PLSS, parcels, zoning information, and voting districts. The centerlines and water feature classes were also added to several of the maps. An aerial imagery base map was added to all of the six maps, and the features were made transparent to view the base map.

Results:

The results of the project are represented by the six maps below displayed as Figure 1.

Figure 1. Six maps displaying the necessary information needed for the Confluence Project. Civil Divisions, Census Boundaries, PLSS Features, EC City Parcel Data, Zoning, and Voting Districts are represented in the six maps.




References:

http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services

City of Eau Claire, Wisconsin : Mapping Services. (2016). Retrieved from http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services

http://www.eauclairearts.com/confluence/

"Eau Claire Confluence Project | Community Involvment Collaboration." Eau Claire Confluence Project | Community Involvment Collaboration. Eau Claire Regional Arts Center, n.d. Web. 28 Sept. 2014.

http://www.uwec.edu/News/more/confluenceprojectFAQs.htm

"Frequently Asked Questions: The Confluence Project." Frequently Asked Questions: The Confluence Project. University of Wisconsin Eau Claire, 22 July 2014. Web. 28 Sept. 2014.